blocks|key|2989009|text|在groupby对象上，agg函数可以一次将列表传递给apply+several+aggregation+methods。这将为您提供所需的结果：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2989010|df[['col1',+'col2',+'col3',+'col4']].groupby(['col1',+'col2']).agg(['mean',+'count'])|code-block|syntax|javascript|2989011|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#applying-multiple-functions-at-once^0|1|7|C|3|R|X|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|H|7|10|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|11|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

On <code>groupby</code> object, the <code>agg</code> function can take a list to <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#applying-multiple-functions-at-once" rel="noreferrer">apply several aggregation methods</a> at once. This should give you the result you need:

<pre><code>df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).agg(['mean', 'count'])
</code></pre>

blocks|key|172608|text|快速回答：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|172609|获取每个组的行数的最简单方法是调用.size()，它返回一个Series|offset|length|style|CODE|172610|df.groupby(['col1','col2']).size()|code-block|syntax|javascript|172611|通常，您希望此结果为DataFrame+(而不是Series)，因此您可以执行以下操作：|172612|df.groupby(['col1',+'col2']).size().reset_index(name='counts')|172613|如果您想了解如何计算每个组的行数和其他统计数据，请继续阅读下面的内容。|172614|172615|详细示例：|172616|考虑以下示例数据帧：|172617|In+[2]:+df
Out[2]:+
++col1+col2++col3++col4++col5++col6
0++++A++++B++0.20+-0.61+-0.49++1.49
1++++A++++B+-1.53+-1.01+-0.39++1.82
2++++A++++B+-0.44++0.27++0.72++0.11
3++++A++++B++0.28+-1.32++0.38++0.18
4++++C++++D++0.12++0.59++0.81++0.66
5++++C++++D+-0.13+-1.65+-1.64++0.50
6++++C++++D+-1.42+-0.11+-0.18+-0.44
7++++E++++F+-0.00++1.42+-0.26++1.17
8++++E++++F++0.91+-0.47++1.35+-0.34
9++++G++++H++1.48+-0.63+-1.14++0.17|172618|首先，让我们使用.size()来获取行数：|172619|In+[3]:+df.groupby(['col1',+'col2']).size()
Out[3]:+
col1++col2
A+++++B+++++++4
C+++++D+++++++3
E+++++F+++++++2
G+++++H+++++++1
dtype:+int64|172620|然后让我们使用.size().reset_index(name='counts')来获取行数：|172621|In+[4]:+df.groupby(['col1',+'col2']).size().reset_index(name='counts')
Out[4]:+
++col1+col2++counts
0++++A++++B+++++++4
1++++C++++D+++++++3
2++++E++++F+++++++2
3++++G++++H+++++++1|172622|包含更多统计信息的结果|172623|当您想要计算分组数据的统计数据时，通常如下所示：|172624|In+[5]:+(df
+++...:+.groupby(['col1',+'col2'])
+++...:+.agg({
+++...:+++++'col3':+['mean',+'count'],+
+++...:+++++'col4':+['median',+'min',+'count']
+++...:+}))
Out[5]:+
++++++++++++col4++++++++++++++++++col3++++++
++++++++++median+++min+count++++++mean+count
col1+col2+++++++++++++++++++++++++++++++++++
A++++B++++-0.810+-1.32+++++4+-0.372500+++++4
C++++D++++-0.110+-1.65+++++3+-0.476667+++++3
E++++F+++++0.475+-0.47+++++2++0.455000+++++2
G++++H++++-0.630+-0.63+++++1++1.480000+++++1|172625|上面的结果处理起来有点麻烦，因为有嵌套的列标签，而且行数是以列为单位的。|172626|为了更好地控制输出，我通常将统计数据分成多个单独的聚合，然后使用join组合这些聚合。它看起来是这样的：|172627|In+[6]:+gb+=+df.groupby(['col1',+'col2'])
