您可以使用多个子图来实现这一点,这些子图可以使用plt.subplots 轻松设置(查看更多subplot examples)。
这允许您以适当的比例显示您的分布,并且不会“浪费”显示空间。大多数(全部?)seaborn 的绘图函数都接受 ax= 参数,因此您可以设置要渲染绘图的轴。轴之间也有明显的分隔。
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# generate some random distribution data
n = 800 # samples
prod = 95 + 5 * np.random.beta(0.6, 0.5, size=n); # a bimodal distribution
forecast = prod + 3*np.random.randn(n) # forecast is noisy estimate around the "true" production
diff = prod-forecast # should be with mu 0 sigma 3
df = pd.DataFrame(np.array([prod, forecast, diff]).T, columns=['SoundProduction','SoundForecast','diff']);
# set up two subplots, with one wider than the other
fig, ax = plt.subplots(1,2, num=1, gridspec_kw={'width_ratios':[2,1]})
# plot violin distribution estimates separately so the y-scaling makes sense in each group
sns.violinplot(data=df[['SoundProduction','SoundForecast']], ax=ax[0])
sns.violinplot(data=df[['diff']], ax=ax[1])