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.
# importing pandas as pd import pandas as pd # Creating the dataframe 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]}) # Print the dataframe df |
Let’s use the dataframe.mad() function to find the mean absolute deviation.
# find the mean absolute deviation # over the index axis df.mad(axis = 0) |
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.
# importing pandas as pd import pandas as pd # Creating the dataframe 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]}) # To find the mean absolute deviation # skip the Na values when finding the mad value df.mad(axis = 1, skipna = True) |
Output :
