How to Use Inner Merge in pandas for DataFrames

Use pandas.merge() with the parameter how='inner' to combine two DataFrames by keeping only rows with matching keys in both. This performs an inner join, returning the intersection of the data based on specified columns.

Syntax

The basic syntax for an inner merge in pandas is:

• left: The first DataFrame.
• right: The second DataFrame.
• on: Column name(s) to join on. Must be present in both DataFrames.
• how='inner': Specifies an inner join to keep only matching rows.

python

pd.merge(left, right, on='key_column', how='inner')

Example

This example shows how to merge two DataFrames on a common column using an inner join. Only rows with matching keys in both DataFrames are kept.

python

import pandas as pd

# Create first DataFrame
left = pd.DataFrame({
    'key': ['A', 'B', 'C', 'D'],
    'value_left': [1, 2, 3, 4]
})

# Create second DataFrame
right = pd.DataFrame({
    'key': ['B', 'C', 'E', 'F'],
    'value_right': [5, 6, 7, 8]
})

# Perform inner merge on 'key'
result = pd.merge(left, right, on='key', how='inner')
print(result)

Output

  key  value_left  value_right
0   B           2            5
1   C           3            6

Common Pitfalls

Common mistakes when using inner merge include:

• Not specifying the on parameter correctly, leading to unexpected merges or errors.
• Using columns with different names in each DataFrame without specifying left_on and right_on.
• Assuming inner merge keeps all rows; it only keeps rows with keys present in both DataFrames.

python

import pandas as pd

# Wrong: columns have different names but 'on' is used
left = pd.DataFrame({'key1': ['A', 'B'], 'val': [1, 2]})
right = pd.DataFrame({'key2': ['B', 'C'], 'val': [3, 4]})

# This will raise an error because 'key1' and 'key2' differ
# pd.merge(left, right, on='key1', how='inner')  # Error

# Correct way: specify left_on and right_on
correct_merge = pd.merge(left, right, left_on='key1', right_on='key2', how='inner')
print(correct_merge)

Output

  key1  val_x key2  val_y
0    B      2    B      3

Quick Reference

Parameter	Description	Example
left	First DataFrame to merge	pd.merge(left, right, ...)
right	Second DataFrame to merge	pd.merge(left, right, ...)
on	Column(s) to join on (must exist in both)	'key'
left_on	Column(s) from left DataFrame if names differ	'key1'
right_on	Column(s) from right DataFrame if names differ	'key2'
how	Type of merge: 'inner' keeps only matching rows	'inner'

Key Takeaways

Use pd.merge() with how='inner' to keep only rows with matching keys in both DataFrames.

Specify the 'on' parameter to define the join column(s) when names match in both DataFrames.

Use 'left_on' and 'right_on' if join columns have different names in each DataFrame.

Inner merge returns the intersection of data, excluding non-matching rows.

Always check column names and data types to avoid merge errors.