Merging DataFrames on Multiple Columns with Different Names in Pandas
Pandas: Custom Function Exercise-19 with Solution
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'].
Python-Pandas Code Editor:
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