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.kurt() function return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
Syntax: DataFrame.kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
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 : kurt : Series or DataFrame (if level specified)
Example #1: Use kurt() function to find the kurtosis 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.kurt() function to find the kurtosis.
# find the kurtosis over the index axis df.kurt(axis = 0) |
Output :
Example #2: Use kurt() function to find the kurtosis of dataframe which is having some Na values in it. Find kurtosis over the index axis.
# 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 kurtosis # skip the Na values when finding the kurtosis df.kurt(axis = 0, skipna = True) |
Output :
