Python | Pandas dataframe.mad()
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.mad()
function return the mean absolute deviation of the values for the requested axis. The mean absolute deviation of a dataset is the average distance between each data point and the mean. It gives us an idea about the variability in a dataset.
Syntax: DataFrame.mad(axis=None, skipna=None, level=None)
Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Returns : mad : Series or DataFrame (if level specified)
Example #1: Use mad()
function to find the mean absolute deviation of the values over the index axis.
import pandas as pd
df = pd.DataFrame({ "A" :[ 12 , 4 , 5 , 44 , 1 ],
"B" :[ 5 , 2 , 54 , 3 , 2 ],
"C" :[ 20 , 16 , 7 , 3 , 8 ],
"D" :[ 14 , 3 , 17 , 2 , 6 ]})
df
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Let’s use the dataframe.mad()
function to find the mean absolute deviation.
Output :
Example #2: Use mad()
function to find the mean absolute deviation of values over the column axis which is having some Na
values in it.
import pandas as pd
df = pd.DataFrame({ "A" :[ 12 , 4 , 5 , None , 1 ],
"B" :[ 7 , 2 , 54 , 3 , None ],
"C" :[ 20 , 16 , 11 , 3 , 8 ],
"D" :[ 14 , 3 , None , 2 , 6 ]})
df.mad(axis = 1 , skipna = True )
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Output :
Last Updated :
19 Nov, 2018
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