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此示例沿分类X轴绘制不同样本的水平柱状图。此外，柱状图是绘制对称的关于他们的X位置，从而使他们非常类似小提琴图。
要制作这种高度专业化的绘图，我们不能使用标准 hist 方法。相反，我们使用 barh 直接绘制水平条。钢筋的垂直位置和长度通过 np.histogram 功能。所有样本的柱状图使用相同的范围（最小值和最大值）和箱数计算，以便每个样本的箱位于相同的垂直位置。
选择不同的仓位计数和大小会显著影响直方图的形状。Astropy文档有一个关于如何选择这些参数的重要部分：http://docs.astropy.org/en/stable/visualization/histogram.html
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(19680801)
number_of_bins = 20
# An example of three data sets to compare
number_of_data_points = 387
labels = ["A", "B", "C"]
data_sets = [np.random.normal(0, 1, number_of_data_points),
np.random.normal(6, 1, number_of_data_points),
np.random.normal(-3, 1, number_of_data_points)]
# Computed quantities to aid plotting
hist_range = (np.min(data_sets), np.max(data_sets))
binned_data_sets = [
np.histogram(d, range=hist_range, bins=number_of_bins)[0]
for d in data_sets
]
binned_maximums = np.max(binned_data_sets, axis=1)
x_locations = np.arange(0, sum(binned_maximums), np.max(binned_maximums))
# The bin_edges are the same for all of the histograms
bin_edges = np.linspace(hist_range[0], hist_range[1], number_of_bins + 1)
centers = 0.5 * (bin_edges + np.roll(bin_edges, 1))[:-1]
heights = np.diff(bin_edges)
# Cycle through and plot each histogram
fig, ax = plt.subplots()
for x_loc, binned_data in zip(x_locations, binned_data_sets):
lefts = x_loc - 0.5 * binned_data
ax.barh(centers, binned_data, height=heights, left=lefts)
ax.set_xticks(x_locations)
ax.set_xticklabels(labels)
ax.set_ylabel("Data values")
ax.set_xlabel("Data sets")
plt.show()