pandas groupby，对行求和，然后将总和除以组中的行数 [英] Pandas groupby, sum rows, and divide sum by number of rows in group

问题描述

我有一个数据框:

>>> import pandas as pd
>>>
>>> df = pd.DataFrame({
...     'P': ['P1', 'P1', 'P2', 'P2', 'P2'],
...     'A1': [0,1,2,1,2],
...     'A2': [5,4,1,3,2],
...     'A3': [5,1,3,8,4],
...     'A4': [2,1,3,4,4],
... })
>>> df
    P  A1  A2  A3  A4
0  P1   0   5   5   2
1  P1   1   4   1   1
2  P2   2   1   3   3
3  P2   1   3   8   4
4  P2   2   2   4   4
>>>

对于每个P，我必须对A1-A4列求和.比将这个总和乘以P行的数量.例如，每个P中的行数为:

For each P I have to sum columns A1-A4. Than devide this sum by number of P rows. For example, number of rows in each P is:

>>> df.groupby('P').size()
P
P1    2
P2    3
dtype: int64
>>>

所有列的总和是:

>>> df.groupby('P').sum()
    A1  A2  A3  A4
P
P1   1   9   6   3
P2   5   6  15  11
>>>

但是由于我需要按行求和，因此我将使用:

but as I need sum by rows, I will use:

>>> df.groupby('P').sum().sum(axis=1)
P
P1    19
P2    37
dtype: int64
>>>

现在，我必须将19/2(大小)和37/3相除，以获得所需的结果.为了做到这一点，我将准备像这样的数据:

Now I have to divide 19/2 (size) and 37/3 in order to get the results that I need. In order to do that, I would prepare the data like this:

>>> pd.concat([df.groupby('P').sum().sum(axis=1), df.groupby('P').size()], axis=1)
     0  1
P
P1  19  2
P2  37  3
>>>

然后我可以使用apply来获得结果:

and than I can use apply in order to get the result:

>>> pd.concat([df.groupby('P').sum().sum(axis=1), df.groupby('P').size()], axis=1).apply(lambda row: row[0]/row[1], axis=1)
P
P1     9.500000
P2    12.333333
dtype: float64
>>>

它可以工作，但是我感到我的计算过于复杂，无法获得行数之和除以每个P的行数.

It works, but I have a feeling that I have overcomplicated calculation for getting sum of rows divided by number of rows for each P.

如果有人知道更好的方法，那么我会在毕业时听到它.我想至少摆脱concat.

If someone knows better approach I will be grad to hear it. I would like to get rid of at least concat.

推荐答案

这应该有效:

df.groupby('P').sum().sum(1) / df.groupby('P').size()

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