Pandas DataFrame filter() Method

Preparation

Before any data manipulation can occur, one (1) new library will require installation:

• The Pandas library enables access to/from a DataFrame.

To install this library, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

• How to Install Pandas on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd

💡 Note: To follow along with the examples below, click here to download the finxters.csv file of auto-generated dummy user data. Move this file to the current working directory.

DataFrame filter()

The filter() method returns a subset of a DataFrame/Series rows/columns based on index label(s). This method does not filter a DataFrame/Series on the contents. The filter applies to the specific label(s) of the selected index. The return value is a subset of the callable.

The syntax for this method is as follows:

DataFrame.filter(items=None, like=None, regex=None, axis=None)

For this method, various scenarios below highlight its capabilities.

This example uses the items parameter to filter the DataFrame and return the result.

Code – Example 1

df_fxs = pd.read_csv('finxters.csv')
result = df_fxs.filter(items=['Username', 'Rank'])
print(result.head())

• Line [1] reads in the comma-separated CSV file and saves it to df_fxs.
• Line [2] filters df_fxs to include only the columns matching the item labels (Username and Rank). These columns (and associated values) save to result.
• Line [3] outputs the first five (5) rows (head()) to the terminal.

Output

This example uses the like parameter to filter the DataFrame and return the result.

Code – Example 2

df_fxs = pd.read_csv('finxters.csv')
result = df_fxs.filter(like='FID', axis=1)
print(result.head())

• Line [1] reads in the comma-separated CSV file and saves it to df_fxs.
• Line [2] filters df_fxs to include only the columns matching the like label ('FID'). This column (and associated values) save to result.
• Line [3] outputs the first five (5) rows (head()) to the terminal.

Output

This example uses the regex parameter to filter the DataFrame and return the result.

Code – Example 3

df_fxs = pd.read_csv('finxters.csv')
result = df_fxs.filter(regex='e$', axis=1)
print(result.head())

• Line [1] reads in the comma-separated CSV file and saves it to df_fxs.
• Line [2] filters df_fxs to include only the columns matching the regex label (First_Name, Last_Name and Username). These columns (and associated values) save to result.
• Line [3] outputs the first five (5) rows (head()) to the terminal.

Output

Note: Each column name in the output ends with the letter e.

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.