blocks|key|911615|text|如果您提出一个更完整的工作(或在本例中为非工作)的示例，将会更有帮助。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|911616|我尝试了以下几种方法：|911617|import+numpy+as+np
import+matplotlib.pyplot+as+plt

x+=+np.random.randn(1000)

fig+=+plt.figure()
ax+=+fig.add_subplot(111)
n,+bins,+rectangles+=+ax.hist(x,+50,+density=True)
fig.canvas.draw()
plt.show()|code-block|syntax|javascript|911618|这确实会产生一个带有y轴的条形图直方图，它来自[0,1]。|offset|length|style|CODE|911619|此外，根据hist文档(即来自ipython的ax.hist?+)，我认为总和也很好：|911620|*normed*:
If+*True*,+the+first+element+of+the+return+tuple+will
be+the+counts+normalized+to+form+a+probability+density,+i.e.,
``n/(len(x)*dbin)``.++In+a+probability+density,+the+integral+of
the+histogram+should+be+1;+you+can+verify+that+with+a
trapezoidal+integration+of+the+probability+density+function::

++++pdf,+bins,+patches+=+ax.hist(...)
++++print+np.sum(pdf+*+np.diff(bins))|911621|在上面的命令之后尝试一下：|911622|np.sum(n+*+np.diff(bins))|911623|不出所料，我得到了1.0的返回值。请记住，normed=True并不意味着每个条形图上的值的和将是单位，而不是条形图上的积分是单位。在我的例子中，np.sum(n)返回了大约7.2767。|911624|entityMap^0|0|0|0|N|5|0|5|4|F|7|N|8|0|0|0|0|9|3|L|B|21|9|2F|6|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|11|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|12|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|13|8|@$K|14|L|15|M|N]]|9|@]|A|$]]|$1|O|3|P|5|6|7|16|8|@$K|17|L|18|M|N]|$K|19|L|1A|M|N]|$K|1B|L|1C|M|N]]|9|@]|A|$]]|$1|Q|3|R|5|F|7|1D|8|@]|9|@]|A|$G|H]]|$1|S|3|T|5|6|7|1E|8|@]|9|@]|A|$]]|$1|U|3|V|5|F|7|1F|8|@]|9|@]|A|$G|H]]|$1|W|3|X|5|6|7|1G|8|@$K|1H|L|1I|M|N]|$K|1J|L|1K|M|N]|$K|1L|L|1M|M|N]|$K|1N|L|1O|M|N]]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|1P|8|@]|9|@]|A|$]]]|Z|$]]

It would be more helpful if you posed a more complete working (or in this case non-working) example.

I tried the following:

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

x = np.random.randn(1000)

fig = plt.figure()
ax = fig.add_subplot(111)
n, bins, rectangles = ax.hist(x, 50, density=True)
fig.canvas.draw()
plt.show()
</code></pre>

This will indeed produce a bar-chart histogram with a y-axis that goes from <code>[0,1]</code>.

Further, as per the <code>hist</code> documentation (i.e. <code>ax.hist?</code> from <code>ipython</code>), I think the sum is fine too:

<pre><code>*normed*:
If *True*, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
``n/(len(x)*dbin)``. In a probability density, the integral of
the histogram should be 1; you can verify that with a
trapezoidal integration of the probability density function::

 pdf, bins, patches = ax.hist(...)
 print np.sum(pdf * np.diff(bins))
</code></pre>

Giving this a try after the commands above:

<pre><code>np.sum(n * np.diff(bins))
</code></pre>

I get a return value of <code>1.0</code> as expected. Remember that <code>normed=True</code> doesn't mean that the sum of the value at each bar will be unity, but rather than the integral over the bars is unity. In my case <code>np.sum(n)</code> returned approx <code>7.2767</code>.

blocks|key|911686|text|考虑到这个问题的日期是2010年，我知道这个答案来得太晚了，但我遇到这个问题是因为我自己也面临着类似的问题。正如答案中所述，normed=True意味着直方图下的总面积等于1，但高度总和不等于1。然而，为了便于对直方图进行物理解释，我想制作一个高度总和等于1的直方图。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|911687|我在下面的问题中发现了一个提示-+Python:+Histogram+with+area+normalized+to+something+other+than+1|offset|length|911688|但是我找不到一种方法来使条模仿histtype=的“步骤”特性hist()。这让我转向：Matplotlib+-+Stepped+histogram+with+already+binned+data|911689|如果社区认为这是可以接受的，我想提出一个综合了上述两个帖子的想法的解决方案。|911690|import+matplotlib.pyplot+as+plt

