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# Python | Pandas dataframe.mad ()

The Pandas function `dataframe.mad() ` returns the mean absolute deviation of the values ​​for the requested axis. Average absolute deviation of the dataset — this is the average distance between each data point and the mean. This gives us an idea of ​​the variability in the dataset.

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

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 the ` mad () ` function to find the mean absolute deviation of the values ​​along the index axis.

 ` # import pandas as pd ` ` import ` ` pandas as pd `   ` # Create data frame ` ` 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 data frame ` ` df `

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

`   # find the average absolute deviation # along the index axis df.mad (axis = 0 ) `

Output:

Example # 2: Use the ` mad () ` function to find the mean absolute deviation of values ​​along the axis of a column that has some ` Na ` values.

 ` # import pandas as pd ` ` import ` ` pandas as pd ` ` `  ` # Create data frame ` ` df ` ` = ` ` pd.DataFrame ({` `" A "` `: [` ` 12 ` `, ` ` 4 ` `, ` ` 5 ` `, ` ` None ` `, ` ` 1 ` `], ` ` "B" ` `: [` ` 7 ` `, ` ` 2 , 54 , 3 , None ], `` "C" : [ 20 , 16 , 11 , 3 , 8 ],  "D" < / code> : [ 14 , 3 , None , 2 , 6 ]})   # Find the mean absolute deviation # skip Na values ​​when finding insane value df.mad (axis = 1 , skipna = True ) `

Output: