blocks|key|1645038|text|您可以使用pandas.cut|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1645039|bins+=+[0,+1,+5,+10,+25,+50,+100]
df['binned']+=+pd.cut(df['percentage'],+bins)
print+(df)
+++percentage+++++binned
0+++++++46.50+++(25,+50]
1+++++++44.20+++(25,+50]
2++++++100.00++(50,+100]
3+++++++42.12+++(25,+50]|code-block|syntax|javascript|1645040|1645041|bins+=+[0,+1,+5,+10,+25,+50,+100]
labels+=+[1,2,3,4,5,6]
df['binned']+=+pd.cut(df['percentage'],+bins=bins,+labels=labels)
print+(df)
+++percentage+binned
0+++++++46.50++++++5
1+++++++44.20++++++5
2++++++100.00++++++6
3+++++++42.12++++++5|1645042|或numpy.searchsorted|1645043|bins+=+[0,+1,+5,+10,+25,+50,+100]
df['binned']+=+np.searchsorted(bins,+df['percentage'].values)
print+(df)
+++percentage++binned
0+++++++46.50+++++++5
1+++++++44.20+++++++5
2++++++100.00+++++++6
3+++++++42.12+++++++5|1645044|1645045|...and，然后是value_counts或groupby，然后聚合size|1645046|s+=+pd.cut(df['percentage'],+bins=bins).value_counts()
print+(s)
(25,+50]+++++3
(50,+100]++++1
(10,+25]+++++0
(5,+10]++++++0
(1,+5]+++++++0
(0,+1]+++++++0
Name:+percentage,+dtype:+int64|1645047|1645048|s+=+df.groupby(pd.cut(df['percentage'],+bins=bins)).size()
print+(s)
percentage
(0,+1]+++++++0
(1,+5]+++++++0
(5,+10]++++++0
(10,+25]+++++0
(25,+50]+++++3
(50,+100]++++1
dtype:+int64|1645049|默认情况下，cut返回categorical。|1645050|像Series.value_counts()这样的Series方法将使用所有类别，即使数据中没有某些类别，operations+in+categorical。|1645051|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html|1|https://docs.scipy.org/doc/numpy/reference/generated/numpy.searchsorted.html|2|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html|3|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html|4|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.size.html|5|http://pandas.pydata.org/pandas-docs/stable/categorical.html#operations^0|5|A|5|A|0|0|0|0|0|1|I|1|I|1|0|0|0|A|C|N|7|Z|4|A|C|2|N|7|3|Z|4|4|0|0|0|0|6|3|B|B|0|1|L|P|6|1H|P|5|0^^$0|@$1|2|3|4|5|6|7|1L|8|@$9|1M|A|1N|B|C]]|D|@$9|1O|A|1P|1|1Q]]|E|$]]|$1|F|3|G|5|H|7|1R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|1S|8|@]|D|@]|E|$]]|$1|L|3|M|5|H|7|1T|8|@]|D|@]|E|$I|J]]|$1|N|3|O|5|6|7|1U|8|@$9|1V|A|1W|B|C]]|D|@$9|1X|A|1Y|1|1Z]]|E|$]]|$1|P|3|Q|5|H|7|20|8|@]|D|@]|E|$I|J]]|$1|R|3|-4|5|6|7|21|8|@]|D|@]|E|$]]|$1|S|3|T|5|6|7|22|8|@$9|23|A|24|B|C]|$9|25|A|26|B|C]|$9|27|A|28|B|C]]|D|@$9|29|A|2A|1|2B]|$9|2C|A|2D|1|2E]|$9|2F|A|2G|1|2H]]|E|$]]|$1|U|3|V|5|H|7|2I|8|@]|D|@]|E|$I|J]]|$1|W|3|-4|5|6|7|2J|8|@]|D|@]|E|$]]|$1|X|3|Y|5|H|7|2K|8|@]|D|@]|E|$I|J]]|$1|Z|3|10|5|6|7|2L|8|@$9|2M|A|2N|B|C]|$9|2O|A|2P|B|C]]|D|@]|E|$]]|$1|11|3|12|5|6|7|2Q|8|@$9|2R|A|2S|B|C]|$9|2T|A|2U|B|C]]|D|@$9|2V|A|2W|1|2X]]|E|$]]|$1|13|3|-4|5|6|7|2Y|8|@]|D|@]|E|$]]]|14|$15|$5|16|17|18|E|$19|1A]]|1B|$5|16|17|18|E|$19|1C]]|1D|$5|16|17|18|E|$19|1E]]|1F|$5|16|17|18|E|$19|1G]]|1H|$5|16|17|18|E|$19|1I]]|1J|$5|16|17|18|E|$19|1K]]]]

