blocks|key|824426|text|下面是一个可用的示例：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|824427|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()|code-block|syntax|javascript|824428|​|824429|📷|atomic|offset|length|824430|824431|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/7a360409b6870e568c5176016fe71641.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

Here you have a working example:

<pre><code>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()
</code></pre>

<img src="https://i.stack.imgur.com/acUlv.png" alt="enter image description here">

blocks|key|609560|text|公认的答案给出了带有重叠条的直方图的代码，但如果您希望每个条并排(就像我做的那样)，请尝试下面的变体：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|609561|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()|code-block|syntax|javascript|609562|​|609563|📷|atomic|offset|length|609564|609565|参考：http://matplotlib.org/examples/statistics/histogram_demo_multihist.html|609566|编辑2018/03/16:根据@stochastic_zeitgeist的建议，更新以允许绘制不同大小的数组|609567|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/d1026b0d14e5bf265368226e4fa7f01a.png|imageAlt|1|LINK|MUTABLE|url|http://matplotlib.org/examples/statistics/histogram_demo_multihist.html^0|0|0|0|0|1|0|0|0|3|1Z|1|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|17|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|18|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|19|8|@]|9|@$L|1A|M|1B|1|1C]]|A|$]]|$1|N|3|H|5|6|7|1D|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1E|8|@]|9|@$L|1F|M|1G|1|1H]]|A|$]]|$1|Q|3|R|5|6|7|1I|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1J|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z|10|-4]]|11|$5|12|W|13|A|$14|15]]]]

The accepted answers gives the code for a histogram with overlapping bars, but in case you want each bar to be side-by-side (as I did), try the variation below:
<pre><code>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()
</code></pre>
<a href="https://i.stack.imgur.com/D8Ojb.png" rel="noreferrer"><img src="https://i.stack.imgur.com/D8Ojb.png" alt="enter image description here" /></a>
Reference: <a href="http://matplotlib.org/examples/statistics/histogram_demo_multihist.html" rel="noreferrer">http://matplotlib.org/examples/statistics/histogram_demo_multihist.html</a>
EDIT [2018/03/16]: Updated to allow plotting of arrays of different sizes, as suggested by @stochastic_zeitgeist

blocks|key|824605|text|以防您有熊猫(import+pandas+as+pd)或可以使用它：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|824606|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()|code-block|syntax|javascript|824607|entityMap^0|7|J|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

Just in case you have pandas (<code>import pandas as pd</code>) or are ok with using it:

<pre><code>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()
</code></pre>

blocks|key|824631|text|这里有一个简单的方法，当数据具有不同的大小时，在同一张图上绘制两个直方图，它们的条形并排：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|824632|def+plotHistogram(p,+o):
++++"""
++++p+and+o+are+iterables+with+the+values+you+want+to+
++++plot+the+histogram+of
++++"""
++++plt.hist([p,+o],+color=['g','r'],+alpha=0.8,+bins=50)
++++plt.show()|code-block|syntax|javascript|824633|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Here is a simple method to plot two histograms, with their bars side-by-side, on the same plot when the data has different sizes: 

<pre><code>def plotHistogram(p, o):
 """
 p and o are iterables with the values you want to 
 plot the histogram of
 """
 plt.hist([p, o], color=['g','r'], alpha=0.8, bins=50)
 plt.show()
</code></pre>

blocks|key|824771|text|在具有不同样本大小的情况下，可能很难将分布与单个y轴进行比较。例如：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|824772|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()|code-block|syntax|javascript|824773|​|824774|📷|atomic|offset|length|824775|824776|在这种情况下，您可以在不同的轴上绘制两个数据集。为此，您可以使用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边缘，使它们不重叠)：|824777|#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+%2B+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()|824778|824779|824780|824781|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/170576e668aac61f4ebaa3a3488ca375.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/58a3be71dfe546bbfe491dfe6cebd253.png^0|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|17|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|18|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|19|8|@]|9|@$L|1A|M|1B|1|1C]]|A|$]]|$1|N|3|H|5|6|7|1D|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1E|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|1F|8|@]|9|@]|A|$E|F]]|$1|S|3|H|5|6|7|1G|8|@]|9|@]|A|$]]|$1|T|3|J|5|K|7|1H|8|@]|9|@$L|1I|M|1J|1|1K]]|A|$]]|$1|U|3|H|5|6|7|1L|8|@]|9|@]|A|$]]|$1|V|3|-4|5|6|7|1M|8|@]|9|@]|A|$]]]|W|$X|$5|Y|Z|10|A|$11|12|13|-4]]|14|$5|Y|Z|10|A|$11|15|13|-4]]]]

