How to Use Left Merge in Pandas for DataFrames

Use pandas.merge() with the parameter how='left' to join two DataFrames, keeping all rows from the left DataFrame and matching rows from the right. This is useful when you want to keep all data from one table and add matching information from another.

Syntax

The basic syntax for a left merge in pandas is:

pandas.merge(left, right, how='left', on=None, left_on=None, right_on=None)

Explanation:

• left: The left DataFrame.
• right: The right DataFrame to merge with.
• how='left': Specifies a left join, keeping all rows from left.
• on: Column name(s) to join on, must be present in both DataFrames.
• left_on and right_on: Use these if the join columns have different names in each DataFrame.

python

import pandas as pd

merged_df = pd.merge(left_df, right_df, how='left', on='key_column')

Example

This example shows how to merge two DataFrames using a left merge on a common column called id. All rows from the left DataFrame are kept, and matching rows from the right DataFrame are added.

python

import pandas as pd

# Left DataFrame
left_df = pd.DataFrame({
    'id': [1, 2, 3, 4],
    'name': ['Alice', 'Bob', 'Charlie', 'David']
})

# Right DataFrame
right_df = pd.DataFrame({
    'id': [2, 4, 5],
    'score': [88, 92, 75]
})

# Left merge on 'id'
merged_df = pd.merge(left_df, right_df, how='left', on='id')
print(merged_df)

Output

   id     name  score
0   1    Alice    NaN
1   2      Bob   88.0
2   3  Charlie    NaN
3   4    David   92.0

Common Pitfalls

Common mistakes when using left merge include:

• Not specifying how='left', which defaults to an inner join and drops unmatched rows.
• Using on with columns that don't exist in both DataFrames.
• Forgetting that unmatched rows from the right DataFrame will have NaN values in the result.

Example of a wrong and right way:

python

# Wrong: missing how='left' (defaults to inner join)
import pandas as pd

left_df = pd.DataFrame({'id': [1, 2]})
right_df = pd.DataFrame({'id': [2, 3]})

inner_merge = pd.merge(left_df, right_df, on='id')
print('Inner merge result:')
print(inner_merge)

# Right: specify how='left'
left_merge = pd.merge(left_df, right_df, how='left', on='id')
print('\nLeft merge result:')
print(left_merge)

Output

Inner merge result:
   id
0   2

Left merge result:
   id
0   1
1   2

Quick Reference

Tips for using left merge in pandas:

• Use how='left' to keep all rows from the left DataFrame.
• Use on when the join column has the same name in both DataFrames.
• Use left_on and right_on if join columns differ.
• Unmatched rows from the right DataFrame will have NaN in the result.
• Check your data types on join columns to avoid unexpected results.

Key Takeaways

Use pandas.merge() with how='left' to keep all rows from the left DataFrame and add matching rows from the right.

Specify the join column(s) with on, or left_on and right_on if names differ.

Unmatched rows from the right DataFrame will have NaN values in the merged result.

Not specifying how='left' defaults to inner join, which drops unmatched rows.

Always check that join columns exist and have compatible data types in both DataFrames.