Merging two DataFrames with multiple Keys and conditions in Pandas

Last update on December 21 2024 09:15:03 (UTC/GMT +8 hours)

Pandas: Custom Function Exercise-13 with Solution

Write a Pandas program to merge two DataFrames using multiple keys and specific join conditions.

This exercise shows how to merge two DataFrames using multiple keys and specific join conditions (inner, left, etc.).

Sample Solution :

Code :

import pandas as pd

# Create two sample DataFrames
df1 = pd.DataFrame({
    'ID': [1, 2, 3, 4],
    'Name': ['Selena', 'Annabel', 'Charlie', 'Caeso'],
    'City': ['NY', 'LA', 'NY', 'LA']
})

df2 = pd.DataFrame({
    'ID': [2, 3, 4, 5],
    'Name': ['Annabel', 'Charlie', 'Caeso', 'Eve'],
    'City': ['LA', 'NY', 'LA', 'NY'],
    'Age': [30, 22, 25, 28]
})

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

# Output the result
print(merged_df)

Output:

   ID   Name_x City   Name_y  Age
0   2  Annabel   LA  Annabel   30
1   3  Charlie   NY  Charlie   22
2   4    Caeso   LA    Caeso   25   

Explanation:

• Created two DataFrames df1 and df2 with shared columns 'ID' and 'City'.
• Used pd.merge() to merge on both 'ID' and 'City' with an inner join.
• The result includes only rows where both 'ID' and 'City' match in both DataFrames.

Python-Pandas Code Editor:

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