# 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


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.