#+Let+X+be+the+array+whose+histogram+needs+to+be+plotted.
nx,+xbins,+ptchs+=+plt.hist(X,+bins=20)
plt.clf()+#+Get+rid+of+this+histogram+since+not+the+one+we+want.

nx_frac+=+nx/float(len(nx))+#+Each+bin+divided+by+total+number+of+objects.
width+=+xbins[1]+-+xbins[0]+#+Width+of+each+bin.
x+=+np.ravel(zip(xbins[:-1],+xbins[:-1]%2Bwidth))
y+=+np.ravel(zip(nx_frac,nx_frac))

plt.plot(x,y,linestyle="dashed",label="MyLabel")
#...+Further+formatting.|code-block|syntax|javascript|911691|这对我来说非常有效，尽管在某些情况下，我注意到直方图最左边的“条”或最右边的“条”不会因为触及Y轴的最低点而关闭。在这种情况下，在请求或y的末尾添加一个元素0可以得到必要的结果。|911692|只是想和你分享一下我的经历。谢谢。|911693|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/9036298/python-histogram-with-area-normalized-to-something-other-than-1|1|https://stackoverflow.com/questions/11297030/matplotlib-stepped-histogram-with-already-binned-data^0|0|H|1S|0|0|18|1J|1|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|13|8|@]|9|@$D|14|E|15|1|16]]|A|$]]|$1|F|3|G|5|6|7|17|8|@]|9|@$D|18|E|19|1|1A]]|A|$]]|$1|H|3|I|5|6|7|1B|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|1C|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|1D|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1E|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1F|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]|10|$5|V|W|X|A|$Y|11]]]]

I know this answer is too late considering the question is dated 2010 but I came across this question as I was facing a similar problem myself. As already stated in the answer, normed=True means that the total area under the histogram is equal to 1 but the sum of heights is not equal to 1. However, I wanted to, for convenience of physical interpretation of a histogram, make one with sum of heights equal to 1. 

I found a hint in the following question - <a href="https://stackoverflow.com/questions/9036298/python-histogram-with-area-normalized-to-something-other-than-1">Python: Histogram with area normalized to something other than 1</a>

But I was not able to find a way of making bars mimic the histtype="step" feature hist(). This diverted me to : <a href="https://stackoverflow.com/questions/11297030/matplotlib-stepped-histogram-with-already-binned-data">Matplotlib - Stepped histogram with already binned data</a> 

If the community finds it acceptable I should like to put forth a solution which synthesises ideas from both the above posts.

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

# Let X be the array whose histogram needs to be plotted.
nx, xbins, ptchs = plt.hist(X, bins=20)
plt.clf() # Get rid of this histogram since not the one we want.

nx_frac = nx/float(len(nx)) # Each bin divided by total number of objects.
width = xbins[1] - xbins[0] # Width of each bin.
x = np.ravel(zip(xbins[:-1], xbins[:-1]+width))
y = np.ravel(zip(nx_frac,nx_frac))

plt.plot(x,y,linestyle="dashed",label="MyLabel")
#... Further formatting.
</code></pre>

This has worked wonderfully for me though in some cases I have noticed that the left most "bar" or the right most "bar" of the histogram does not close down by touching the lowest point of the Y-axis. In such a case adding an element 0 at the begging or the end of y achieved the necessary result.

Just thought I'd share my experience. Thank you.

blocks|key|911759|text|如果希望所有条形图的总和等于1，请按值的总数对每个条形图进行加权：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|911760|weights+=+np.ones_like(myarray)+/+len(myarray)
plt.hist(myarray,+weights=weights)|code-block|syntax|javascript|911761|希望这能有所帮助，尽管这个线程已经很老了。|911762|Python2.x注意事项:为除法运算符之一向float()添加强制转换，否则由于整数除法，您将以零结束|offset|length|style|CODE|911763|entityMap^0|0|0|0|N|7|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|T|8|@$K|U|L|V|M|N]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

If you want the sum of all bars to be equal unity, weight each bin by the total number of values:

<pre><code>weights = np.ones_like(myarray) / len(myarray)
plt.hist(myarray, weights=weights)
</code></pre>

Hope that helps, although the thread is quite old...

