Lesson df merging

Author: pubudu

content/0.Numpy_for_bioinformatics.md

@@ -1,4 +1,4 @@
-# Lesson plan: Numpy for biological data handling
+# Lesson plan
 
 ## Overall objectives
 

content/10.DataFrame_Manipulation.md

@@ -1,18 +1,21 @@
 # DataFrame Manipulation & Sorting
 
-## Introduction
+:::{objectives}
 
-**DataFrame Manipulation & Sorting:**
+* Demonstrate effective techniques for selecting specific rows and from DataFrames.
+* Equip participants with skills to add and remove columns within a DataFrame.
+* Implement sorting methods by values, by index, and perform multiple column sorting with custom orders.
+:::
 
-* Now that we can import data, we need to reshape it for analysis
-* Most real-world datasets need significant manipulation before analysis
-* Selecting, adding, removing, and reordering data are fundamental skills
-* These operations build directly on our understanding of DataFrames as labeled, 2D structures
+:::{exercise} Time
+20 minutes
+:::
 
 :::{discussion}
 
+* Now that we can import data, we need to reshape and manipulate it for analysis
 * In real-world data analysis, you'll spend about 80% of your time cleaning and manipulating data, and only 20% on actual analysis
-* The skills we're covering today form the backbone of data wrangling in Python
+* The skills we're covering in this session form the backbone of data wrangling in Python
 * Think of these operations as transforming raw data into analysis-ready information
 
 :::
@@ -296,7 +299,7 @@ Understand the different ways to select data from a DataFrame, including column
   * multiple columns
   * columns in-place
 
-:::{done}
+:::{demo}
 
 ```python
 
@@ -787,45 +790,14 @@ emp004    David   42  Seattle   92000       Tech       Tech
 
 :::{discussion}
 
-* "Sorting is essential for both data analysis and presentation."
-* "Multi-column sorting is particularly useful for hierarchical data."
-* "The `ascending` parameter can be a single boolean or a list of booleans for multi-column sorts."
-* "Custom sorting allows for domain-specific ordering beyond simple alphabetical or numerical order."
-* "The `inplace=True` parameter can be used to modify the original DataFrame rather than creating a new one."
+* Sorting is essential for both data analysis and presentation.
+* Multi-column sorting is particularly useful for hierarchical data.
+* The `ascending` parameter can be a single boolean or a list of booleans for multi-column sorts.
+* Custom sorting allows for domain-specific ordering beyond simple alphabetical or numerical order.
+* The `inplace=True` parameter can be used to modify the original DataFrame rather than creating a new one.
 
 :::
 
-**Exercise 3 - Sorting Practice:**
-
-Have students execute:
-```python
-# Continue with the final inventory DataFrame from Exercise 2
-print("Original inventory:")
-print(inventory_final)
-
-# 1. Sort the inventory by Category, then by Price (descending) within each category
-sorted_by_cat_price = inventory_final.sort_values(['Category', 'Price'], 
-                                               ascending=[True, False])
-print("\n1. Sorted by Category, then by Price (descending):")
-print(sorted_by_cat_price)
-
-# 2. Sort the inventory by Value (highest first)
-sorted_by_value = inventory_final.sort_values('Value', ascending=False)
-print("\n2. Sorted by Total Value (descending):")
-print(sorted_by_value)
-
-# 3. Advanced: Create a custom sorting order for Status
-# Make 'Available' come before 'Out of Stock'
-inventory_final['Status_Coded'] = pd.Categorical(
-    inventory_final['Status'],
-    categories=['Available', 'Out of Stock'],
-    ordered=True
-)
-sorted_by_status = inventory_final.sort_values('Status_Coded')
-print("\n3. Sorted by Status (custom order):")
-print(sorted_by_status[['Product_Name', 'Status', 'Status_Coded']])
-```
-
 :::{exercise}
 
 **Multiple Column Sorting with Custom Orders:**