+++...:+counts+=+gb.size().to_frame(name='counts')
+++...:+(counts
+++...:++.join(gb.agg({'col3':+'mean'}).rename(columns={'col3':+'col3_mean'}))
+++...:++.join(gb.agg({'col4':+'median'}).rename(columns={'col4':+'col4_median'}))
+++...:++.join(gb.agg({'col4':+'min'}).rename(columns={'col4':+'col4_min'}))
+++...:++.reset_index()
+++...:+)
+++...:+
Out[6]:+
++col1+col2++counts++col3_mean++col4_median++col4_min
0++++A++++B+++++++4++-0.372500+++++++-0.810+++++-1.32
1++++C++++D+++++++3++-0.476667+++++++-0.110+++++-1.65
2++++E++++F+++++++2+++0.455000++++++++0.475+++++-0.47
3++++G++++H+++++++1+++1.480000+++++++-0.630+++++-0.63|172628|172629|脚注|172630|用于生成测试数据的代码如下所示：|172631|In+[1]:+import+numpy+as+np
+++...:+import+pandas+as+pd+
+++...:+
+++...:+keys+=+np.array([
+++...:+++++++++['A',+'B'],
+++...:+++++++++['A',+'B'],
+++...:+++++++++['A',+'B'],
+++...:+++++++++['A',+'B'],
+++...:+++++++++['C',+'D'],
+++...:+++++++++['C',+'D'],
+++...:+++++++++['C',+'D'],
+++...:+++++++++['E',+'F'],
+++...:+++++++++['E',+'F'],
+++...:+++++++++['G',+'H']+
+++...:+++++++++])
+++...:+
+++...:+df+=+pd.DataFrame(
+++...:+++++np.hstack([keys,np.random.randn(10,4).round(2)]),+
+++...:+++++columns+=+['col1',+'col2',+'col3',+'col4',+'col5',+'col6']
+++...:+)
+++...:+
+++...:+df[['col3',+'col4',+'col5',+'col6']]+=+\
+++...:+++++df[['col3',+'col4',+'col5',+'col6']].astype(float)
+++...:+|172632|免责声明：|BOLD|172633|如果您正在聚合的一些列具有空值，那么您确实希望将组行数视为每个列的独立聚合。否则，您可能会被误导，因为pandas会在mean计算中丢弃NaN条目，而不会告诉您实际使用了多少记录。|172634|entityMap^0|0|H|7|U|6|0|0|A|9|O|6|0|0|0|0|0|0|0|8|7|0|0|7|Y|0|0|0|0|0|0|W|4|0|0|0|0|0|0|0|5|0|1W|3|0^^$0|@$1|2|3|4|5|6|7|1X|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1Y|8|@$D|1Z|E|20|F|G]|$D|21|E|22|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|23|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|24|8|@$D|25|E|26|F|G]|$D|27|E|28|F|G]]|9|@]|A|$]]|$1|O|3|P|5|J|7|29|8|@]|9|@]|A|$K|L]]|$1|Q|3|R|5|6|7|2A|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|2B|8|@]|9|@]|A|$]]|$1|T|3|U|5|6|7|2C|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|2D|8|@]|9|@]|A|$]]|$1|X|3|Y|5|J|7|2E|8|@]|9|@]|A|$K|L]]|$1|Z|3|10|5|6|7|2F|8|@$D|2G|E|2H|F|G]]|9|@]|A|$]]|$1|11|3|12|5|J|7|2I|8|@]|9|@]|A|$K|L]]|$1|13|3|14|5|6|7|2J|8|@$D|2K|E|2L|F|G]]|9|@]|A|$]]|$1|15|3|16|5|J|7|2M|8|@]|9|@]|A|$K|L]]|$1|17|3|18|5|6|7|2N|8|@]|9|@]|A|$]]|$1|19|3|1A|5|6|7|2O|8|@]|9|@]|A|$]]|$1|1B|3|1C|5|J|7|2P|8|@]|9|@]|A|$K|L]]|$1|1D|3|1E|5|6|7|2Q|8|@]|9|@]|A|$]]|$1|1F|3|1G|5|6|7|2R|8|@$D|2S|E|2T|F|G]]|9|@]|A|$]]|$1|1H|3|1I|5|J|7|2U|8|@]|9|@]|A|$K|L]]|$1|1J|3|-4|5|6|7|2V|8|@]|9|@]|A|$]]|$1|1K|3|1L|5|6|7|2W|8|@]|9|@]|A|$]]|$1|1M|3|1N|5|6|7|2X|8|@]|9|@]|A|$]]|$1|1O|3|1P|5|J|7|2Y|8|@]|9|@]|A|$K|L]]|$1|1Q|3|1R|5|6|7|2Z|8|@$D|30|E|31|F|1S]]|9|@]|A|$]]|$1|1T|3|1U|5|6|7|32|8|@$D|33|E|34|F|G]]|9|@]|A|$]]|$1|1V|3|-4|5|6|7|35|8|@]|9|@]|A|$]]]|1W|$]]

