5 Best Ways to Remove Multiple Levels Using Level Names in Python and Return the Index

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💡 Problem Formulation: In data structures such as pandas DataFrames with multi-level indices, there might be circumstances where one needs to remove specific levels by their names. This article provides ways to manipulate a multi-level index to remove chosen levels and return the modified index. For example, from an index with levels (‘Year’, ‘Month’, ‘Day’), one might want to remove ‘Month’ and ‘Day’ to work solely with ‘Year’.

Method 1: Using droplevel() Method in pandas

This method leverages the droplevel() method provided by the pandas library. It’s designed for dropping specified levels from a multi-index by name or level number, efficiently returning a new index without those levels.

Here’s an example:

import pandas as pd

# Create a multi-level index
index = pd.MultiIndex.from_tuples([('2021', 'Jan', '01'), ('2021', 'Feb', '02')], names=['Year', 'Month', 'Day'])

# Remove 'Month' and 'Day' levels
new_index = index.droplevel(['Month', 'Day'])

print(new_index)

Output:

Index(['2021', '2021'], dtype='object', name='Year')

This code first creates a multi-level index with year, month, and day. Then, it removes the ‘Month’ and ‘Day’ levels using the droplevel() function, resulting in a new index that only contains the ‘Year’ level.

Method 2: Using List Comprehension and get_level_values()

List comprehension alongside the get_level_values() method is used to extract values from the desired level while excluding unwanted levels, and then reform to create a new index.

Here’s an example:

import pandas as pd

# Create a multi-level index
index = pd.MultiIndex.from_tuples([('2021', 'Jan', '01'), ('2021', 'Feb', '02')], names=['Year', 'Month', 'Day'])

# Keep only the 'Year' level using list comprehension
new_index = pd.Index([year for year in index.get_level_values('Year')])

print(new_index)

Output:

Index(['2021', '2021'], dtype='object')

The code uses a list comprehension to iterate over the ‘Year’ values of our multi-level index and reconstructs a new pandas Index object with only those values.

Method 3: Using the reset_index() Method

The reset_index() method flattens the levels of a multi-index by converting them into columns in a DataFrame, which allows specific levels to be dropped and the remaining index to be reconstructed.

Here’s an example:

import pandas as pd

# Create a multi-level index
index = pd.MultiIndex.from_tuples([('2021', 'Jan', '01'), ('2021', 'Feb', '02')], names=['Year', 'Month', 'Day'])
df = pd.DataFrame(index=index)

# Remove specific levels and construct a new index
df = df.reset_index(level=['Month', 'Day'], drop=True)
new_index = df.index

print(new_index)

Output:

Index(['2021', '2021'], dtype='object', name='Year')

The code snippet demonstrates how to turn a multi-level index into a DataFrame in order to use the reset_index() method for removing unwanted levels. The remaining ‘Year’ level becomes the new DataFrame’s index, which can then be used as needed.

Method 4: Re-indexing with a Sliced Tuple

By slicing the existing index’s tuples, we can filter out unnecessary levels and construct a new index with the desired levels.

Here’s an example:

import pandas as pd

# Create a multi-level index
index = pd.MultiIndex.from_tuples([('2021', 'Jan', '01'), ('2021', 'Feb', '02')], names=['Year', 'Month', 'Day'])

# Create a new index by taking the first element of each tuple
new_index = pd.MultiIndex.from_tuples((x[0] for x in index), names=['Year'])

print(new_index)

Output:

Index(['2021', '2021'], dtype='object', name='Year')

This approach uses a generator expression to create tuples only with the first element (the ‘Year’ in our case) from each original index tuple, and then generates a new index from these.

Bonus One-Liner Method 5: Chain reset_index() and set_index()

For a quick, fluent one-liner, we can chain the reset_index() method to drop the unwanted levels and immediately use set_index() to establish the remaining level as the new index.

Here’s an example:

import pandas as pd

# Create a multi-level index DataFrame
df = pd.DataFrame(index=pd.MultiIndex.from_tuples([('2021', 'Jan', '01'), ('2021', 'Feb', '02')], names=['Year', 'Month', 'Day']))

# One-liner to remove specific levels and create a new index
new_index = df.reset_index(level=['Month', 'Day'], drop=True).index

print(new_index)

Output:

Index(['2021', '2021'], dtype='object', name='Year')

This single line of code efficiently drops the ‘Month’ and ‘Day’ levels from the DataFrame’s index and takes the remaining index, ‘Year’, as the final index.

Summary/Discussion

  • Method 1: droplevel(). Efficient and pandas-native. Best used when dealing with simple multi-index structures. May not be suitable for more complex level manipulations.
  • Method 2: List Comprehension and get_level_values(). Good for specific manipulations and direct level operations. Can be less efficient for large datasets due to the construction of intermediate lists.
  • Method 3: reset_index(). Very versatile as it turns indices into columns, which can then be modified as regular DataFrame operations. Might be overkill for simple level drops.
  • Method 4: Re-indexing with a Sliced Tuple. Pythonic method that might appeal to those comfortable with list comprehensions and generator expressions, but can be less readable for others.
  • Bonus One-Liner. Quick and elegant. Great for those who favor concise code. However, it could harm readability and debugging ease for some developers.