Pandas Fundamentals | Join, Merge & Concatenate

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In this video, you’ll learn how to combine data sets using Pandas in Python for effective data analysis. We cover essential techniques such as concatenating, merging, and joining data frames. You’ll understand when to use vertical and horizontal concatenation, inner and outer joins, and how to merge data frames based on single or multiple columns. Practical examples are provided throughout the video to help you master these key Pandas methods.

To start learning without installing Python locally, use Google Colab.
Introduction to Google Colab:    • Google Colab Basics  
Create a new Google Colab Notebook: colab.research.google.com/#create=true

Table of Contents:

0:00 Introduction to Combining Data Sets
0:09 Overview of Pandas Methods
0:17 Understanding Concatenation
0:25 Stacking data vertically and horizontally
0:30 Creating sales data frames
0:46 Vertical Concatenation
0:54 Using the concat method
3:01 Horizontal Concatenation
3:05 Adding axis=1 for column-wise combination
3:41 Handling unmatched indexes
4:32 Introduction to Merging
4:42 Matching rows based on columns
6:00 Inner Join
6:10 Default merging behavior
8:17 Outer Join
8:28 Including all rows with missing values filled with NAN
9:34 Left Join
9:49 Including all rows from the left data frame
10:18 Right Join
10:30 Including all rows from the right data frame
11:12 Merging on Multiple Columns
11:36 Using multiple keys for merging
13:15 Introduction to Joining
13:20 Combining data frames based on index
15:35 Inner Join with Indexes
15:58 Matching indexes between data frames
16:46 Outer Join with Indexes
16:56 Including all rows regardless of matching indexes
17:09 Join the AI Learning Hub Community
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