Python | Pandas Series.mad() to calculate Mean Absolute Deviation of a Series
Last Updated :
30 Sep, 2019
Improve
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:
Python3
Output:
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 valueExample #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
5.876543209876543Explanation:
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)
