python - 绘制多个 seaborn 分布图

Question

我正在尝试创建按列分组的数据框的 distplot

data_plot = creditcard_df.copy()
amount = data_plot['Amount']
data_plot.drop(labels=['Amount'], axis=1, inplace = True)
data_plot.insert(0, 'Amount', amount)

# Plot the distributions of the features
columns = data_plot.iloc[:,0:30].columns
plt.figure(figsize=(12,30*4))
grids = gridspec.GridSpec(30, 1)
for grid, index in enumerate(data_plot[columns]):
    ax = plt.subplot(grids[grid])
    sns.distplot(data_plot[index][data_plot.Class == 1], hist=False, kde_kws={"shade": True}, bins=20)
    sns.distplot(data_plot[index][data_plot.Class == 0], hist=False, kde_kws={"shade": True}, bins=20)
    ax.set_xlabel("")
    ax.set_title("Distribution of Column: "  + str(index))
    plt.show()

我尝试对 y 轴使用对数刻度，更改 gridspec 和 figsize；但所有这些都只会弄乱分布。有没有办法使地 block 统一？

Answer 1

• seaborn.distplot已弃用。使用seaborn.kdeplot ，这是一个轴级图。否则使用 seaborn.displot用于图形级别的情节。
• 在 python 3.8.11、pandas 1.3.2、matplotlib 3.4.2、seaborn 0.11 中测试。 2

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})