以防你有pandas (import pandas as pd)或者可以使用它:

test = pd.DataFrame([[random.gauss(3,1) for _ in range(400)], 
                     [random.gauss(4,2) for _ in range(400)]])
plt.hist(test.values.T)
plt.show()

绘制两个重叠的直方图(或更多)会导致一个相当混乱的图。我发现使用阶梯直方图(又名空心直方图)可以大大提高可读性。唯一的缺点是在matplotlib中，步骤直方图的默认图例没有正确格式化，所以可以像下面的例子那样编辑它:

import numpy as np                   # v 1.19.2
import matplotlib.pyplot as plt      # v 3.3.2
from matplotlib.lines import Line2D

rng = np.random.default_rng(seed=123)

# Create two normally distributed random variables of different sizes
# and with different shapes
data1 = rng.normal(loc=30, scale=10, size=500)
data2 = rng.normal(loc=50, scale=10, size=1000)

# Create figure with 'step' type of histogram to improve plot readability
fig, ax = plt.subplots(figsize=(9,5))
ax.hist([data1, data2], bins=15, histtype='step', linewidth=2,
        alpha=0.7, label=['data1','data2'])

# Edit legend to get lines as legend keys instead of the default polygons
# and sort the legend entries in alphanumeric order
handles, labels = ax.get_legend_handles_labels()
leg_entries = {}
for h, label in zip(handles, labels):
    leg_entries[label] = Line2D([0], [0], color=h.get_facecolor()[:-1],
                                alpha=h.get_alpha(), lw=h.get_linewidth())
labels_sorted, lines = zip(*sorted(leg_entries.items()))
ax.legend(lines, labels_sorted, frameon=False)

# Remove spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Add annotations
plt.ylabel('Frequency', labelpad=15)
plt.title('Matplotlib step histogram', fontsize=14, pad=20)
plt.show()

如您所见，结果看起来非常干净。这在重叠两个以上的直方图时尤其有用。根据变量的分布方式，这最多可以适用于5个重叠的分布。除此之外，还需要使用另一种类型的情节，比如这里介绍的其中一种。

还有一个选项和华金的答案很相似:

import random
from matplotlib import pyplot

#random data
x = [random.gauss(3,1) for _ in range(400)]
y = [random.gauss(4,2) for _ in range(400)]

#plot both histograms(range from -10 to 10), bins set to 100
pyplot.hist([x,y], bins= 100, range=[-10,10], alpha=0.5, label=['x', 'y'])
#plot legend
pyplot.legend(loc='upper right')
#show it
pyplot.show()

给出如下输出:

这里有一个工作示例:

import random
import numpy
from matplotlib import pyplot

x = [random.gauss(3,1) for _ in range(400)]
y = [random.gauss(4,2) for _ in range(400)]

bins = numpy.linspace(-10, 10, 100)

pyplot.hist(x, bins, alpha=0.5, label='x')
pyplot.hist(y, bins, alpha=0.5, label='y')
pyplot.legend(loc='upper right')
pyplot.show()

在有不同样本量的情况下，用单个y轴比较分布可能很困难。例如:

import numpy as np
import matplotlib.pyplot as plt

#makes the data
y1 = np.random.normal(-2, 2, 1000)
y2 = np.random.normal(2, 2, 5000)
colors = ['b','g']

#plots the histogram
fig, ax1 = plt.subplots()
ax1.hist([y1,y2],color=colors)
ax1.set_xlim(-10,10)
ax1.set_ylabel("Count")
plt.tight_layout()
plt.show()

在这种情况下，您可以在不同的轴上绘制两个数据集。为此，你可以使用matplotlib获取直方图数据，清除轴，然后在两个单独的轴上重新绘制它(移动bin边，使它们不重叠):

#sets up the axis and gets histogram data
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.hist([y1, y2], color=colors)
n, bins, patches = ax1.hist([y1,y2])
ax1.cla() #clear the axis

#plots the histogram data
width = (bins[1] - bins[0]) * 0.4
bins_shifted = bins + width
ax1.bar(bins[:-1], n[0], width, align='edge', color=colors[0])
ax2.bar(bins_shifted[:-1], n[1], width, align='edge', color=colors[1])

#finishes the plot
ax1.set_ylabel("Count", color=colors[0])
ax2.set_ylabel("Count", color=colors[1])
ax1.tick_params('y', colors=colors[0])
ax2.tick_params('y', colors=colors[1])
plt.tight_layout()
plt.show()

接受的答案给出了带有重叠条的直方图的代码，但如果你想要每个条并排(就像我做的那样)，请尝试下面的变化:

import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-deep')

x = np.random.normal(1, 2, 5000)
y = np.random.normal(-1, 3, 2000)
bins = np.linspace(-10, 10, 30)

plt.hist([x, y], bins, label=['x', 'y'])
plt.legend(loc='upper right')
plt.show()

参考:http://matplotlib.org/examples/statistics/histogram_demo_multihist.html

EDIT[2018/03/16]:更新到允许绘制不同大小的数组，正如@stochastic_zeitgeist所建议的那样