Merging and Joining DataFrames
In real-world data projects, information is often distributed across multiple tables.
For instance, one DataFrame might store customer details, while another contains their purchase records.
To analyze them effectively, you can combine them using merge or join operations in Pandas.
Merge and Join Basics
Pandas provides flexible tools for combining data:
pd.merge()combines rows from two DataFrames based on matching column values, similar to SQL joins..join()is a method that adds columns from one DataFrame to another using the index or a key column.
Common Join Types
| Join Type | Description |
|---|---|
| Inner | Keeps only matching rows (default). |
| Left | Keeps all rows from the left DataFrame and adds matches from the right. |
| Right | Keeps all rows from the right DataFrame and adds matches from the left. |
| Outer | Keeps all rows from both DataFrames; missing values are filled with NaN. |
These join types give you precise control over how data is aligned and combined — from strict matches to complete unions.
Quiz
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What function would you use in pandas to merge two DataFrames based on column values, similar to SQL joins?
To combine two DataFrames based on common column values, you use the function in pandas.
pd.concat()
pd.merge()
pd.append()
pd.groupBy()
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