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

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