Merging Multiple DataFrames on a Common Column in Pandas

Last update on December 21 2024 07:41:33 (UTC/GMT +8 hours)

Pandas: Custom Function Exercise-10 with Solution

Write a Pandas program to merge multiple DataFrames on a common column.

Following exercise shows how to merge three DataFrames on a common column.

Sample Solution :

Code :

import pandas as pd

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

df2 = pd.DataFrame({
    'ID': [1, 2, 3],
    'Age': [25, 30, 22]
})

df3 = pd.DataFrame({
    'ID': [1, 2, 3],
    'Salary': [50000, 60000, 70000]
})

# Merge the three DataFrames on the 'ID' column
merged_df = pd.merge(pd.merge(df1, df2, on='ID'), df3, on='ID')

# 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 three DataFrames df1, df2, and df3 with a common column 'ID'.
• Used pd.merge() twice to merge all three DataFrames on the 'ID' column.
• The result is a merged DataFrame that includes data from all three DataFrames.

Python-Pandas Code Editor:

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