Pandas: Drop a level from a multi-level column index

Last update on May 01 2025 12:55:54 (UTC/GMT +8 hours)

21. Drop an Index Level from Multi-level Column Index

Write a Pandas program to drop a index level from a multi-level column index of a dataframe.

Note: Levels are 0-indexed beginning from the top.

Sample Solution:

Python Code :

import pandas as pd
cols = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("a", "z")])
print("\nConstruct a Dataframe using the said MultiIndex levels: ")
df = pd.DataFrame([[1,2,3], [3,4,5], [5,6,7]], columns=cols)
print(df)
#Levels are 0-indexed beginning from the top.
print("\nRemove the top level index:")
df.columns = df.columns.droplevel(0)
print(df)
df = pd.DataFrame([[1,2,3], [3,4,5], [5,6,7]], columns=cols)
print("\nOriginal dataframe:")
print(df)
print("\nRemove the index next to top level:")
df.columns = df.columns.droplevel(1)
print(df)

Sample Output:

Construct a Dataframe using the said MultiIndex levels: 
   a      
   x  y  z
0  1  2  3
1  3  4  5
2  5  6  7

Remove the top level index:
   x  y  z
0  1  2  3
1  3  4  5
2  5  6  7

Original dataframe:
   a      
   x  y  z
0  1  2  3
1  3  4  5
2  5  6  7

Remove the index next to top level:
   a  a  a
0  1  2  3
1  3  4  5
2  5  6  7

For more Practice: Solve these Related Problems:

Write a Pandas program to drop a specified level from a MultiIndex in the columns and then display the resulting DataFrame.
Write a Pandas program to remove one level of a multi-level column index and then verify the new column headers.
Write a Pandas program to flatten a multi-level column index by dropping one of its levels and then output the DataFrame.
Write a Pandas program to eliminate a specific level from a MultiIndex column and then reassign new column names based on the remaining levels.

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Python Code Editor:

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