In the case you have different sample sizes, it may be difficult to compare the distributions with a single y-axis. For example:

<pre><code>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()
</code></pre>

<a href="https://i.stack.imgur.com/6iOYj.png" rel="noreferrer"><img src="https://i.stack.imgur.com/6iOYj.png" alt="hist_single_ax"></a>

In this case, you can plot your two data sets on different axes. To do so, you can get your histogram data using matplotlib, clear the axis, and then re-plot it on two separate axes (shifting the bin edges so that they don't overlap):

<pre><code>#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()
</code></pre>

<a href="https://i.stack.imgur.com/iayQ1.png" rel="noreferrer"><img src="https://i.stack.imgur.com/iayQ1.png" alt="hist_twin_ax"></a>

blocks|key|609880|text|您应该使用hist返回的值中的bins|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|609881|import+numpy+as+np
import+matplotlib.pyplot+as+plt

foo+=+np.random.normal(loc=1,+size=100)+#+a+normal+distribution
bar+=+np.random.normal(loc=-1,+size=10000)+#+a+normal+distribution

_,+bins,+_+=+plt.hist(foo,+bins=50,+range=[-6,+6],+normed=True)
_+=+plt.hist(bar,+bins=bins,+alpha=0.5,+normed=True)|code-block|syntax|javascript|609882|​|609883|📷|atomic|609884|609885|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/37c7a0681a00380fadc570632d33539f.png|imageAlt^0|5|4|F|4|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@$9|10|A|11|B|C]|$9|12|A|13|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|15|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|16|8|@]|D|@$9|17|A|18|1|19]]|E|$]]|$1|P|3|L|5|6|7|1A|8|@]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1B|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X|Y|-4]]]]

You should use <code>bins</code> from the values returned by <code>hist</code>:

<pre><code>import numpy as np
import matplotlib.pyplot as plt

foo = np.random.normal(loc=1, size=100) # a normal distribution
bar = np.random.normal(loc=-1, size=10000) # a normal distribution

_, bins, _ = plt.hist(foo, bins=50, range=[-6, 6], normed=True)
_ = plt.hist(bar, bins=bins, alpha=0.5, normed=True)
</code></pre>

<a href="https://i.stack.imgur.com/ATCHO.png" rel="noreferrer"><img src="https://i.stack.imgur.com/ATCHO.png" alt="Two matplotlib histograms with same binning"></a>

blocks|key|824878|text|这个问题以前已经回答过了，但想要添加另一个快速/简单的解决方法，以帮助其他访问者解决这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|824879|import+seasborn+as+sns+
sns.kdeplot(mydata1)
sns.kdeplot(mydata2)|code-block|syntax|javascript|824880|一些有用的例子是用于kde与直方图比较的here。|offset|length|824881|entityMap|0|LINK|mutability|MUTABLE|url|https://pythonhosted.org/PyQt-Fit/KDE_tut.html^0|0|0|K|4|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@$I|V|J|W|1|X]]|A|$]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|R]]]]

This question has been answered before, but wanted to add another quick/easy workaround that might help other visitors to this question.

<pre><code>import seasborn as sns 
sns.kdeplot(mydata1)
sns.kdeplot(mydata2)
</code></pre>

Some helpful examples are <a href="https://pythonhosted.org/PyQt-Fit/KDE_tut.html" rel="nofollow noreferrer">here</a> for kde vs histogram comparison.

blocks|key|824930|text|受到所罗门答案的启发，但要坚持与直方图相关的问题，一个干净的解决方案是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|824931|sns.distplot(bar)
sns.distplot(foo)
plt.show()|code-block|syntax|javascript|824932|确保首先绘制较高的直方图，否则需要设置plt.ylim(0,0.45)，以便不会截断较高的直方图。|824933|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Inspired by Solomon's answer, but to stick with the question, which is related to histogram, a clean solution is:

<pre><code>sns.distplot(bar)
sns.distplot(foo)
plt.show()
</code></pre>

Make sure to plot the taller one first, otherwise you would need to set plt.ylim(0,0.45) so that the taller histogram is not chopped off.

blocks|key|825022|text|当你想要从一个二维Numpy数组绘制直方图时，有一个警告。您需要交换这两个轴。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|825023|import+numpy+as+np
import+matplotlib.pyplot+as+plt

data+=+np.random.normal(size=(2,+300))
#+swapped_data.shape+==+(300,+2)
swapped_data+=+np.swapaxes(x,+axis1=0,+axis2=1)
plt.hist(swapped_data,+bins=30,+label=['x',+'y'])
plt.legend()
plt.show()|code-block|syntax|javascript|825024|​|825025|📷|atomic|offset|length|825026|825027|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/2c16cf08365cc50c80593fb71a811c6d.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

There is one caveat when you want to plot the histogram from a 2-d numpy array. You need to swap the 2 axes.