<h2>Quick Answer:</h2>

The simplest way to get row counts per group is by calling <code>.size()</code>, which returns a <code>Series</code>:

<pre><code>df.groupby(['col1','col2']).size()
</code></pre>

 
Usually you want this result as a <code>DataFrame</code> (instead of a <code>Series</code>) so you can do:

<pre><code>df.groupby(['col1', 'col2']).size().reset_index(name='counts')
</code></pre>

 
If you want to find out how to calculate the row counts and other statistics for each group continue reading below.
 

<hr>

<h2>Detailed example:</h2>

Consider the following example dataframe:

<pre><code>In [2]: df
Out[2]: 
 col1 col2 col3 col4 col5 col6
0 A B 0.20 -0.61 -0.49 1.49
1 A B -1.53 -1.01 -0.39 1.82
2 A B -0.44 0.27 0.72 0.11
3 A B 0.28 -1.32 0.38 0.18
4 C D 0.12 0.59 0.81 0.66
5 C D -0.13 -1.65 -1.64 0.50
6 C D -1.42 -0.11 -0.18 -0.44
7 E F -0.00 1.42 -0.26 1.17
8 E F 0.91 -0.47 1.35 -0.34
9 G H 1.48 -0.63 -1.14 0.17
</code></pre>

First let's use <code>.size()</code> to get the row counts:

<pre><code>In [3]: df.groupby(['col1', 'col2']).size()
Out[3]: 
col1 col2
A B 4
C D 3
E F 2
G H 1
dtype: int64
</code></pre>

Then let's use <code>.size().reset_index(name='counts')</code> to get the row counts:

<pre><code>In [4]: df.groupby(['col1', 'col2']).size().reset_index(name='counts')
Out[4]: 
 col1 col2 counts
0 A B 4
1 C D 3
2 E F 2
3 G H 1
</code></pre>

 

<h3>Including results for more statistics</h3>

When you want to calculate statistics on grouped data, it usually looks like this:

<pre><code>In [5]: (df
 ...: .groupby(['col1', 'col2'])
 ...: .agg({
 ...: 'col3': ['mean', 'count'], 
 ...: 'col4': ['median', 'min', 'count']
 ...: }))
Out[5]: 
 col4 col3 
 median min count mean count
col1 col2 
A B -0.810 -1.32 4 -0.372500 4
C D -0.110 -1.65 3 -0.476667 3
E F 0.475 -0.47 2 0.455000 2
G H -0.630 -0.63 1 1.480000 1
</code></pre>

The result above is a little annoying to deal with because of the nested column labels, and also because row counts are on a per column basis. 

