如何计算大 pandas groupby中的所有正值和负值? [英] How to count all positive and negative values in a pandas groupby?
问题描述
假设我们有一张桌子:
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)})
输出:
A B C D
0 foo one -1.304026 0.237045
1 bar one 0.030488 -0.672931
2 foo two 0.530976 -0.669559
3 bar three -0.004624 -1.604039
4 foo two -0.247809 -1.571291
5 bar two -0.570580 1.454514
6 foo one 1.441081 0.096880
7 foo three 0.296377 1.575791
我想计算C列中的正数和负数分别属于A列中的每个组,并按比例计算. A中的组比foo和bar多得多,因此组名不应出现在代码中.
I want to count how many positive and negative numbers in column C belong to each group in column A and in what proportion. There are much more groups in A than foo and bar, so group names shouldn't be in the code.
我试图对A进行分组,然后进行过滤,但没有找到正确的方法.还尝试使用一些聪明的lambda进行汇总,但没有成功.
I was trying to groupby A and then filter, but didn't find the right way. Also tried to aggregate with some smart lambda, but didn't succeed.
推荐答案
您可以将其作为一行应用(第一列为负,第二列为正):
You could do this as a one line apply (the first column being negative, the second positive):
In [11]: df.groupby('A').C.apply(lambda x: pd.Series([(x < 0).sum(), (x >= 0).sum()])).unstack()
Out[111]:
0 1
A
bar 2 1
foo 2 3
[2 rows x 2 columns]
但是,我认为更巧妙的方法是使用虚拟列并使用value_counts
:
However, I think a neater way is to use a dummy column and use value_counts
:
In [21]: df['C_sign'] = np.sign(df.C)
In [22]: df.groupby('A').C_sign.value_counts()
Out[22]:
A
bar -1 2
1 1
foo 1 3
-1 2
dtype: int64
In [23]: df.groupby('A').C_sign.value_counts().unstack()
Out[23]:
-1 1
A
bar 2 1
foo 2 3
[2 rows x 2 columns]
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