Merging and Joining DataFrames
In real-world data tasks, information is often spread across multiple tables.
For example, one DataFrame might hold customer information, while another contains their orders.
To analyze them together, you'll need to merge or join the datasets.
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 joins let you control how much data you include, whether you want a strict match or a full combination.
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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