To gain more control over the output I usually split the statistics into individual aggregations that I then combine using <code>join</code>. It looks like this:

<pre><code>In [6]: gb = df.groupby(['col1', 'col2'])
 ...: counts = gb.size().to_frame(name='counts')
 ...: (counts
 ...: .join(gb.agg({'col3': 'mean'}).rename(columns={'col3': 'col3_mean'}))
 ...: .join(gb.agg({'col4': 'median'}).rename(columns={'col4': 'col4_median'}))
 ...: .join(gb.agg({'col4': 'min'}).rename(columns={'col4': 'col4_min'}))
 ...: .reset_index()
 ...: )
 ...: 
Out[6]: 
 col1 col2 counts col3_mean col4_median col4_min
0 A B 4 -0.372500 -0.810 -1.32
1 C D 3 -0.476667 -0.110 -1.65
2 E F 2 0.455000 0.475 -0.47
3 G H 1 1.480000 -0.630 -0.63
</code></pre>

 

<hr>

<h3>Footnotes</h3>

The code used to generate the test data is shown below:

<pre><code>In [1]: import numpy as np
 ...: import pandas as pd 
 ...: 
 ...: keys = np.array([
 ...: ['A', 'B'],
 ...: ['A', 'B'],
 ...: ['A', 'B'],
 ...: ['A', 'B'],
 ...: ['C', 'D'],
 ...: ['C', 'D'],
 ...: ['C', 'D'],
 ...: ['E', 'F'],
 ...: ['E', 'F'],
 ...: ['G', 'H'] 
 ...: ])
 ...: 
 ...: df = pd.DataFrame(
 ...: np.hstack([keys,np.random.randn(10,4).round(2)]), 
 ...: columns = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6']
 ...: )
 ...: 
 ...: df[['col3', 'col4', 'col5', 'col6']] = \
 ...: df[['col3', 'col4', 'col5', 'col6']].astype(float)
 ...: 
</code></pre>

 
Disclaimer:

If some of the columns that you are aggregating have null values, then you really want to be looking at the group row counts as an independent aggregation for each column. Otherwise you may be misled as to how many records are actually being used to calculate things like the mean because pandas will drop <code>NaN</code> entries in the mean calculation without telling you about it.

blocks|key|2776424|text|我们可以通过使用groupby和count轻松地做到这一点。但是，我们应该记住使用reset_index()。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2776425|df[['col1','col2','col3','col4']].groupby(['col1','col2']).count().\
reset_index()|code-block|syntax|javascript|2776426|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|$]]

We can easily do it by using groupby and count. But, we should remember to use reset_index().

<pre><code>df[['col1','col2','col3','col4']].groupby(['col1','col2']).count().\
reset_index()
</code></pre>

blocks|key|2989092|text|瑞士军刀：GroupBy.describe|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2989093|返回每个组的count、mean、std和其他有用的统计信息。|2989094|df.groupby(['A',+'B'])['C'].describe()

+++++++++++count++mean+++std+++min+++25%25+++50%25+++75%25+++max
A+++B+++++++++++++++++++++++++++++++++++++++++++++++++++++
bar+one++++++1.0++0.40+++NaN++0.40++0.40++0.40++0.40++0.40
++++three++++1.0++2.24+++NaN++2.24++2.24++2.24++2.24++2.24
++++two++++++1.0+-0.98+++NaN+-0.98+-0.98+-0.98+-0.98+-0.98
foo+one++++++2.0++1.36++0.58++0.95++1.15++1.36++1.56++1.76
++++three++++1.0+-0.15+++NaN+-0.15+-0.15+-0.15+-0.15+-0.15
++++two++++++2.0++1.42++0.63++0.98++1.20++1.42++1.65++1.87|code-block|syntax|javascript|2989095|要获得特定的统计数据，只需选择它们，|2989096|df.groupby(['A',+'B'])['C'].describe()[['count',+'mean']]

