pandas.core.groupby.DataFrameGroupBy.kurt#

DataFrameGroupBy.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.

**kwargs

Additional keyword arguments to be passed to the function.

Returns:

DataFrame

Unbiased kurtosis within groups.

See also

DataFrame.kurt

Return unbiased kurtosis over requested axis.

Examples

>>> arrays = [
...     [
...         "falcon",
...         "parrot",
...         "cockatoo",
...         "kiwi",
...         "eagle",
...         "lion",
...         "monkey",
...         "rabbit",
...         "dog",
...         "wolf",
...     ],
...     [
...         "bird",
...         "bird",
...         "bird",
...         "bird",
...         "bird",
...         "mammal",
...         "mammal",
...         "mammal",
...         "mammal",
...         "mammal",
...     ],
... ]
>>> index = pd.MultiIndex.from_arrays(arrays, names=("name", "class"))
>>> df = pd.DataFrame(
...     {
...         "max_speed": [
...             389.0,
...             24.0,
...             70.0,
...             np.nan,
...             350.0,
...             80.5,
...             21.5,
...             15.0,
...             40.0,
...             50.0,
...         ]
...     },
...     index=index,
... )
>>> df
                 max_speed
name     class
falcon   bird        389.0
parrot   bird         24.0
cockatoo bird         70.0
kiwi     bird          NaN
eagle    bird        350.0
lion     mammal       80.5
monkey   mammal       21.5
rabbit   mammal       15.0
dog      mammal       40.0
wolf     mammal       50.0
>>> gb = df.groupby(["class"])
>>> gb.kurt()
        max_speed
class
bird    -5.493277
mammal   0.204125
>>> gb.kurt(skipna=False)
        max_speed
class
bird          NaN
mammal   0.204125