blocks|key|237378|text|柱状图是表示直方图沿X轴的单个条形宽度的范围。您也可以将其称为间隔。(维基百科更正式地将它们定义为“不相交的类别”。)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|237379|Numpy+histogram函数不会绘制直方图，但它会计算每个条形图中输入数据的出现次数，进而确定每个条形图的面积(如果条形图的宽度不相等，则不一定是高度)。|offset|length|style|CODE|237380|在此示例中：|237381|+np.histogram([1,+2,+1],+bins=[0,+1,+2,+3])|code-block|syntax|javascript|237382|有3个存储箱，取值范围从0到1(不包括1)、1到2(不包括2)和2到3(包括3)。在本例中，Numpy通过给出一个分隔符列表([0,+1,+2,+3])来定义这些bin的方式，尽管它也会在结果中返回bin，因为如果没有指定，它可以自动从输入中选择它们。例如，如果为bins=5，它将使用5个等宽分布在最小输入值和最大输入值之间的存储箱。|237383|输入值为1、2和1。因此，bin+"1+to+2“包含两个实例(两个1值)，bin+"2+to+3”包含一个实例(+2)。这些结果位于返回的元组的第一项中：array([0,+2,+1])。|237384|由于此处的存储箱具有相同的宽度，因此您可以使用每个条形的高度的引用次数。绘制后，您将拥有：|237385|237386|一个高度为0的条形，代表范围/条柱0,1在X轴上，|unordered-list-item|237387|一个高度为2的条形，代表范围/条柱1,2，|237388|一个高度为1的条形，代表范围/条柱2,3.|237389|237390|237391|你可以直接用Matplotlib来绘制(它的hist函数也会返回箱子和值)：|237392|>>>+import+matplotlib.pyplot+as+plt
>>>+plt.hist([1,+2,+1],+bins=[0,+1,+2,+3])
(array([0,+2,+1]),+array([0,+1,+2,+3]),+<a+list+of+3+Patch+objects>)
>>>+plt.show()|237393|​|237394|📷|atomic|237395|237396|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/1c7c844f766f347be537dc8e4a887593.png|imageAlt^0|0|6|9|0|0|0|1R|C|3O|6|0|Y|1|1M|1|26|G|0|0|0|0|0|0|0|0|M|4|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|1N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1O|8|@$D|1P|E|1Q|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|1R|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|1S|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|1T|8|@$D|1U|E|1V|F|G]|$D|1W|E|1X|F|G]]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1Y|8|@$D|1Z|E|20|F|G]|$D|21|E|22|F|G]|$D|23|E|24|F|G]]|9|@]|A|$]]|$1|S|3|T|5|6|7|25|8|@]|9|@]|A|$]]|$1|U|3|-4|5|6|7|26|8|@]|9|@]|A|$]]|$1|V|3|W|5|X|7|27|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|X|7|28|8|@]|9|@]|A|$]]|$1|10|3|11|5|X|7|29|8|@]|9|@]|A|$]]|$1|12|3|-4|5|6|7|2A|8|@]|9|@]|A|$]]|$1|13|3|-4|5|6|7|2B|8|@]|9|@]|A|$]]|$1|14|3|15|5|6|7|2C|8|@$D|2D|E|2E|F|G]]|9|@]|A|$]]|$1|16|3|17|5|L|7|2F|8|@]|9|@]|A|$M|N]]|$1|18|3|19|5|6|7|2G|8|@]|9|@]|A|$]]|$1|1A|3|1B|5|1C|7|2H|8|@]|9|@$D|2I|E|2J|1|2K]]|A|$]]|$1|1D|3|19|5|6|7|2L|8|@]|9|@]|A|$]]|$1|1E|3|-4|5|6|7|2M|8|@]|9|@]|A|$]]]|1F|$1G|$5|1H|1I|1J|A|$1K|1L|1M|-4]]]]

A bin is range that represents the width of a single bar of the histogram along the X-axis. You could also call this the interval. (Wikipedia defines them more formally as "disjoint categories".)

The Numpy <code>histogram</code> function doesn't draw the histogram, but it computes the occurrences of input data that fall within each bin, which in turns determines the area (not necessarily the height if the bins aren't of equal width) of each bar.

In this example:

<pre><code> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
</code></pre>

There are 3 bins, for values ranging from 0 to 1 (excl 1.), 1 to 2 (excl. 2) and 2 to 3 (incl. 3), respectively. The way Numpy defines these bins if by giving a list of delimiters (<code>[0, 1, 2, 3]</code>) in this example, although it also returns the bins in the results, since it can choose them automatically from the input, if none are specified. If <code>bins=5</code>, for example, it will use 5 bins of equal width spread between the minimum input value and the maximum input value.

The input values are 1, 2 and 1. Therefore, bin "1 to 2" contains two occurrences (the two <code>1</code> values), and bin "2 to 3" contains one occurrence (the <code>2</code>). These results are in the first item in the returned tuple: <code>array([0, 2, 1])</code>.

