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)
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
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.
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)
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.