diff --git a/markDown/README.md b/markDown/README.md new file mode 100644 index 0000000000000..f221d60a5dc32 --- /dev/null +++ b/markDown/README.md @@ -0,0 +1,55 @@ +# DataFrame and Series to Markdown Converter + +## Overview + +This package provides utility functions to convert pandas DataFrames and Series into markdown-formatted tables. These functions are integrated into the pandas library as methods for seamless usage. + +## Installation + +Ensure you have pandas installed: +```bash +pip install pandas +``` + +1. Clone the Repository + +2. Import the Module: In you Python script or Jupyter Notebook, you can import the markDown.py module like so: + +```python +from markDown import markdownTable, markdownSeries +``` +3. Use the Functions: Once imported, you can use the `markdownTable` and `markdownSeries` functions directly, or you can utilize the extended functionality added to Pandas DataFrame and Series. + +For example, to convert a DataFrame to a markdown table: + +```python +import pandas as pd + +# Assuming 'df' is the DataFrame you want to convert +markdown_table = df.markdownTable() +``` +Or, to convert a Series to a markdown table: + +```python +import pandas as pd + +# Assuming 'series' is the Series you want to convert +markdown_table = series.markdownSeries() +``` + +Users can also specify specific columns for DataFrame conversion: + +```python +markdown_table = df.markdownTable('Column1', 'Column2', 'Column3') +``` + +Or customize the column names for Series conversion: + +```python +markdown_table = series.markdownTable(col1='Index', col2='Value') +``` +## Conclusion +These functions provide an easy way to convert pandas DataFrames and Series into markdown-formatted tables, enhancing the readability and presentation of your data in markdown-supported environments. This is simular to using `to_markdown` in pandas without having to install another library. + +pandas, the `to_markdown` method is provided by the `tabulate` library, which needs to be installed separately. The to_markdown method is available in pandas version 1.3.0 and later. + diff --git a/markDown/markDown.py b/markDown/markDown.py new file mode 100644 index 0000000000000..27a24a6b821e4 --- /dev/null +++ b/markDown/markDown.py @@ -0,0 +1,27 @@ +import pandas as pd + + +def markdownTable(df, *column_names): + if not column_names: + column_names = df.columns.tolist() + table_markdown = "| " + " | ".join(column_names) + " |\n" + table_markdown += "| " + " | ".join(["---"] * len(column_names)) + " |\n" + for index, row in df.iterrows(): + row_data = [str(row[col]) for col in column_names] + table_markdown += "| " + " | ".join(row_data) + " |\n" + return table_markdown + + +def markdownSeries(series, col1=None, col2=None): + if not col1: + col1 = series.index.name if series.index.name else "Index" + if not col2: + col2 = series.name if series.name else "Value" + table_markdown = f"| {col1} | {col2} |\n|---|---|\n" + for index, value in series.items(): + table_markdown += f"| {index} | {value} |\n" + return table_markdown + + +pd.DataFrame.markdownTable = markdownTable +pd.Series.markdownTable = markdownSeries diff --git a/markDown/sample_use.py b/markDown/sample_use.py new file mode 100644 index 0000000000000..847775e463de4 --- /dev/null +++ b/markDown/sample_use.py @@ -0,0 +1,25 @@ +import pandas as pd +from markDown import markdownTable, markdownSeries + +# Generic Grade book data +data = { + 'Student': [f'Student {i}' for i in range(1, 21)], + 'Math': [round(50 + i + (i % 3) * 5, 1) for i in range(1, 21)], + 'Science': [round(55 + i + (i % 4) * 3.5, 1) for i in range(1, 21)], + 'English': [round(60 + i + (i % 2) * 2.7, 1) for i in range(1, 21)], + 'History': [round(65 + i + (i % 5) * 4.2, 1) for i in range(1, 21)] +} +df = pd.DataFrame(data) + +# Convert the DataFrame to a markdown table +markdown_table = markdownTable(df) +print(f"Convert the DataFrame to a markdown table\n{markdown_table}") + +# Convert the 'Math' Series to a markdown table +math_series_markdown = markdownSeries(df['Math'], col1='Student', col2='Math Grade') # noqa +print(f"Convert the 'Math' Series to a markdown table\n{math_series_markdown}") + +# Print value counts of 'Student' column in a markdown table format +value_counts_series = df['Student'].value_counts().sort_values(ascending=False) +markdown_table = markdownSeries(value_counts_series, col1='Student', col2='Count') # noqa +print(f"Print value counts of 'Student' column in a markdown table format\n{markdown_table}") # noqa \ No newline at end of file diff --git a/markDown/sample_use2.py b/markDown/sample_use2.py new file mode 100644 index 0000000000000..54096939e7a0b --- /dev/null +++ b/markDown/sample_use2.py @@ -0,0 +1,32 @@ +import pandas as pd +from markDown import markdownTable, markdownSeries + +# Create a hypothetical dataset +data = { + 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'], + 'Age': [25, 30, 35, 40, 45], + 'Gender': ['Female', 'Male', 'Male', 'Male', 'Female'], + 'Salary': [50000, 60000, 70000, 80000, 90000], + 'Department': ['HR', 'Finance', 'IT', 'Marketing', 'Operations'] +} +df = pd.DataFrame(data) + +# Create a new column 'Bonus' based on complex calculation +df['Bonus'] = df['Salary'] * 0.1 + df['Age'] * 0.05 + +# Perform some complex operations on the DataFrame +# For example, let's filter the DataFrame for individuals with Age > 30 and Salary > 60000 +filtered_df = df[(df['Age'] > 30) & (df['Salary'] > 60000)] + +# Convert the filtered DataFrame to a markdown table +filtered_markdown_table = markdownTable(filtered_df) +print("Filtered DataFrame as Markdown Table:") +print(filtered_markdown_table) + +# Calculate the average Bonus for each gender +avg_bonus_by_gender = df.groupby('Gender')['Bonus'].mean() + +# Convert the Series to a markdown table +avg_bonus_markdown = markdownSeries(avg_bonus_by_gender, col1='Gender', col2='Average Bonus') +print("\nAverage Bonus by Gender as Markdown Table:") +print(avg_bonus_markdown) diff --git a/markDown/time.ipynb b/markDown/time.ipynb new file mode 100644 index 0000000000000..c4d61b4fdb046 --- /dev/null +++ b/markDown/time.ipynb @@ -0,0 +1,16991 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from tabulate import tabulate\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def markdownTable(df, *column_names):\n", + " if not column_names:\n", + " column_names = df.columns.tolist()\n", + " table_markdown = \"| \" + \" | \".join(column_names) + \" |\\n\"\n", + " table_markdown += \"| \" + \" | \".join([\"---\"] * len(column_names)) + \" |\\n\"\n", + " for index, row in df.iterrows():\n", + " row_data = [str(row[col]) for col in column_names]\n", + " table_markdown += \"| \" + \" | \".join(row_data) + \" |\\n\"\n", + " return table_markdown\n", + "\n", + "\n", + "def markdownSeries(series, col1=None, col2=None):\n", + " if not col1:\n", + " col1 = series.index.name if series.index.name else \"Index\"\n", + " if not col2:\n", + " col2 = series.name if series.name else \"Value\"\n", + " table_markdown = f\"| {col1} | {col2} |\\n|---|---|\\n\"\n", + " for index, value in series.items():\n", + " table_markdown += f\"| {index} | {value} |\\n\"\n", + " return table_markdown\n", + "\n", + "\n", + "pd.DataFrame.markdownTable = markdownTable\n", + "pd.Series.markdownTable = markdownSeries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "Filtered DataFrame as Markdown Table:\n", + "| Name | Age | Gender | Salary | Department | Bonus |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| David | 40 | Male | 80000 | Marketing | 8002.0 |\n", + "| Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "\n", + "\n", + "Average Bonus by Gender as Markdown Table:\n", + "| Gender | Average Bonus |\n", + "|---|---|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "\n", + "643 μs ± 25.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit -n 100\n", + "\n", + "# Create a hypothetical dataset\n", + "data = {\n", + " 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],\n", + " 'Age': [25, 30, 35, 40, 45],\n", + " 'Gender': ['Female', 'Male', 'Male', 'Male', 'Female'],\n", + " 'Salary': [50000, 60000, 70000, 80000, 90000],\n", + " 'Department': ['HR', 'Finance', 'IT', 'Marketing', 'Operations']\n", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Create a new column 'Bonus' based on complex calculation\n", + "df['Bonus'] = df['Salary'] * 0.1 + df['Age'] * 0.05\n", + "\n", + "# Perform some complex operations on the DataFrame\n", + "# For example, let's filter the DataFrame for individuals with Age > 30 and Salary > 60000\n", + "filtered_df = df[(df['Age'] > 30) & (df['Salary'] > 60000)]\n", + "\n", + "# Convert the filtered DataFrame to a markdown table\n", + "filtered_markdown_table = markdownTable(filtered_df)\n", + "print(\"Filtered DataFrame as Markdown Table:\")\n", + "print(filtered_markdown_table)\n", + "\n", + "# Calculate the average Bonus for each gender\n", + "avg_bonus_by_gender = df.groupby('Gender')['Bonus'].mean()\n", + "\n", + "# Convert the Series to a markdown table\n", + "avg_bonus_markdown = markdownSeries(avg_bonus_by_gender, col1='Gender', col2='Average Bonus')\n", + "print(\"\\nAverage Bonus by Gender as Markdown Table:\")\n", + "print(avg_bonus_markdown)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "Filtered DataFrame as Markdown Table:\n", + "| | Name | Age | Gender | Salary | Department | Bonus |\n", + "|---:|:--------|------:|:---------|---------:|:-------------|--------:|\n", + "| 2 | Charlie | 35 | Male | 70000 | IT | 7001.75 |\n", + "| 3 | David | 40 | Male | 80000 | Marketing | 8002 |\n", + "| 4 | Eve | 45 | Female | 90000 | Operations | 9002.25 |\n", + "| Gender | Bonus |\n", + "|:---------|--------:|\n", + "| Female | 7001.75 |\n", + "| Male | 7001.75 |\n", + "1 ms ± 70.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit -n 100\n", + "\n", + "# Create a hypothetical dataset\n", + "data = {\n", + " 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],\n", + " 'Age': [25, 30, 35, 40, 45],\n", + " 'Gender': ['Female', 'Male', 'Male', 'Male', 'Female'],\n", + " 'Salary': [50000, 60000, 70000, 80000, 90000],\n", + " 'Department': ['HR', 'Finance', 'IT', 'Marketing', 'Operations']\n", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Create a new column 'Bonus' based on complex calculation\n", + "df['Bonus'] = df['Salary'] * 0.1 + df['Age'] * 0.05\n", + "\n", + "# Perform some complex operations on the DataFrame\n", + "# For example, let's filter the DataFrame for individuals with Age > 30 and Salary > 60000\n", + "filtered_df = df[(df['Age'] > 30) & (df['Salary'] > 60000)]\n", + "\n", + "# Convert the filtered DataFrame to a markdown table\n", + "print(\"Filtered DataFrame as Markdown Table:\")\n", + "print(filtered_df.to_markdown())\n", + "\n", + "# Calculate the average Bonus for each gender\n", + "avg_bonus_by_gender = df.groupby('Gender')['Bonus'].mean()\n", + "\n", + "print(avg_bonus_by_gender.to_markdown())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# data from %%timeit runs\n", + "labels = ['Custom Markdown', 'Tabulate Markdown']\n", + "means = [643, 1000]\n", + "std_devs = [25.9, 70.9]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.bar(labels, means, yerr=std_devs, capsize=10, color=['skyblue', 'lightgreen'])\n", + "\n", + "ax.set_ylabel('Time (μs)')\n", + "ax.set_title('Performance Comparison of Markdown Formatting Methods')\n", + "ax.set_ylim(0, 1200) # Set y-axis limit for better visualization\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}