9611.py

import pandas as pd
from sklearn.preprocessing import QuantileTransformer

qt = QuantileTransformer().fit(crazy_distributions)

crazy_feature_names = ["f18", "f31", "f61"]
crazy_distributions = pd.DataFrame(qt.transform(crazy_distributions), columns=crazy_feature_names)

fig, axes = plt.subplots(1, 3, figsize=(20, 6))

for ax, f_name in zip(axes.flatten(), crazy_feature_names):
    sns.kdeplot(crazy_distributions[f_name], ax=ax, color="#E50914")