+++++++++++count++++++mean
A+++B+++++++++++++++++++++
bar+one++++++1.0++0.400157
++++three++++1.0++2.240893
++++two++++++1.0+-0.977278
foo+one++++++2.0++1.357070
++++three++++1.0+-0.151357
++++two++++++2.0++1.423148|2989097|describe适用于多个列(将['C']更改为['C',+'D']-or将其完全删除-看看会发生什么，结果是一个MultiIndexed列式数据帧)。|2989098|您还可以获得字符串数据的不同统计信息。这里有一个例子，|2989099|df2+=+df.assign(D=list('aaabbccc')).sample(n=100,+replace=True)

with+pd.option_context('precision',+2):
++++display(df2.groupby(['A',+'B'])
+++++++++++++++.describe(include='all')
+++++++++++++++.dropna(how='all',+axis=1))

++++++++++++++C+++++++++++++++++++++++++++++++++++++++++++++++++++D++++++++++++++++
++++++++++count++mean+++++++std+++min+++25%25+++50%25+++75%25+++max+count+unique+top+freq
A+++B++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
bar+one++++14.0++0.40++5.76e-17++0.40++0.40++0.40++0.40++0.40++++14++++++1+++a+++14
++++three++14.0++2.24++4.61e-16++2.24++2.24++2.24++2.24++2.24++++14++++++1+++b+++14
++++two+++++9.0+-0.98++0.00e%2B00+-0.98+-0.98+-0.98+-0.98+-0.98+++++9++++++1+++c++++9
foo+one++++22.0++1.43++4.10e-01++0.95++0.95++1.76++1.76++1.76++++22++++++2+++a+++13
++++three++15.0+-0.15++0.00e%2B00+-0.15+-0.15+-0.15+-0.15+-0.15++++15++++++1+++c+++15
++++two++++26.0++1.49++4.48e-01++0.98++0.98++1.87++1.87++1.87++++26++++++2+++b+++15|2989100|有关详细信息，请参阅documentation。|2989101|2989102|pandas+>=+1.1：|2989103|如果你只是想要捕获每个组的大小，这可以从pandas+1.1中获得，这减少了GroupBy，而且速度更快。|2989104|df.value_counts(subset=['col1',+'col2'])|2989105|最小示例|BOLD|2989106|#+Setup
np.random.seed(0)
df+=+pd.DataFrame({'A'+:+['foo',+'bar',+'foo',+'bar',
++++++++++++++++++++++++++'foo',+'bar',+'foo',+'foo'],
+++++++++++++++++++'B'+:+['one',+'one',+'two',+'three',
++++++++++++++++++++++++++'two',+'two',+'one',+'three'],
+++++++++++++++++++'C'+:+np.random.randn(8),
+++++++++++++++++++'D'+:+np.random.randn(8)})

