Merging two DataFrames on multiple columns using merge() in Pandas

Last update on December 21 2024 07:41:30 (UTC/GMT +8 hours)

Pandas: Custom Function Exercise-5 with Solution

Write a Pandas program to merge two DataFrames on multiple columns.

In this exercise shows how to merge two DataFrames on multiple columns using pd.merge().

Sample Solution :

Code :

import pandas as pd

# Create two sample DataFrames
df1 = pd.DataFrame({
    'ID': [1, 2, 3],
    'Name': ['Selena', 'Annabel', 'Caeso'],
    'Age': [25, 30, 22]
})

df2 = pd.DataFrame({
    'ID': [1, 2, 3],
    'Name': ['Selena', 'Annabel', 'Caeso'],
    'Salary': [50000, 60000, 70000]
})

# Merge the DataFrames on both 'ID' and 'Name' columns
merged_df = pd.merge(df1, df2, on=['ID', 'Name'])

# Output the result
print(merged_df)

Output:

   ID     Name  Age  Salary
0   1   Selena   25   50000
1   2  Annabel   30   60000
2   3    Caeso   22   70000              

Explanation:

• Created two DataFrames df1 and df2 with common columns 'ID' and 'Name'.
• Used pd.merge() to merge on both the 'ID' and 'Name' columns.
• The result includes rows where both columns have matching values in both DataFrames.

Python-Pandas Code Editor:

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