pandas 在pandas中使用groupby时如何分别求和负值和正值？
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How to sum negative and positive values separately when using groupby in pandas?
提问by ALH
How to sum positive and negative values differently in pandasand put them let's say in positiveand negativecolumns?
如何以不同的方式对正值和负值求和pandas并将它们放入positive和negative列中？
I have this dataframe like below:
我有这个数据框，如下所示：
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)})
Output is as below:
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
I used the below code to get negatives:
我使用下面的代码得到否定：
df['negative'] = df.groupby('A')['C'].apply(lambda x: x[x<0].sum()).reset_index()]
But the problem is when I want to add it to one of dataframecolumns called negativeit gives error:
但问题是当我想将它添加到dataframe名为negative它的列之一时会出现错误：
ValueError: Wrong number of items passed 2, placement implies 1
Again I know what it says that groupbyhas 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.
我再次知道它说的是什么groupby返回了不止一列并且无法将其分配给df['negatives']但我不知道如何解决这部分问题。我也需要阳性结肠。
The desired outcome would be:
期望的结果是：
A Positive Negative
0 foo 0.374156 -0.319699
1 bar 0.356339 -0.629649
What is the right solution to the problem?
问题的正确解决方案是什么？
回答by Nader Hisham
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
回答by behzad.nouri
You may groupbyon Aand df['C'] > 0, and unstackthe result:
您可以groupby打开A和df['C'] > 0，unstack结果：
>>> right = df.groupby(['A', df['C'] > 0])['C'].sum().unstack()
>>> right = right.rename(columns={True:'positive', False:'negative'})
>>> right
C negative positive
A
bar -3.4423 NaN
foo -2.6277 0.857
The NaNvalue is because all the A == barrows have negative value for C.
该NaN值是因为所有A == bar行都具有负值C。
if you want to add these to the original frame corresponding to values of groupbykey, i.e. A, it would require a left join:
如果您想将这些添加到与groupby键值对应的原始帧中，即A，它需要一个 left join：
>>> df.join(right, on='A', how='left')
A B C D negative positive
0 foo one 0.3742 0.3197 -2.6277 0.857
1 bar one -0.3563 -0.6296 -3.4423 NaN
2 foo two -0.3902 -1.3879 -2.6277 0.857
3 bar three -0.7834 -0.9597 -3.4423 NaN
4 foo two -1.2686 -0.2509 -2.6277 0.857
5 bar two -2.3025 -1.2960 -3.4423 NaN
6 foo one -0.9688 1.2477 -2.6277 0.857
7 foo three 0.4828 1.0047 -2.6277 0.857
