pandas.Series.kurtosis#

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

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisherâ€™s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters:
axis{index (0)}

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.

New 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. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns:
scalar or scalar

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
```