Merging DataFrames on Multiple Columns with Different Names in Pandas

Last update on April 09 2025 12:52:32 (UTC/GMT +8 hours)

19. Merge Multi-Col Diff Names

Write a Pandas program to merge DataFrames on multiple columns with different names.

In this exercise, we have merged two DataFrames on multiple columns where the columns have different names in each DataFrame.

Sample Solution :

Code :

import pandas as pd

# Create two sample DataFrames with matching values
df1 = pd.DataFrame({
    'Employee_ID': [1, 2, 3],
    'Name': ['Selena', 'Annabel', 'Caeso']  # Matching names with df2
})

df2 = pd.DataFrame({
    'ID': [1, 2, 3],
    'Employee_Name': ['Selena', 'Annabel', 'Caeso'],  # Matching names with df1
    'Salary': [50000, 60000, 70000]
})

# Merge the DataFrames on different column names
merged_df = pd.merge(df1, df2, left_on=['Employee_ID', 'Name'], right_on=['ID', 'Employee_Name'])

# Output the result
print(merged_df)

Output:

   Employee_ID     Name  ID Employee_Name  Salary
0            1   Selena   1        Selena   50000
1            2  Annabel   2       Annabel   60000
2            3    Caeso   3         Caeso   70000

Explanation:

The DataFrames df1 and df2 now have matching values for 'Name' and 'Employee_Name'.
The merge() function now correctly merges based on left_on=['Employee_ID', 'Name'] and right_on=['ID', 'Employee_Name'].

For more Practice: Solve these Related Problems:

Write a Pandas program to merge two DataFrames on multiple columns with different names and validate the merge with a summary statistic.
Write a Pandas program to merge two DataFrames on multiple columns with different names and create a new composite key column.
Write a Pandas program to merge two DataFrames on multiple columns with different names and filter out rows with mismatched key data types.
Write a Pandas program to merge two DataFrames on multiple columns with different names and then sort the result based on the composite key.

Python-Pandas Code Editor:

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