Pandas DataFrame Drop Method

Introduction

In this lab, we will learn how to use the drop() method in the Python Pandas library to remove specific rows or columns from a DataFrame. The drop() method allows us to drop labels by specifying index or column names and corresponding axis. We can also drop labels on different levels in a multi-index DataFrame.

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Skills Graph

Import the pandas library

First, we need to import the pandas library to use the DataFrame and drop() method. We can import pandas using the following code:

import pandas as pd

Create a DataFrame

Next, let's create a DataFrame to work with. We'll create a DataFrame with some sample data using the following code:

df = pd.DataFrame([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], columns=['a', 'b', 'c', 'd'])

Remove specific rows using the drop() method

To remove specific rows from the DataFrame, we can use the drop() method with the index parameter. The index parameter specifies the label or labels of the rows to be dropped. We can also specify the axis parameter as 0 or 'index' to indicate that we want to drop rows. Here's an example:

dropped_df = df.drop([1])

Remove specific columns using the drop() method

To remove specific columns from the DataFrame, we can use the drop() method with the columns parameter. The columns parameter specifies the label or labels of the columns to be dropped. We need to set the axis parameter as 1 or 'columns' to indicate that we want to drop columns. Here's an example:

dropped_df = df.drop(columns=['b'])

Use inplace=True to modify the DataFrame in-place

By default, the drop() method returns a new DataFrame with the dropped rows or columns. However, if we want to modify the original DataFrame in-place, we can set the inplace parameter as True. Here's an example:

df.drop([1], inplace=True)

Summary

In this lab, we learned how to use the drop() method in the Python Pandas library to remove specific rows or columns from a DataFrame. We can specify the labels to be dropped using the index or columns parameter, and use the axis parameter to indicate the axis along which the labels should be dropped. Remember that by default, the drop() method returns a new DataFrame with the dropped labels, but we can modify the original DataFrame in-place by setting the inplace parameter as True. The drop() method is a powerful tool when working with DataFrames in pandas.

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