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.
# 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)
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Output :
Now we will use Series.kurtosis()
function to find the kurtosis of the underlying data in the given series object.
# return the kurtosis result = sr.kurtosis()
# Print the result print (result)
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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.
# 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)
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Output :
Now we will use Series.kurtosis()
function to find the kurtosis of the underlying data in the given series object.
# return the kurtosis # skip the missing values result = sr.kurtosis(skipna = True )
# Print the result print (result)
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Output :
As we can see in the output, the Series.kurtosis()
function has returned the kurtosis of the given series object.