pandas.core.window.Rolling.kurt

Calculate unbiased rolling kurtosis.

This function uses Fisher’s definition of kurtosis without bias.

**kwargs Under Review. :return: Series or DataFrame

Returned object type is determined by the caller of the rolling calculation.

Examples

Calculate unbiased rolling kurtosis.

import pandas as pd
from numba import njit


@njit
def series_rolling_kurt():
    series = pd.Series([4, 3, 5, 2, 6])  # Series of 4, 3, 5, 2, 6
    out_series = series.rolling(4).kurt()

    return out_series  # Expect series of NaN, NaN, NaN, -1.2, -3.3


print(series_rolling_kurt())

$ python ./series/rolling/series_rolling_kurt.py
0    NaN
1    NaN
2    NaN
3   -1.2
4   -3.3
dtype: float64

Calculate unbiased rolling kurtosis.

import pandas as pd
from numba import njit


@njit
def df_rolling_kurt():
    df = pd.DataFrame({'A': [4, 3, 5, 2, 6], 'B': [-4, -3, -5, -2, -6]})
    out_df = df.rolling(4).kurt()

    # Expect DataFrame of
    # {'A': [NaN, NaN, NaN, -1.2, -3.3], 'B': [NaN, NaN, NaN, -1.2, -3.3]}
    return out_df


print(df_rolling_kurt())

$ python ./dataframe/rolling/dataframe_rolling_kurt.py
     A    B
0  NaN  NaN
1  NaN  NaN
2  NaN  NaN
3 -1.2 -1.2
4 -3.3 -3.3

See also

Series.rolling

Calling object with a Series.

DataFrame.rolling

Calling object with a DataFrame.

Series.kurt

Similar method for Series.

DataFrame.kurt

Similar method for DataFrame.