<pre class="lang-py prettyprint-override"><code>import numpy as np
import matplotlib.pyplot as plt

data = np.random.normal(size=(2, 300))
# swapped_data.shape == (300, 2)
swapped_data = np.swapaxes(x, axis1=0, axis2=1)
plt.hist(swapped_data, bins=30, label=['x', 'y'])
plt.legend()
plt.show()
</code></pre>

<a href="https://i.stack.imgur.com/k4kk3.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/k4kk3.png" alt="enter image description here"></a>

blocks|key|610146|text|这也是一个与joaquin答案非常相似的选项：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|610147|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()|code-block|syntax|javascript|610148|提供以下输出：|610149|​|610150|📷|atomic|offset|length|610151|610152|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/079355cec11a8a9039a8c9661e7658e2.png|imageAlt^0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|10|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|11|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|12|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|13|8|@]|9|@$N|14|O|15|1|16]]|A|$]]|$1|P|3|J|5|6|7|17|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|18|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X|Y|-4]]]]

Also an option which is quite similar to joaquin answer:

<pre><code>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()
</code></pre>

Gives the following output:

<a href="https://i.stack.imgur.com/xzJl7.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/xzJl7.png" alt="enter image description here"></a>

blocks|key|610252|text|绘制两个重叠的直方图(或更多)可能会导致相当混乱的绘图。我发现使用step+histograms+(也称为空心直方图)可以大大提高可读性。唯一的缺点是，在matplotlib中，步长直方图的默认图例格式不正确，因此可以像下面的示例那样对其进行编辑：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|610253|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()|code-block|syntax|javascript|610254|​|610255|📷|atomic|610256|610257|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，这可以适用于大约5个重叠的分布。除此之外，还需要使用另一种类型的绘图，例如其中一种呈现的here。|610258|entityMap|0|LINK|mutability|MUTABLE|url|https://matplotlib.org/gallery/statistics/histogram_histtypes.html|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/f75c2dcf53da979c6b7d5181ab2a9734.png|imageAlt|2|https://www.data-to-viz.com/story/OneNumOneCatSeveralObs.html^0|X|F|0|0|0|0|0|1|1|0|0|2F|4|2|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@$A|17|B|18|1|19]]|C|$]]|$1|D|3|E|5|F|7|1A|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1B|8|@]|9|@]|C|$]]|$1|K|3|L|5|M|7|1C|8|@]|9|@$A|1D|B|1E|1|1F]]|C|$]]|$1|N|3|J|5|6|7|1G|8|@]|9|@]|C|$]]|$1|O|3|P|5|6|7|1H|8|@]|9|@$A|1I|B|1J|1|1K]]|C|$]]|$1|Q|3|-4|5|6|7|1L|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|Z|U|10|C|$11|12|13|-4]]|14|$5|T|U|V|C|$W|15]]]]

Plotting two overlapping histograms (or more) can lead to a rather cluttered plot. I find that using <a href="https://matplotlib.org/gallery/statistics/histogram_histtypes.html" rel="nofollow noreferrer">step histograms</a> (aka hollow histograms) improves the readability quite a bit. The only downside is that in matplotlib the default legend for a step histogram is not properly formatted, so it can be edited like in the following example:
<pre><code>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()
</code></pre>
<a href="https://i.stack.imgur.com/OpYk5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OpYk5.png" alt="step_hist" /></a>
As you can see, the result looks quite clean. This is especially useful when overlapping even more than two histograms. Depending on how the variables are distributed, this can work for up to around 5 overlapping distributions. More than that would require the use of another type of plot, such as one of those presented <a href="https://www.data-to-viz.com/story/OneNumOneCatSeveralObs.html" rel="nofollow noreferrer">here</a>.

I created a histogram plot using data from a file and no problem. Now I wanted to superpose data from another file in the same histogram, so I do something like this

<pre><code>n,bins,patchs = ax.hist(mydata1,100)
n,bins,patchs = ax.hist(mydata2,100)
</code></pre>

but the problem is that for each interval, only the bar with the highest value appears, and the other is hidden. I wonder how could I plot both histograms at the same time with different colors.

Plot two histograms on single chart with matplotlib

我使用文件中的数据创建了一个直方图，没有问题。现在我想在同一直方图中叠加来自另一个文件的数据，所以我这样做n,bins,patchs = ax.hist(mydata1,100)n,bins,patchs = ax.hist(mydata2,100)但问题是，对于每个间隔，只显示具有最高值的条形图，而另一个条形图是隐藏...

问使用matplotlib在单个图表上绘制两个直方图
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