Python | Pandas Series.kurtosis() (original) (raw)

Last Updated : 11 Feb, 2019

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas **Series.kurtosis()** function returns an unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). The final result is normalized by N-1.

Syntax: Series.kurtosis(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)Parameter : axis : Axis for the function to be applied on.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 scalar.numeric_only : Include only float, int, boolean columns.**kwargs : Additional keyword arguments to be passed to the function.Returns : kurt : scalar or Series (if level specified)

Example #1: Use Series.kurtosis() function to find the kurtosis of the underlying data in the given series object.

Python3 `

importing pandas as pd

import pandas as pd

Creating the Series

sr = pd.Series([10, 25, 3, 25, 24, 6])

Create the Index

index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']

set the index

sr.index = index_

Print the series

print(sr)

`

Output : Now we will use Series.kurtosis() function to find the kurtosis of the underlying data in the given series object.

Python3 `

return the kurtosis

result = sr.kurtosis()

Print the result

print(result)

`

Output : As we can see in the output, the Series.kurtosis() function has returned the kurtosis of the given series object.Example #2 : Use Series.kurtosis() function to find the kurtosis of the underlying data in the given series object. The given series object contains some missing values.

Python3 `

importing pandas as pd

import pandas as pd

Creating the Series

sr = pd.Series([19.5, 16.8, None, 22.78, 16.8, 20.124, None, 64, 89])

Print the series

print(sr)

`

Output : Now we will use Series.kurtosis() function to find the kurtosis of the underlying data in the given series object.

Python3 `

return the kurtosis

skip the missing values

result = sr.kurtosis(skipna = True)

Print the result

print(result)

`

Output : As we can see in the output, the Series.kurtosis() function has returned the kurtosis of the given series object.