Here’s how to calculate kurtosis in Pandas.
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How to calculate kurtosis in Pandas
To calculate a Kurtosis in Pandas just use a Kurt method which Pandas is offering to you.
import pandas as pd
my_df = pd.DataFrame({"my_column1": ['9', '2', '3', '5'],
"my_column2": ['3', '7', '6', '4'],
"my_column3": ['4', '8', '8', '8']})
print(f'The kurtosis of columns:\n{my_df.kurtosis()}')
The kurtosis of columns: my_column1 0.757656 my_column2 -3.300000 my_column3 4.000000 dtype: float64
The kurtosis method calculates the excess kurtosis, which is the kurtosis of a dataset minus the kurtosis of a normal distribution. A positive value of kurtosis indicates that the data has heavier tails than a normal distribution, while a negative value indicates lighter tails.
For more details see the documentation of kurt function.