Pandas DataFrame mad() Method – Be on the Right Side of Change

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

The Pandas library enables access to/from a DataFrame.
The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, 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.

$ pip install numpy

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 libraries.

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
import numpy as np

DataFrame mad()

The mad() method (Mean Absolute Deviation) is the average distance of all DataFrame elements from the mean.

To fully understand MAD from a mathematical point of view, feel free to watch this short tutorial:

The syntax for this method is as follows:

DataFrame.mad(axis=None, skipna=None, level=None)

Parameter	Description
`axis`	If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row.
`skipna`	If this parameter is `True`, any `NaN`/NULL value(s) ignored. If `False`, all value(s) included: valid or empty. If no value, then `None` is assumed.
`level`	Set the appropriate parameter if the DataFrame/Series is multi-level. If no value, then `None` is assumed.

This example retrieves the MAD of four (4) Hockey Teams.

df_teams = pd.DataFrame({'Bruins':   [4, 5, 9],
                         'Oilers':   [3, 6, 10],
                         'Leafs':    [2, 7, 11],
                         'Flames':   [1, 8, 12]})

result = df_teams.mad(axis=0).apply(lambda x:round(x,3))
print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
Line [2] uses the mad() method with the axis parameter set to columns to calculate MAD from the DataFrame. The lambda function formats the output to three (3) decimal places. This output saves to the result variable.
Line [3] outputs the result to the terminal.

Output

Bruins	2.000
Oilers	2.444
Leafs	3.111
Flames	4.000
dtype:	float64

More Pandas DataFrame Methods

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

👈 df.last()

df.max() 👉

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