Pandas DataFrame Droplevel Method

Introduction

In this lab, we will learn how to use the droplevel() method in the Pandas library for Python. The droplevel() method is used to remove one or more levels from the index or column of a DataFrame. We will go through examples to understand how to use this method.

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Skills Graph

Import the necessary libraries

To start, we need to import the pandas library, as it provides the functionality to work with data in a tabular format.

import pandas as pd

Create a DataFrame

Next, let's create a DataFrame to work with. We will use the pd.DataFrame() function to create a DataFrame with some sample data. We will also set a multi-level index using the set_index() method.

df = pd.DataFrame([[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12]]).set_index([0, 1]).rename_axis(['a', 'b'])
df.columns = pd.MultiIndex.from_tuples([('c', 'e'), ('d', 'f')], names=['level_1', 'level_2'])
print(df)

Drop a level from the index

We can use the droplevel() method to drop a level from the index of the DataFrame. To do this, we need to specify the level we want to drop as an argument to the droplevel() method. The method will return a new DataFrame with the specified level dropped.

dropped_level_df = df.droplevel('a')
print(dropped_level_df)

Drop a level from the columns

Similarly, we can drop a level from the columns of the DataFrame using the droplevel() method. We need to specify the level we want to drop and the axis parameter as 1 to indicate that we are dropping a level from the columns.

dropped_level_df = df.droplevel('level_2', axis=1)
print(dropped_level_df)

Drop multiple levels from the index

In some cases, we may want to drop multiple levels from the index. To do this, we can pass a list of levels to the droplevel() method. The method will drop all the specified levels from the index and return a new DataFrame.

dropped_levels_df = df.droplevel(['a', 'b'])
print(dropped_levels_df)

Summary

In this lab, we learned how to use the droplevel() method in the Pandas library for Python. We learned how to drop a level from the index and columns of a DataFrame using this method. We also learned how to drop multiple levels from the index. This method is useful when we want to manipulate the structure of a DataFrame by removing unnecessary levels from its index or columns.