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Python | Pandas Series.mad() to calculate Mean Absolute Deviation of a Series
  • Difficulty Level : Hard
  • Last Updated : 30 Sep, 2019

Pandas provide a method to make Calculation of MAD (Mean Absolute Deviation) very easy. MAD is defined as average distance between each value and mean.

The formula used to calculate MAD is:


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:
In this example, a Series is created from a Python List using Pandas .Series() method. The .mad() method is called on series with all default parameters.




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

Output:

5.876543209876543

Explanation:

Calculating Mean of series 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)

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