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小提琴图类似于柱状图和方框图，它们显示了样本概率分布的抽象表示。小提琴图并没有显示落入箱中的数据点计数或顺序统计，而是使用核密度估计（kde）来计算样本的经验分布。这个计算由几个参数控制。这个例子演示了如何修改计算kde的点数。 (points ）以及如何修改kde的带宽 (bw_method ）
有关小提琴绘图和kde的更多信息，Scikit学习文档有一个很好的部分：http://scikit-learn.org/stable/modules/density.html
import numpy as np
import matplotlib.pyplot as plt
# 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, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6))
axes[0, 0].violinplot(data, pos, points=20, widths=0.3,
showmeans=True, showextrema=True, showmedians=True)
axes[0, 0].set_title('Custom violinplot 1', fontsize=fs)
axes[0, 1].violinplot(data, pos, points=40, widths=0.5,
showmeans=True, showextrema=True, showmedians=True,
bw_method='silverman')
axes[0, 1].set_title('Custom violinplot 2', fontsize=fs)
axes[0, 2].violinplot(data, pos, points=60, widths=0.7, showmeans=True,
showextrema=True, showmedians=True, bw_method=0.5)
axes[0, 2].set_title('Custom violinplot 3', fontsize=fs)
axes[1, 0].violinplot(data, pos, points=80, vert=False, widths=0.7,
showmeans=True, showextrema=True, showmedians=True)
axes[1, 0].set_title('Custom violinplot 4', fontsize=fs)
axes[1, 1].violinplot(data, pos, points=100, vert=False, widths=0.9,
showmeans=True, showextrema=True, showmedians=True,
bw_method='silverman')
axes[1, 1].set_title('Custom violinplot 5', fontsize=fs)
axes[1, 2].violinplot(data, pos, points=200, vert=False, widths=1.1,
showmeans=True, showextrema=True, showmedians=True,
bw_method=0.5)
axes[1, 2].set_title('Custom violinplot 6', fontsize=fs)
for ax in axes.flatten():
ax.set_yticklabels([])
fig.suptitle("Violin Plotting Examples")
fig.subplots_adjust(hspace=0.4)
plt.show()