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

absolute | iat | mean | NumPy | Python functions

Michael Zippo 18.07.2021

Formula used to calculate MAD:

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)