注意
跳到末尾下载完整的示例代码。
具有多个数据集的直方图(hist)函数#
绘制具有多个样本集的直方图并演示
将图例与多个样本集一起使用
堆叠条形
无填充的阶梯曲线
不同样本大小的数据集
选择不同的箱子计数和大小会显着影响直方图的形状。Astropy 文档有一个关于如何选择这些参数的精彩部分:http://docs.astropy.org/en/stable/visualization/histogram.html
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(19680801)
n_bins = 10
x = np.random.randn(1000, 3)
fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)
colors = ['red', 'tan', 'lime']
ax0.hist(x, n_bins, density=True, histtype='bar', color=colors, label=colors)
ax0.legend(prop={'size': 10})
ax0.set_title('bars with legend')
ax1.hist(x, n_bins, density=True, histtype='bar', stacked=True)
ax1.set_title('stacked bar')
ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
ax2.set_title('stack step (unfilled)')
# Make a multiple-histogram of data-sets with different length.
x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
ax3.hist(x_multi, n_bins, histtype='bar')
ax3.set_title('different sample sizes')
fig.tight_layout()
plt.show()
为每个数据集设置属性#
您可以通过将值列表传递给以下参数来单独设置直方图的样式
edgecolor
facecolor
hatch
linewidth
linestyle
edgecolor#
fig, ax = plt.subplots()
edgecolors = ['green', 'red', 'blue']
ax.hist(x, n_bins, fill=False, histtype="step", stacked=True,
edgecolor=edgecolors, label=edgecolors)
ax.legend()
ax.set_title('Stacked Steps with Edgecolors')
plt.show()
facecolor#
fig, ax = plt.subplots()
facecolors = ['green', 'red', 'blue']
ax.hist(x, n_bins, histtype="barstacked", facecolor=facecolors, label=facecolors)
ax.legend()
ax.set_title("Bars with different Facecolors")
plt.show()
hatch#
linewidth#
fig, ax = plt.subplots()
linewidths = [1, 2, 3]
edgecolors = ["green", "red", "blue"]
ax.hist(x, n_bins, fill=False, histtype="bar", linewidth=linewidths,
edgecolor=edgecolors, label=linewidths)
ax.legend()
ax.set_title("Bars with Linewidths")
plt.show()
linestyle#
fig, ax = plt.subplots()
linestyles = ['-', ':', '--']
ax.hist(x, n_bins, fill=False, histtype='bar', linestyle=linestyles,
edgecolor=edgecolors, label=linestyles)
ax.legend()
ax.set_title('Bars with Linestyles')
plt.show()
脚本总运行时间:(0 分钟 3.955 秒)