Pandas DataFrame filter() Method – Be on the Right Side of Change (original) (raw)

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.

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)

Parameter	Description
items	A filter list of columns to include in the result.
like	A filter string of columns (ex: like='FID') to include in the result.
regex	A regex string to filter columns (ex: regex='e$') to include in the result.
axis	If zero (0) or index is selected, apply to each column. Default 0.If one (1) apply to each row..

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

| | Username | Rank | | | ---------- | ---------- | ------------------- | | 0 | wildone92 | Authority | | 1 | AmyP | Beginner | | 2 | 1998_pete | Basic Knowledge | | 3 | TheCoder | Experienced Learner | | 4 | AliceM | Authority |

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

| | FID | | | ----- | -------- | | 0 | 30022145 | | 1 | 30022192 | | 2 | 30022331 | | 3 | 30022345 | | 4 | 30022359 |

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

| | First_Name | Last_Name | Username | | | ------------- | ---------- | --------- | ---------- | | 0 | Steve | Hamilton | wildone92 | | 1 | Amy | Pullister | AmyP | | 2 | Peter | Dunn | 1998_pete | | 3 | Marcus | Williams | TheCoder | | 4 | Alice | Miller | AliceM |

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.