You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html" rel="noreferrer"><code>pandas.cut</code></a>:
<pre><code>bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)
print (df)
 percentage binned
0 46.50 (25, 50]
1 44.20 (25, 50]
2 100.00 (50, 100]
3 42.12 (25, 50]
</code></pre>
<hr />
<pre><code>bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
df['binned'] = pd.cut(df['percentage'], bins=bins, labels=labels)
print (df)
 percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
</code></pre>
Or <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.searchsorted.html" rel="noreferrer"><code>numpy.searchsorted</code></a>:
<pre><code>bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = np.searchsorted(bins, df['percentage'].values)
print (df)
 percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
</code></pre>
<hr />
...and then <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html" rel="noreferrer"><code>value_counts</code></a> or <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="noreferrer"><code>groupby</code></a> and aggregate <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.size.html" rel="noreferrer"><code>size</code></a>:
<pre><code>s = pd.cut(df['percentage'], bins=bins).value_counts()
print (s)
(25, 50] 3
(50, 100] 1
(10, 25] 0
(5, 10] 0
(1, 5] 0
(0, 1] 0
Name: percentage, dtype: int64
</code></pre>
<hr />
<pre><code>s = df.groupby(pd.cut(df['percentage'], bins=bins)).size()
print (s)
percentage
(0, 1] 0
(1, 5] 0
(5, 10] 0
(10, 25] 0
(25, 50] 3
(50, 100] 1
dtype: int64
</code></pre>
By default <code>cut</code> returns <code>categorical</code>.
<code>Series</code> methods like <code>Series.value_counts()</code> will use all categories, even if some categories are not present in the data, <a href="http://pandas.pydata.org/pandas-docs/stable/categorical.html#operations" rel="noreferrer">operations in categorical</a>.

blocks|key|1645069|text|我们也可以使用np.select|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1645070|bins+=+[0,+1,+5,+10,+25,+50,+100]
df['groups']+=+(np.select([df['percentage'].between(i,+j,+inclusive='right')+
+++++++++++++++++++++++++++for+i,j+in+zip(bins,+bins[1:])],+
++++++++++++++++++++++++++[1,+2,+3,+4,+5,+6]))|code-block|syntax|javascript|1645071|输出：|1645072|+++percentage++groups
0+++++++46.50+++++++5
1+++++++44.20+++++++5
2++++++100.00+++++++6
3+++++++42.12+++++++5|1645073|entityMap^0|7|9|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|$]]

We could also use <code>np.select</code>:
<pre><code>bins = [0, 1, 5, 10, 25, 50, 100]
df['groups'] = (np.select([df['percentage'].between(i, j, inclusive='right') 
 for i,j in zip(bins, bins[1:])], 
 [1, 2, 3, 4, 5, 6]))
</code></pre>
Output:
<pre><code> percentage groups
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
</code></pre>

I have a data frame column with numeric values:
<pre><code>df['percentage'].head()
46.5
44.2
100.0
42.12
</code></pre>
I want to see the column as <a href="https://en.wikipedia.org/wiki/Data_binning" rel="noreferrer">bin counts</a>:
<pre><code>bins = [0, 1, 5, 10, 25, 50, 100]
</code></pre>
How can I get the result as bins with their value counts?
<pre><code>[0, 1] bin amount
[1, 5] etc
[5, 10] etc
...
</code></pre>

Binning a column with Python Pandas

我有一个包含数值的数据框列：df['percentage'].head()46.544.2100.042.12我希望将该列显示为bins = [0, 1, 5, 10, 25, 50, 100]我怎样才能得到带有值计数的结果？[0, 1] bin amount[1, 5] etc[5, 10] etc...

问使用Python Pandas绑定列
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