docs: Write PandasBasics Page Content + Styling

src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx

@@ -2,12 +2,96 @@ import React from "react";
 
 const PandasBasics = () => {
   return (
-    <div className=" break-words w-screen lg:w-auto overflow-x-hidden px-7 my-10">
-      <h1>Introduction to Pandas</h1>
+    <div className="w-full lg:w-auto p-6 lg:p-10">
+      <h1 className="text-3xl font-bold text-gray-700">Introduction to Pandas</h1>
 
-      <p>Development can start for Pandas from here</p>
+      <p className="mt-4 text-gray-600 text-lg">
+        Welcome to the world of Pandas, where you'll learn to wield the power of data with ease. Pandas is more than just a cute bear; it's also a versatile Python library that empowers you to manipulate, analyze, and visualize data effortlessly. Let's dive deeper into Pandas basics.
+      </p>
+
+      <h2 className="text-2xl font-bold text-gray-700 mt-6">What is Pandas?</h2>
+
+      <p className="mt-4 text-gray-600 text-lg">
+        Pandas is a data manipulation and analysis library for Python. It provides you with easy-to-use data structures and functions that enable you to work with structured data effectively. If you've ever worked with data in spreadsheets or databases, Pandas will feel like a familiar friend.
+      </p>
+      <p className="mt-2 text-gray-600 text-lg">
+        At its core, Pandas introduces two main data structures: the <strong>Series</strong> and the <strong>DataFrame</strong>. A Series is like a one-dimensional array with labels, while a DataFrame is a two-dimensional table like a spreadsheet or SQL table. These structures open up endless possibilities for data manipulation.
+      </p>
+
+      <h2 className="text-2xl font-bold text-gray-700 mt-6">Key Features of Pandas:</h2>
+      <ul className="list-disc ml-6 text-gray-600 text-lg">
+        <li>
+          <b>Data Structures:</b> Pandas provides the Series and DataFrame data structures to store and work with data. These structures are highly flexible and can handle various data types.
+        </li>
+        <li>
+          <b>Data Cleaning:</b> Data is rarely perfect. Pandas offers powerful tools to clean and preprocess data, such as handling missing values, removing duplicates, and more.
+        </li>
+        <li>
+          <b>Data Selection:</b> You can easily select and filter data, allowing you to focus on the information relevant to your analysis.
+        </li>
+        <li>
+          <b>Data Aggregation:</b> Pandas enables you to summarize data, create pivot tables, and aggregate information based on specific criteria.
+        </li>
+        <li>
+          <b>Data Visualization:</b> While not a dedicated visualization library, Pandas can work seamlessly with libraries like Matplotlib and Seaborn to create insightful plots and charts.
+        </li>
+      </ul>
+
+      <h2 className="text-2xl font-bold text-gray-700 mt-6">Why Use Pandas?</h2>
+
+      <p className="mt-4 text-gray-600 text-lg">
+        You might be wondering, "Why should I bother with Pandas when I can work with data in other ways?" The answer lies in Pandas' efficiency and user-friendliness.
+      </p>
+      <p className="mt-2 text-gray-600 text-lg">
+        Without Pandas, you'd find yourself writing numerous lines of code to perform even the simplest data operations. Pandas simplifies data manipulation tasks, streamlining the process and making it more intuitive.
+      </p>
+      <p className="text-gray-600 text-lg mt-2">
+        Here are a few scenarios where Pandas shines:
+      </p>
+      <ul className="list-disc ml-6 text-gray-600 text-lg">
+        <li>
+          <b>Data Exploration:</b> Quickly get an overview of your dataset by inspecting its structure and contents.
+        </li>
+        <li>
+          <b>Data Cleaning:</b> Address issues like missing values, outliers, and duplicate records without the headache of manual data wrangling.
+        </li>
+        <li>
+          <b>Data Transformation:</b> Shape your data to suit your analysis needs, whether it's reshaping, pivoting, or aggregating.
+        </li>
+        <li>
+          <b>Data Analysis:</b> Dive into in-depth analysis by selecting, filtering, and aggregating data, and create visualizations for better insights.
+        </li>
+      </ul>
+
+      <h2 className="text-2xl font-bold text-gray-700 mt-6">How to Get Started with Pandas:</h2>
+      <p className="mt-4 text-gray-600 text-lg">
+        Are you excited to harness the power of Pandas? Let's get you started with some foundational steps:
+      </p>
+      <ol className="list-decimal ml-6 text-gray-600 text-lg">
+        <li>
+          <b>Install Pandas:</b> First, ensure you have Python installed on your system. Then, open your terminal or command prompt and install Pandas using pip:
+          <div className="bg-gray-800 p-4 rounded-lg mt-2">
+            <pre className="text-gray-100">pip install pandas</pre>
+          </div>
+        </li>
+        <li>
+          <b>Import Pandas:</b> In your Python script or Jupyter Notebook, import Pandas to access its functionality:
+          <div className="bg-gray-800 p-4 rounded-lg mt-2">
+            <pre className="text-gray-100">import pandas as pd</pre>
+          </div>
+        </li>
+        <li>
+          <b>Create a DataFrame:</b> You can create a DataFrame from various data sources, such as Python dictionaries or by reading data from files like CSV, Excel, or a SQL database.
+        </li>
+        <li>
+          <b>Start Exploring:</b> Dive into your data. Use Pandas functions to examine your dataset's structure, clean it if necessary, and begin your analysis.
+        </li>
+      </ol>
+      <p className="text-gray-600 text-lg mt-6">
+        Get ready to embark on your journey into the world of data analysis and manipulation with Pandas. Whether you're working on business data, scientific research, or any data-related project, Pandas simplifies your path to success. Remember, practice and experimentation are key to mastering Pandas, so don't hesitate to get your hands dirty with real data.
+      </p>
     </div>
   );
 };
 
-export default PandasBasics;
+export default PandasBasics;