小提琴图基础

小提琴图类似于直方图和箱线图，因为它们显示了样本概率分布的抽象表示。与显示落入箱子中的数据点计数或顺序统计量不同，小提琴图使用核密度估计（KDE）来计算样本的经验分布。该计算由几个参数控制。此示例演示了如何修改KDE评估点的数量（points）以及如何修改KDE的带宽（bw_method）。

关于小提琴图和KDE的更多信息，scikit-learn文档有一个很棒的部分：https://scikit-learn.org/stable/modules/density.html

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)


# fake data
fs = 10  # fontsize
pos = [1, 2, 4, 5, 7, 8]
data = [np.random.normal(0, std, size=100) for std in pos]

fig, axs = plt.subplots(nrows=2, ncols=6, figsize=(10, 4))

axs[0, 0].violinplot(data, pos, points=20, widths=0.3,
                     showmeans=True, showextrema=True, showmedians=True)
axs[0, 0].set_title('Custom violin 1', fontsize=fs)

axs[0, 1].violinplot(data, pos, points=40, widths=0.5,
                     showmeans=True, showextrema=True, showmedians=True,
                     bw_method='silverman')
axs[0, 1].set_title('Custom violin 2', fontsize=fs)

axs[0, 2].violinplot(data, pos, points=60, widths=0.7, showmeans=True,
                     showextrema=True, showmedians=True, bw_method=0.5)
axs[0, 2].set_title('Custom violin 3', fontsize=fs)

axs[0, 3].violinplot(data, pos, points=60, widths=0.7, showmeans=True,
                     showextrema=True, showmedians=True, bw_method=0.5,
                     quantiles=[[0.1], [], [], [0.175, 0.954], [0.75], [0.25]])
axs[0, 3].set_title('Custom violin 4', fontsize=fs)

axs[0, 4].violinplot(data[-1:], pos[-1:], points=60, widths=0.7,
                     showmeans=True, showextrema=True, showmedians=True,
                     quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
axs[0, 4].set_title('Custom violin 5', fontsize=fs)

axs[0, 5].violinplot(data[-1:], pos[-1:], points=60, widths=0.7,
                     showmeans=True, showextrema=True, showmedians=True,
                     quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='low')

axs[0, 5].violinplot(data[-1:], pos[-1:], points=60, widths=0.7,
                     showmeans=True, showextrema=True, showmedians=True,
                     quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='high')
axs[0, 5].set_title('Custom violin 6', fontsize=fs)

axs[1, 0].violinplot(data, pos, points=80, orientation='horizontal', widths=0.7,
                     showmeans=True, showextrema=True, showmedians=True)
axs[1, 0].set_title('Custom violin 7', fontsize=fs)

axs[1, 1].violinplot(data, pos, points=100, orientation='horizontal', widths=0.9,
                     showmeans=True, showextrema=True, showmedians=True,
                     bw_method='silverman')
axs[1, 1].set_title('Custom violin 8', fontsize=fs)

axs[1, 2].violinplot(data, pos, points=200, orientation='horizontal', widths=1.1,
                     showmeans=True, showextrema=True, showmedians=True,
                     bw_method=0.5)
axs[1, 2].set_title('Custom violin 9', fontsize=fs)

axs[1, 3].violinplot(data, pos, points=200, orientation='horizontal', widths=1.1,
                     showmeans=True, showextrema=True, showmedians=True,
                     quantiles=[[0.1], [], [], [0.175, 0.954], [0.75], [0.25]],
                     bw_method=0.5)
axs[1, 3].set_title('Custom violin 10', fontsize=fs)

axs[1, 4].violinplot(data[-1:], pos[-1:], points=200, orientation='horizontal',
                     widths=1.1, showmeans=True, showextrema=True, showmedians=True,
                     quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
axs[1, 4].set_title('Custom violin 11', fontsize=fs)

axs[1, 5].violinplot(data[-1:], pos[-1:], points=200, orientation='horizontal',
                     widths=1.1, showmeans=True, showextrema=True, showmedians=True,
                     quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='low')

axs[1, 5].violinplot(data[-1:], pos[-1:], points=200, orientation='horizontal',
                     widths=1.1, showmeans=True, showextrema=True, showmedians=True,
                     quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='high')
axs[1, 5].set_title('Custom violin 12', fontsize=fs)


for ax in axs.flat:
    ax.set_yticklabels([])

fig.suptitle("Violin Plotting Examples")
fig.subplots_adjust(hspace=0.4)
plt.show()

参考文献

以下函数、方法、类和模块的使用在本示例中展示：

• matplotlib.axes.Axes.violinplot / matplotlib.pyplot.violinplot