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# Python | Pandas Series.mad () to calculate the mean absolute deviation of a series

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

Parameters:
axis: 0 or ’index’ for row wise operation and 1 or ’columns’ for column wise operation.
skipna: Includes NaN values ​​too if False, Result will also be NaN even if a single Null value is included.
level: Defines level name or number in case of multilevel series.

Return Type: Float value

Example # 1:
B This example creates a Series from a Python list using the Pandas .Series () method. The .mad () method is called for a series with all default parameters.

 ` # pandas module import ` ` import ` ` pandas as pd `   ` # numpy module import ` ` import ` ` numpy as np `   ` # create a list ` ` list ` ` = ` ` [` ` 5 ` `, ` ` 12 ` `, ` ` 1 ` `, ` ` 0 ` `, ` ` 4 ` `, ` ` 22 ` `, ` ` 15 ` `, ` ` 3 ` `, ` ` 9 ` `] `   ` # create series ` ` series ` ` = ` ` pd.Series (` ` list ` `) `   ` # method call .mad () ` ` result ` ` = ` ` series.mad () `   ` # display ` ` result `

Output:

`5.876543209876543`

Explanation :

Calculating Mean of serie s mean = (5 + 12 + 1 + 0 + 4 + 22 + 15 + 3 + 9) / 9 = 7.8888

MAD = | (5-7.88) + (12-7.88) + (1-7.88) + (0-7.88) + (4-7.88) + (22-7.88) + (15-7.88) + (3-7.88) + (9 -7.88)) | / 9.00

MAD = (2.88 + 4.12 + 6.88 + 7.88 + 3.88 + 14.12 + 7.12 + 4.88 + 1.12) / 9.00

MAD = 5.8755 (More accurately = 5.876543209876543)