SQL的“按组分组"的含义是什么?在 pandas 上?

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问题描述

使用groupby并同时在熊猫中应用过滤器的最有效方法是什么?

what would be the most efficient way to use groupby and in parallel apply a filter in pandas?

我基本上要求的是SQL中的等价物

Basically I am asking for the equivalent in SQL of

select *
...
group by col_name
having condition

我认为有很多用例，包括条件均值，总和，条件概率等，这些使这种命令非常强大.

I think there are many uses cases ranging from conditional means, sums, conditional probabilities, etc. which would make such a command very powerful.

我需要一个非常好的性能，因此理想情况下，这样的命令将不是在python中进行多次分层操作的结果.

I need a very good performance, so ideally such a command would not be the result of several layered operations done in python.

推荐答案

如unutbu的评论所述， groupby的过滤器等同于SQL的HAVING:

As mentioned in unutbu's comment, groupby's filter is the equivalent of SQL'S HAVING:

In [11]: df = pd.DataFrame([[1, 2], [1, 3], [5, 6]], columns=['A', 'B'])

In [12]: df
Out[12]:
   A  B
0  1  2
1  1  3
2  5  6

In [13]: g = df.groupby('A')  #  GROUP BY A

In [14]: g.filter(lambda x: len(x) > 1)  #  HAVING COUNT(*) > 1
Out[14]:
   A  B
0  1  2
1  1  3

您可以编写更复杂的函数(将这些函数应用于每个组)，前提是它们返回简单的布尔值:

You can write more complicated functions (these are applied to each group), provided they return a plain ol' bool:

In [15]: g.filter(lambda x: x['B'].sum() == 5)
Out[15]:
   A  B
0  1  2
1  1  3

注意:可能存在一个错误，您无法编写可对之采取行动的函数您用于分组的列...一种解决方法是手动对列进行分组，即g = df.groupby(df['A'])).

Note: potentially there is a bug where you can't write you function to act on the columns you've used to groupby... a workaround is the groupby the columns manually i.e. g = df.groupby(df['A'])).

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