Since the bins here are of equal width, you can use the number of occurrences for the height of each bar. When drawn, you would have:

<ul>
<li>a bar of height 0 for range/bin [0,1] on the X-axis,</li>
<li>a bar of height 2 for range/bin [1,2],</li>
<li>a bar of height 1 for range/bin [2,3].</li>
</ul>

<hr>

You can plot this directly with Matplotlib (its <code>hist</code> function also returns the bins and the values):

<pre><code>&gt;&gt;&gt; import matplotlib.pyplot as plt
&gt;&gt;&gt; plt.hist([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]), &lt;a list of 3 Patch objects&gt;)
&gt;&gt;&gt; plt.show()
</code></pre>

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

blocks|key|3123008|text|import+numpy+as+np++++
hist,+bin_edges+=+np.histogram([1,+1,+2,+2,+2,+2,+3],+bins+=+range(5))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|3123009|下面，hist表示在bin+#0中有0项，在bin+#1中有2项，在bin+#3中有4项，在bin+#4中有1项。|unstyled|offset|length|style|CODE|3123010|print(hist)
#+array([0,+2,+4,+1])+++|3123011|bin_edges表示仓位#0是区间[0，1]，仓位#1是[1,2)，...，仓位#3是[3，4]。|3123012|print+(bin_edges)
#+array([0,+1,+2,+3,+4]))++|3123013|使用上面的代码，将输入更改为np.histogram，看看它是如何工作的。|3123014|3123015|但一张图片胜过千言万语：|3123016|import+matplotlib.pyplot+as+plt
plt.bar(bin_edges[:-1],+hist,+width+=+1)
plt.xlim(min(bin_edges),+max(bin_edges))
plt.show()+++|3123017|​|3123018|📷|atomic|3123019|3123020|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/775cf91c555e28fa42d9f84ba60950a5.png|imageAlt^0|0|3|4|0|0|0|9|0|0|E|C|0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|1C|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|1D|8|@$G|1E|H|1F|I|J]]|9|@]|A|$]]|$1|K|3|L|5|6|7|1G|8|@]|9|@]|A|$B|C]]|$1|M|3|N|5|F|7|1H|8|@$G|1I|H|1J|I|J]]|9|@]|A|$]]|$1|O|3|P|5|6|7|1K|8|@]|9|@]|A|$B|C]]|$1|Q|3|R|5|F|7|1L|8|@$G|1M|H|1N|I|J]]|9|@]|A|$]]|$1|S|3|-4|5|F|7|1O|8|@]|9|@]|A|$]]|$1|T|3|U|5|F|7|1P|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|1Q|8|@]|9|@]|A|$B|C]]|$1|X|3|Y|5|F|7|1R|8|@]|9|@]|A|$]]|$1|Z|3|10|5|11|7|1S|8|@]|9|@$G|1T|H|1U|1|1V]]|A|$]]|$1|12|3|Y|5|F|7|1W|8|@]|9|@]|A|$]]|$1|13|3|-4|5|F|7|1X|8|@]|9|@]|A|$]]]|14|$15|$5|16|17|18|A|$19|1A|1B|-4]]]]

<pre><code>import numpy as np 
hist, bin_edges = np.histogram([1, 1, 2, 2, 2, 2, 3], bins = range(5))
</code></pre>

Below, <code>hist</code> indicates that there are 0 items in bin #0, 2 in bin #1, 4 in bin #3, 1 in bin #4.

<pre><code>print(hist)
# array([0, 2, 4, 1]) 
</code></pre>

<code>bin_edges</code> indicates that bin #0 is the interval [0,1), bin #1 is [1,2), ...,
bin #3 is [3,4).

<pre><code>print (bin_edges)
# array([0, 1, 2, 3, 4])) 
</code></pre>

Play with the above code, change the input to <code>np.histogram</code> and see how it works.

<hr>

But a picture is worth a thousand words:

<pre><code>import matplotlib.pyplot as plt
plt.bar(bin_edges[:-1], hist, width = 1)
plt.xlim(min(bin_edges), max(bin_edges))
plt.show() 
</code></pre>

<img src="https://i.stack.imgur.com/M669l.png" alt="enter image description here">

blocks|key|3123070|text|使用numpy.histogram的另一件有用的事情是在折线图上将输出绘制为x和y坐标。例如：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3123071|arr+=+np.random.randint(1,+51,+500)
y,+x+=+np.histogram(arr,+bins=np.arange(51))
fig,+ax+=+plt.subplots()
ax.plot(x[:-1],+y)
fig.show()|code-block|syntax|javascript|3123072|​|3123073|📷|atomic|3123074|3123075|这可能是可视化直方图的一种有用的方法，在这种情况下，您需要更高级别的粒度，而不是到处都是条形图。在识别极端像素值的图像直方图中非常有用。|3123076|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/09bcdaa249444590e8305e07b8cf3299.png|imageAlt^0|2|F|0|0|0|0|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|11|8|@$9|12|A|13|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|15|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|16|8|@]|D|@$9|17|A|18|1|19]]|E|$]]|$1|P|3|L|5|6|7|1A|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1B|8|@]|D|@]|E|$]]|$1|S|3|-4|5|6|7|1C|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z|10|-4]]]]

Another useful thing to do with <code>numpy.histogram</code> is to plot the output as the x and y coordinates on a linegraph. For example:

<pre><code>arr = np.random.randint(1, 51, 500)
y, x = np.histogram(arr, bins=np.arange(51))
fig, ax = plt.subplots()
ax.plot(x[:-1], y)
fig.show()
</code></pre>

<a href="https://i.stack.imgur.com/Yi3kI.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Yi3kI.png" alt="enter image description here"></a>

This can be a useful way to visualize histograms where you would like a higher level of granularity without bars everywhere. Very useful in image histograms for identifying extreme pixel values.

While reading up on numpy, I encountered the function <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html"><code>numpy.histogram()</code></a>.

What is it for and how does it work? In the docs they mention bins: What are they?

Some googling led me to the <a href="http://en.wikipedia.org/wiki/Histogram">definition of Histograms in general</a>. I get that. But unfortunately I can't link this knowledge to the examples given in the docs.

How does numpy.histogram() work?

在阅读numpy时，我遇到了函数。它是用来做什么的，它是如何工作的？在文档中，他们提到了：它们是什么？通过谷歌搜索，我找到了。我明白了。但不幸的是，我不能将这些知识与文档中给出的例子联系起来。

问numpy.histogram()是如何工作的？
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