Note for Python 2.x: add casting to <code>float()</code> for one of the operators of the division as otherwise you would end up with zeros due to integer division

blocks|key|1126788|text|这是另一个使用np.histogram()方法的简单解决方案。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1126789|myarray+=+np.random.random(100)
results,+edges+=+np.histogram(myarray,+normed=True)
binWidth+=+edges[1]+-+edges[0]
plt.bar(edges[:-1],+results*binWidth,+binWidth)|code-block|syntax|javascript|1126790|您确实可以使用以下命令检查总和是否为1：|1126791|>+print+sum(results*binWidth)
1.0|1126792|entityMap^0|7|E|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|P|$]]

Here is another simple solution using <code>np.histogram()</code> method.

<pre><code>myarray = np.random.random(100)
results, edges = np.histogram(myarray, normed=True)
binWidth = edges[1] - edges[0]
plt.bar(edges[:-1], results*binWidth, binWidth)
</code></pre>

You can indeed check that the total sums up to 1 with:

<pre><code>&gt; print sum(results*binWidth)
1.0
</code></pre>

blocks|key|911859|text|从matplotlib+3.0.2开始，normed=True就被弃用了。要获得所需的输出，我必须这样做：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|911860|import+numpy+as+np
data=np.random.randn(1000)
bins=np.arange(-3.0,3.0,51)
counts,+_+=+np.histogram(data,bins=bins)
if+density:+#+equivalent+of+normed=True
++++counts_weighter=counts.sum()
else:+#+equivalent+of+normed=False
++++counts_weighter=1.0
plt.hist(bins[:-1],bins=bins,weights=counts/counts_weighter)|code-block|syntax|javascript|911861|尝试将weights和density同时指定为plt.hist()的参数对我来说是行不通的。如果任何人知道有一种方法可以在不访问normed关键字参数的情况下工作，那么请在评论中让我知道，我会删除/修改这个答案。|911862|如果你想要面元中心，那么不要使用bins[:-1]，它是面元的边缘-你需要选择一个合适的方案来计算中心(可能是也可能不是微不足道的导出)。|911863|entityMap^0|K|B|0|0|3|7|B|7|N|A|0|G|9|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@$9|V|A|W|B|C]|$9|X|A|Y|B|C]|$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|11|8|@$9|12|A|13|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|P|$]]

Since matplotlib 3.0.2, <code>normed=True</code> is deprecated. To get the desired output I had to do:
<pre class="lang-py prettyprint-override"><code>import numpy as np
data=np.random.randn(1000)
bins=np.arange(-3.0,3.0,51)
counts, _ = np.histogram(data,bins=bins)
if density: # equivalent of normed=True
 counts_weighter=counts.sum()
else: # equivalent of normed=False
 counts_weighter=1.0
plt.hist(bins[:-1],bins=bins,weights=counts/counts_weighter)
</code></pre>
Trying to specify <code>weights</code> and <code>density</code> simultaneously as arguments to <code>plt.hist()</code> did not work for me. If anyone know of a way to get that working without having access to the normed keyword argument then please let me know in the comments and I will delete/modify this answer.
If you want bin centres then don't use <code>bins[:-1]</code> which are the bin edges - you need to choose a suitable scheme for how to calculate the centres (which may or may not be trivially derived).

I'd like to plot a normalized histogram from a vector using <code>matplotlib</code>. I tried the following:
<pre><code>plt.hist(myarray, normed=True)
</code></pre>
as well as:
<pre><code>plt.hist(myarray, normed=1)
</code></pre>
but neither option produces a y-axis from [0, 1] such that the bar heights of the histogram sum to 1.

Plot a histogram such that bar heights sum to 1 (probability)

我想用matplotlib绘制一个矢量的归一化直方图。我尝试了以下几种方法：plt.hist(myarray, normed=True)以及：plt.hist(myarray, normed=1)但这两种选择都不会产生从0，1开始的y轴，使得直方图的条形高度之和为1。

问绘制直方图，使条形高度之和为1(概率)
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