df.value_counts(['A',+'B'])+

A++++B++++
foo++two++++++2
+++++one++++++2
+++++three++++1
bar++two++++++1
+++++three++++1
+++++one++++++1
dtype:+int64|2989107|2989108|Other+Statistical+Analysis+Tools|2989109|如果您没有找到上面要找的工具，User+Guide提供了支持的静态分析、关联和回归工具的全面列表。|2989110|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.describe.html|1|2|https://pandas.pydata.org/docs/user_guide/computation.html#computational-tools|3^0|5|G|5|G|0|0|6|5|C|4|H|3|0|0|0|0|0|8|G|5|O|A|0|0|0|A|D|1|0|0|0|12|7|0|0|0|4|0|0|0|0|W|2|0|F|A|3|0^^$0|@$1|2|3|4|5|6|7|1R|8|@$9|1S|A|1T|B|C]]|D|@$9|1U|A|1V|1|1W]]|E|$]]|$1|F|3|G|5|6|7|1X|8|@$9|1Y|A|1Z|B|C]|$9|20|A|21|B|C]|$9|22|A|23|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|24|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|25|8|@]|D|@]|E|$]]|$1|O|3|P|5|J|7|26|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|27|8|@$9|28|A|29|B|C]|$9|2A|A|2B|B|C]|$9|2C|A|2D|B|C]]|D|@]|E|$]]|$1|S|3|T|5|6|7|2E|8|@]|D|@]|E|$]]|$1|U|3|V|5|J|7|2F|8|@]|D|@]|E|$K|L]]|$1|W|3|X|5|6|7|2G|8|@]|D|@$9|2H|A|2I|1|2J]]|E|$]]|$1|Y|3|-4|5|6|7|2K|8|@]|D|@]|E|$]]|$1|Z|3|10|5|6|7|2L|8|@]|D|@]|E|$]]|$1|11|3|12|5|6|7|2M|8|@$9|2N|A|2O|B|C]]|D|@]|E|$]]|$1|13|3|14|5|J|7|2P|8|@]|D|@]|E|$K|L]]|$1|15|3|16|5|6|7|2Q|8|@$9|2R|A|2S|B|17]]|D|@]|E|$]]|$1|18|3|19|5|J|7|2T|8|@]|D|@]|E|$K|L]]|$1|1A|3|-4|5|6|7|2U|8|@]|D|@]|E|$]]|$1|1B|3|1C|5|6|7|2V|8|@]|D|@$9|2W|A|2X|1|2Y]]|E|$]]|$1|1D|3|1E|5|6|7|2Z|8|@]|D|@$9|30|A|31|1|32]]|E|$]]|$1|1F|3|-4|5|6|7|33|8|@]|D|@]|E|$]]]|1G|$1H|$5|1I|1J|1K|E|$1L|1M]]|1N|$5|1I|1J|1K|E|$1L|1M]]|1O|$5|1I|1J|1K|E|$1L|1P]]|1Q|$5|1I|1J|1K|E|$1L|1P]]]]

<h2>Swiss Army Knife: <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.describe.html" rel="noreferrer"><code>GroupBy.describe</code></a></h2>
Returns <code>count</code>, <code>mean</code>, <code>std</code>, and other useful statistics per-group.
<pre><code>df.groupby(['A', 'B'])['C'].describe()

 count mean std min 25% 50% 75% max
A B 
bar one 1.0 0.40 NaN 0.40 0.40 0.40 0.40 0.40
 three 1.0 2.24 NaN 2.24 2.24 2.24 2.24 2.24
 two 1.0 -0.98 NaN -0.98 -0.98 -0.98 -0.98 -0.98
foo one 2.0 1.36 0.58 0.95 1.15 1.36 1.56 1.76
 three 1.0 -0.15 NaN -0.15 -0.15 -0.15 -0.15 -0.15
 two 2.0 1.42 0.63 0.98 1.20 1.42 1.65 1.87
</code></pre>
To get specific statistics, just select them,
<pre><code>df.groupby(['A', 'B'])['C'].describe()[['count', 'mean']]

 count mean
A B 
bar one 1.0 0.400157
 three 1.0 2.240893
 two 1.0 -0.977278
foo one 2.0 1.357070
 three 1.0 -0.151357
 two 2.0 1.423148
</code></pre>
<code>describe</code> works for multiple columns (change <code>['C']</code> to <code>['C', 'D']</code>—or remove it altogether—and see what happens, the result is a MultiIndexed columned dataframe).
You also get different statistics for string data. Here's an example,
<pre><code>df2 = df.assign(D=list('aaabbccc')).sample(n=100, replace=True)

with pd.option_context('precision', 2):
 display(df2.groupby(['A', 'B'])
 .describe(include='all')
 .dropna(how='all', axis=1))

