How Do Different Pandas `merge()` Join Types Combine DataFrames?

Pandas Merging 101: The Basics

Introduction

Merging DataFrames in Pandas is a powerful tool for combining and manipulating data from different sources. This guide provides a comprehensive overview of the basic types of joins and their applications.

Types of Joins

1. INNER JOIN (default)

• Matches rows with common keys in both DataFrames.
• Returns only rows that have matching values in both frames.

• Example:

  left.merge(right, on='key')

2. LEFT OUTER JOIN

• Matches rows from the left DataFrame with corresponding rows in the right DataFrame.
• If no matching row is found, NaNs are inserted in the output for the missing columns from the right DataFrame.

• Example:

  left.merge(right, on='key', how='left')

3. RIGHT OUTER JOIN

• Matches rows from the right DataFrame with corresponding rows in the left DataFrame.
• If no matching row is found, NaNs are inserted in the output for the missing columns from the left DataFrame.

• Example:

  left.merge(right, on='key', how='right')

4. FULL OUTER JOIN

• Matches all rows from both DataFrames, regardless of whether they have common keys.
• NaNs are inserted for missing rows in both frames.

• Example:

  left.merge(right, on='key', how='outer')

Other Join Variations

1. LEFT-Excluding JOIN

• Returns rows from the left DataFrame that do not match any rows in the right DataFrame.

2. RIGHT-Excluding JOIN

• Returns rows from the right DataFrame that do not match any rows in the left DataFrame.

3. ANTI JOIN (Excluding on Either Side)

• Returns rows from both DataFrames that do not match any rows on the other side.

Handling Different Key Column Names

• Use left_on and right_on arguments to merge on columns with different names.

Avoiding Duplicate Key Columns in Output

• Set the index as a preliminary step to merge on the index and eliminate the duplicate key column.

Merging Single Column from One DataFrame

• Subset columns before merging to select specific columns from one of the DataFrames.
• Use map for a more efficient approach in cases where only one column is being merged.

Merging on Multiple Columns

• Specify a list for on (or left_on and right_on) to join on multiple columns.

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