• seaborn.distplot已过时。请使用seaborn.kdeplot，它是轴级绘图。否则，请使用seaborn.displot进行图形级绘图。
  *在python 3.8.11、pandas 1.3.2、matplotlib 3.4.2、seaborn 0.11.2中进行测试

导入和测试数据

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(365)
rows = 10000
data = {'a': np.random.normal(5, 5, rows),
        'b': np.random.normal(20, 5, rows),
        'c': np.random.normal(35, 5, rows),
        'd': np.random.normal(500, 50, rows),
        'e': np.random.normal(6500, 500, rows),
        'class': np.random.choice([0, 1], size=(rows), p=[0.25, 0.75])}
df = pd.DataFrame(data)

# display(df.head(3))
          a          b          c           d            e  class
0  5.839606  20.807027  34.798230  509.328065  6003.228497      0
1  7.617526  21.691519  40.519995  445.724478  7204.039621      0
2  9.086878  27.193222  32.776264  498.254687  6810.960924      1

使用seaborn.kdeplot绘图

fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15, 7), sharex=False, sharey=False)
axes = axes.ravel()  # array to 1D
cols = df.columns[:-1]  # create a list of dataframe columns to use

for col, ax in zip(cols, axes):
    data = df[[col, 'class']]  # select the data
    sns.kdeplot(data=data, x=col, hue='class', shade=True, ax=ax)
    ax.set(title=f'Distribution of Column: {col}', xlabel=None)
    
fig.delaxes(axes[5])  # delete the empty subplot
fig.tight_layout()
plt.show()

使用seaborn.displot绘图

# convert the dataframe from wide to long
dfm = df.melt(id_vars='class', var_name='Distribution')

# display(dfm.head(3))
   class Distribution     value
0      0            a  5.839606
1      0            a  7.617526
2      1            a  9.086878

# plot
sns.displot(kind='kde', data=dfm, col='Distribution', col_wrap=3, x='value', hue='class', fill=True, facet_kws={'sharey': False, 'sharex': False})