 C D 
 count mean std min 25% 50% 75% max count unique top freq
A B 
bar one 14.0 0.40 5.76e-17 0.40 0.40 0.40 0.40 0.40 14 1 a 14
 three 14.0 2.24 4.61e-16 2.24 2.24 2.24 2.24 2.24 14 1 b 14
 two 9.0 -0.98 0.00e+00 -0.98 -0.98 -0.98 -0.98 -0.98 9 1 c 9
foo one 22.0 1.43 4.10e-01 0.95 0.95 1.76 1.76 1.76 22 2 a 13
 three 15.0 -0.15 0.00e+00 -0.15 -0.15 -0.15 -0.15 -0.15 15 1 c 15
 two 26.0 1.49 4.48e-01 0.98 0.98 1.87 1.87 1.87 26 2 b 15
</code></pre>
For more information, see the <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.describe.html" rel="noreferrer">documentation</a>.
<hr />
<h2>pandas &gt;= 1.1: <a href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.value_counts.html" rel="noreferrer"><code>DataFrame.value_counts</code></a></h2>
This is available from pandas 1.1 if you just want to capture the size of every group, this cuts out the <code>GroupBy</code> and is faster.
<pre><code>df.value_counts(subset=['col1', 'col2'])
</code></pre>
Minimal Example
<pre><code># Setup
np.random.seed(0)
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
 'foo', 'bar', 'foo', 'foo'],
 'B' : ['one', 'one', 'two', 'three',
 'two', 'two', 'one', 'three'],
 'C' : np.random.randn(8),
 'D' : np.random.randn(8)})

df.value_counts(['A', 'B']) 

A B 
foo two 2
 one 2
 three 1
bar two 1
 three 1
 one 1
dtype: int64
</code></pre>
<hr />
<h2><a href="https://pandas.pydata.org/docs/user_guide/computation.html#computational-tools" rel="noreferrer">Other Statistical Analysis Tools</a></h2>
If you didn't find what you were looking for above, the <a href="https://pandas.pydata.org/docs/user_guide/computation.html#computational-tools" rel="noreferrer">User Guide</a> has a comprehensive listing of supported statical analysis, correlation, and regression tools.

blocks|key|2776464|text|创建一个group对象，并调用如下示例的方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2776465|grp+=+df.groupby(['col1',++'col2',++'col3'])+

grp.max()+
grp.mean()+
grp.describe()+|code-block|syntax|javascript|2776466|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|$]]

Create a group object and call methods like below example: 

<pre><code>grp = df.groupby(['col1', 'col2', 'col3']) 

grp.max() 
grp.mean() 
grp.describe() 
</code></pre>

blocks|key|2989155|text|获取多个统计信息、折叠索引并保留列名的：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2989156|df+=+df.groupby(['col1','col2']).agg(['mean',+'count'])
df.columns+=+[+'+'.join(str(i)+for+i+in+col)+for+col+in+df.columns]
df.reset_index(inplace=True)
df|code-block|syntax|javascript|2989157|产品：|2989158|​|2989159|📷|atomic|2989160|2989161|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/9b23549c8a1aa105a7706fb198c3176d.png|imageAlt^0|J|1|0|0|0|3|0|0|0|1|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|@$9|16|A|17|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|18|8|@]|D|@]|E|$]]|$1|O|3|P|5|Q|7|19|8|@]|D|@$9|1A|A|1B|1|1C]]|E|$]]|$1|R|3|N|5|6|7|1D|8|@]|D|@]|E|$]]|$1|S|3|-4|5|6|7|1E|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z|10|-4]]]]

To get multiple stats, collapse the index, and retain column names:

<pre><code>df = df.groupby(['col1','col2']).agg(['mean', 'count'])
df.columns = [ ' '.join(str(i) for i in col) for col in df.columns]
df.reset_index(inplace=True)
df
</code></pre>

Produces:

<a href="https://i.stack.imgur.com/I0gVt.png" rel="noreferrer"><img src="https://i.stack.imgur.com/I0gVt.png" alt="**enter image description here**"></a>

blocks|key|172687|text|请尝试此代码|type|unstyled|depth|inlineStyleRanges|entityRanges|data|172688|new_column=df[['col1',+'col2',+'col3',+'col4']].groupby(['col1',+'col2']).count()
df['count_it']=new_column
df|code-block|syntax|javascript|172689|我想代码会添加一个名为'count+it‘的列，对每个组进行计数|172690|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|$]]

