Pandas:按两列分组以获得另一列的总和

问题描述

我查看了之前提出的大部分问题，但无法找到我的问题的答案:

I look most of the previously asked questions but was not able to find answer for my question:

我有以下 data.frame

I have following data.frame

           id   year month score num_attempts
0      483625  2010    01   50      1
1      967799  2009    03   50      1
2      213473  2005    09  100      1
3      498110  2010    12   60      1
5      187243  2010    01  100      1
6      508311  2005    10   15      1
7      486688  2005    10   50      1
8      212550  2005    10  500      1
10     136701  2005    09   25      1
11     471651  2010    01   50      1

我想获得以下数据框

year month sum_score sum_num_attempts
2009    03   50           1
2005    09  125           2
2010    12   60           1
2010    01  200           2
2005    10  565           3

这是我尝试过的:

sum_df = df.groupby(by=['year','month'])['score'].sum()

但这看起来并不高效和正确.如果我有多个列需要聚合，这似乎是一个非常昂贵的调用.例如，如果我有另一列 num_attempts 并且只想按年月求和作为分数.

But this doesn't look efficient and correct. If I have more than one column need to be aggregate this seems like a very expensive call. for example if I have another column num_attempts and just want to sum by year month as score.

推荐答案

这应该是一个有效的方法:

This should be an efficient way:

sum_df = df.groupby(['year','month']).agg({'score': 'sum', 'num_attempts': 'sum'})

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