Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas` dataframe.mad()` function return the mean absolute deviation of the values for the requested axis. The mean absolute deviation of a dataset is the average distance between each data point and the mean. It gives us an idea about the variability in a dataset.

Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns : mad : Series or DataFrame (if level specified)

Example #1: Use `mad()` function to find the mean absolute deviation of the values over the index axis.

 `# importing pandas as pd ` `import` `pandas as pd ` ` `  `# Creating the dataframe  ` `df ``=` `pd.DataFrame({``"A"``:[``12``, ``4``, ``5``, ``44``, ``1``], ` `                   ``"B"``:[``5``, ``2``, ``54``, ``3``, ``2``],  ` `                   ``"C"``:[``20``, ``16``, ``7``, ``3``, ``8``], ` `                   ``"D"``:[``14``, ``3``, ``17``, ``2``, ``6``]}) ` ` `  `# Print the dataframe ` `df `

Let’s use the `dataframe.mad()` function to find the mean absolute deviation.

 `# find the mean absolute deviation  ` `# over the index axis ` `df.mad(axis ``=` `0``) `

Output :

Example #2: Use `mad()` function to find the mean absolute deviation of values over the column axis which is having some `Na` values in it.

 `# importing pandas as pd ` `import` `pandas as pd ` ` `  `# Creating the dataframe  ` `df ``=` `pd.DataFrame({``"A"``:[``12``, ``4``, ``5``, ``None``, ``1``], ` `                   ``"B"``:[``7``, ``2``, ``54``, ``3``, ``None``], ` `                   ``"C"``:[``20``, ``16``, ``11``, ``3``, ``8``],  ` `                   ``"D"``:[``14``, ``3``, ``None``, ``2``, ``6``]}) ` ` `  `# To find the mean absolute deviation ` `# skip the Na values when finding the mad value ` `df.mad(axis ``=` `1``, skipna ``=` `True``) `

Output :

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