pandas.DataFrame.kurtosis#

DataFrame.kurtosis(*, axis=0, skipna=True, numeric_only=False, **kwargs)[source]#

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters:

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

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:

Series or scalar: Unbiased kurtosis over requested axis.

See also

Dataframe.kurtosis: Returns unbiased kurtosis over requested axis.

Examples

>>> s = pd.Series([1, 2, 2, 3], index=["cat", "dog", "dog", "mouse"])
>>> s
cat    1
dog    2
dog    2
mouse  3
dtype: int64
>>> s.kurt()
1.5

With a DataFrame

>>> df = pd.DataFrame(
...     {"a": [1, 2, 2, 3], "b": [3, 4, 4, 4]},
...     index=["cat", "dog", "dog", "mouse"],
... )
>>> df
       a   b
  cat  1   3
  dog  2   4
  dog  2   4
mouse  3   4
>>> df.kurt()
a   1.5
b   4.0
dtype: float64

With axis=None

>>> df.kurt(axis=None).round(6)
-0.988693

Using axis=1

>>> df = pd.DataFrame(
...     {"a": [1, 2], "b": [3, 4], "c": [3, 4], "d": [1, 2]},
...     index=["cat", "dog"],
... )
>>> df.kurt(axis=1)
cat   -6.0
dog   -6.0
dtype: float64