How to handle highly skewed data according to ChatGPT :)

 

import numpy as np

# assume sm_df is your pandas DataFrame

sm_df["A"] = sm_df["A"] - sm_df["A"].min() + 1

from scipy.stats import boxcox

# assume sm_df is your pandas DataFrame

sm_df["A"], _ = boxcox(sm_df["A"])

# assume sm_df is your pandas DataFrame

sm_df["A"] = (sm_df["A"] - sm_df["A"].mean()) / sm_df["A"].std()

import numpy as np

# assume sm_df is your pandas DataFrame

sm_df["A"] = sm_df["A"] - sm_df["A"].min() + 1

sm_df["A"] = np.log(sm_df["A"])

Some other information:

The choice of transformation depends on the characteristics of your data and the requirements of your analysis. For example, if your data has a strong right-skewness, a log transformation might be a better option than a Box-Cox or Yeo-Johnson transformation. Conversely, if your data has a strong left-skewness or a significant number of negative values, the Yeo-Johnson transformation might be more appropriate.