text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')

cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]

plt.figure(figsize=(20, 10))
ax = sns.violinplot(data=train_data[cont_features], inner=None, palette='viridis')
plt.title('Continuous features distribution')