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.
Output :
👁 Image
Now we will use
Series.kurtosis() function to find the kurtosis of the underlying data in the given series object.
Output :
👁 Image
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.
Output :
👁 Image
Now we will use
Series.kurtosis() function to find the kurtosis of the underlying data in the given series object.
Output :
👁 Image
As we can see in the output, the
Series.kurtosis() function has returned the kurtosis of the given series object.
