pandas.core.groupby.SeriesGroupBy.kurt#

SeriesGroupBy.kurt(skipna=True, numeric_only=False, **kwargs)[source] #

Return unbiased kurtosis within groups.

Parameters:

skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series: Unbiased kurtosis within groups.

See also

Series.kurt: Return unbiased kurtosis over requested axis.

Examples

>>> ser = pd.Series(
...  [390.0, 350.0, 357.0, 333.0, np.nan, 22.0, 20.0, 30.0, 40.0, 41.0],
...  index=[
...  "Falcon",
...  "Falcon",
...  "Falcon",
...  "Falcon",
...  "Falcon",
...  "Parrot",
...  "Parrot",
...  "Parrot",
...  "Parrot",
...  "Parrot",
...  ],
...  name="Max Speed",
... )
>>> ser
Falcon 390.0
Falcon 350.0
Falcon 357.0
Falcon 333.0
Falcon NaN
Parrot 22.0
Parrot 20.0
Parrot 30.0
Parrot 40.0
Parrot 41.0
Name: Max Speed, dtype: float64
>>> ser.groupby(level=0).kurt()
Falcon 1.622109
Parrot -2.878714
Name: Max Speed, dtype: float64
>>> ser.groupby(level=0).kurt(skipna=False)
Falcon NaN
Parrot -2.878714
Name: Max Speed, dtype: float64

pandas.core.groupby.SeriesGroupBy.kurt#

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