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## Preparation

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

• 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 ```

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)`

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]})

• 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.