Please try this code

<pre><code>new_column=df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).count()
df['count_it']=new_column
df
</code></pre>

I think that code will add a column called 'count it' which count of each group

blocks|key|2776522|text|如果你熟悉tidyverse+R包，这里有一个用python实现的方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2776523|from+datar.all+import+tibble,+rnorm,+f,+group_by,+summarise,+mean,+n,+rep

df+=+tibble(
++col1=rep(['A',+'B'],+5),+
++col2=rep(['C',+'D'],+each=5),+
++col3=rnorm(10),+
++col4=rnorm(10)
)
df+>>+group_by(f.col1,+f.col2)+>>+summarise(
++count=n(),
++col3_mean=mean(f.col3),+
++col4_mean=mean(f.col4)
)|code-block|syntax|javascript|2776524|++col1+col2++n++mean_col3++mean_col4
0++++A++++C++3++-0.516402+++0.468454
1++++A++++D++2++-0.248848+++0.979655
2++++B++++C++2+++0.545518++-0.966536
3++++B++++D++3++-0.349836++-0.915293
[Groups:+['col1']+(n=2)]|2776525|我是datar包的作者。如果您对使用它有任何疑问，请随时提交问题。|offset|length|2776526|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/pwwang/datar^0|0|0|0|2|5|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|D|7|W|8|@]|9|@]|A|$E|F]]|$1|I|3|J|5|6|7|X|8|@]|9|@$K|Y|L|Z|1|10]]|A|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]]]

If you are familiar with tidyverse R packages, here is a way to do it in python:
<pre class="lang-py prettyprint-override"><code>from datar.all import tibble, rnorm, f, group_by, summarise, mean, n, rep

df = tibble(
 col1=rep(['A', 'B'], 5), 
 col2=rep(['C', 'D'], each=5), 
 col3=rnorm(10), 
 col4=rnorm(10)
)
df &gt;&gt; group_by(f.col1, f.col2) &gt;&gt; summarise(
 count=n(),
 col3_mean=mean(f.col3), 
 col4_mean=mean(f.col4)
)
</code></pre>
<pre><code> col1 col2 n mean_col3 mean_col4
0 A C 3 -0.516402 0.468454
1 A D 2 -0.248848 0.979655
2 B C 2 0.545518 -0.966536
3 B D 3 -0.349836 -0.915293
[Groups: ['col1'] (n=2)]
</code></pre>
I am the author of the <a href="https://github.com/pwwang/datar" rel="nofollow noreferrer">datar</a> package. Please feel free to submit issues if you have any questions about using it.

I have a data frame <code>df</code> and I use several columns from it to <code>groupby</code>:

<pre><code>df['col1','col2','col3','col4'].groupby(['col1','col2']).mean()
</code></pre>

In the above way I almost get the table (data frame) that I need. What is missing is an additional column that contains number of rows in each group. In other words, I have mean but I also would like to know how many number were used to get these means. For example in the first group there are 8 values and in the second one 10 and so on.

In short: How do I get group-wise statistics for a dataframe?

Get statistics for each group (such as count, mean, etc) using pandas GroupBy?

我有一个数据框df，并且我在groupby中使用了其中的几列df['col1','col2','col3','col4'].groupby(['col1','col2']).mean()在上面的方法中，我几乎得到了我需要的表(数据框)。缺少的是包含每个组中的行数的附加列。换言之，我有意思，但我也想知道有多少个数字是用来...

问使用pandas GroupBy获取每个组的统计数据(如计数、平均值等)？
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问使用pandas GroupBy获取每个组的统计数据(如计数、平均值等)？
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