在 pandas 中使用groupby时如何分别对负值和正值求和? [英] How to sum negative and positive values separately when using groupby in pandas?

问题描述

如何在pandas中以不同的方式将正值和负值求和并将它们放在positive和negative列中?

How to sum positive and negative values differently in pandas and put them let's say in positive and negative columns?

我有这个数据框，如下所示:

I have this dataframe like below:

df = pandas.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)})

输出如下:

df
     A      B         C         D
0  foo    one  0.374156  0.319699
1  bar    one -0.356339 -0.629649
2  foo    two -0.390243 -1.387909
3  bar  three -0.783435 -0.959699
4  foo    two -1.268622 -0.250871
5  bar    two -2.302525 -1.295991
6  foo    one -0.968840  1.247675
7  foo  three  0.482845  1.004697

我使用下面的代码得到底片:

I used the below code to get negatives:

df['negative'] = df.groupby('A')['C'].apply(lambda x: x[x<0].sum()).reset_index()]

但是问题是，当我要将其添加到名为negative的dataframe列之一时，它会显示错误:

But the problem is when I want to add it to one of dataframe columns called negative it gives error:

ValueError: Wrong number of items passed 2, placement implies 1

同样，我知道它说groupby返回了多列并且不能将其分配给df['negatives']，但是我不知道如何解决问题的这一部分.我也需要有一个积极的上校.

Again I know what it says that groupby has returned more than one column and cannot assign it to df['negatives'] but I don't know how to solve this part of the problem. I need to have positive col too.

期望的结果将是:

    A      Positive   Negative
0  foo     0.374156  -0.319699
1  bar     0.356339  -0.629649

解决该问题的正确方法是什么?

What is the right solution to the problem?

推荐答案

In [14]:
df.groupby(df['A'])['C'].agg([('negative' , lambda x : x[x < 0].sum()) , ('positive' , lambda x : x[x > 0].sum())])
Out[14]:
     negative   positive
A       
bar -1.418788   2.603452
foo -0.504695   2.880512

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