Python – Remove multiples levels using the level names and return the index

In Pandas, you can remove multiple levels from a MultiIndex using the droplevel() method. This method accepts level names or positions as parameters and returns a new index with the specified levels removed.

Syntax

MultiIndex.droplevel(level)

Parameters:

• level ? Level name(s) or position(s) to drop. Can be a single value or a list of values.

Creating a MultiIndex

First, let's create a MultiIndex with named levels ?

import pandas as pd

# Create a multi-index with named levels
multiIndex = pd.MultiIndex.from_arrays(
    [[5, 10], [15, 20], [25, 30], [35, 40]], 
    names=['a', 'b', 'c', 'd']
)

print("Original MultiIndex:")
print(multiIndex)

Original MultiIndex:
MultiIndex([( 5, 15, 25, 35),
            (10, 20, 30, 40)],
           names=['a', 'b', 'c', 'd'])

Dropping Multiple Levels by Names

Remove specific levels by passing their names as a list ?

import pandas as pd

multiIndex = pd.MultiIndex.from_arrays(
    [[5, 10], [15, 20], [25, 30], [35, 40]], 
    names=['a', 'b', 'c', 'd']
)

# Drop levels 'a' and 'd' by name
result = multiIndex.droplevel(['a', 'd'])
print("After dropping levels 'a' and 'd':")
print(result)

After dropping levels 'a' and 'd':
MultiIndex([(15, 25),
            (20, 30)],
           names=['b', 'c'])

Dropping Levels by Position

You can also drop levels using their numeric positions ?

import pandas as pd

multiIndex = pd.MultiIndex.from_arrays(
    [[5, 10], [15, 20], [25, 30], [35, 40]], 
    names=['a', 'b', 'c', 'd']
)

# Drop levels at positions 0 and 3 (same as 'a' and 'd')
result = multiIndex.droplevel([0, 3])
print("After dropping levels at positions 0 and 3:")
print(result)

After dropping levels at positions 0 and 3:
MultiIndex([(15, 25),
            (20, 30)],
           names=['b', 'c'])

Practical Example with DataFrame

Here's how droplevel() works with a DataFrame ?

import pandas as pd

# Create DataFrame with MultiIndex
arrays = [['A', 'A', 'B', 'B'], ['X', 'Y', 'X', 'Y'], [1, 2, 1, 2]]
index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second', 'third'])
df = pd.DataFrame({'values': [10, 20, 30, 40]}, index=index)

print("Original DataFrame:")
print(df)
print("\nAfter dropping 'first' and 'third' levels:")
print(df.droplevel(['first', 'third']))

Original DataFrame:
              values
first second third       
A     X      1         10
      Y      2         20
B     X      1         30
      Y      2         40

After dropping 'first' and 'third' levels:
        values
second        
X           10
Y           20
X           30
Y           40

Conclusion

Use MultiIndex.droplevel() to remove specific levels from a MultiIndex by passing level names or positions as parameters. This method returns a new index with the specified levels removed, making it useful for simplifying complex hierarchical data structures.