使用 pandas 聚合所有数据框行对组合 [英] Aggregate all dataframe row pair combinations using pandas
问题描述
我使用python pandas跨数据帧执行分组和聚合,但是我现在想对行进行特定的成对聚合(n选择2,统计组合).这是示例数据,在这里我想查看[mygenes]中的所有基因对:
I use python pandas to perform grouping and aggregation across data frames, but I would like to now perform specific pairwise aggregation of rows (n choose 2, statistical combination). Here is the example data, where I would like to look at all pairs of genes in [mygenes]:
import pandas
import itertools
mygenes=['ABC1', 'ABC2', 'ABC3', 'ABC4']
df = pandas.DataFrame({'Gene' : ['ABC1', 'ABC2', 'ABC3', 'ABC4','ABC5'],
'case1' : [0,1,1,0,0],
'case2' : [1,1,1,0,1],
'control1':[0,0,1,1,1],
'control2':[1,0,0,1,0] })
>>> df
Gene case1 case2 control1 control2
0 ABC1 0 1 0 1
1 ABC2 1 1 0 0
2 ABC3 1 1 1 0
3 ABC4 0 0 1 1
4 ABC5 0 1 1 0
最终产品应如下所示(默认情况下,应用np.sum很好):
The final product should look like this (applying np.sum by default is fine):
case1 case2 control1 control2
'ABC1', 'ABC2' 1 2 0 1
'ABC1', 'ABC3' 1 2 1 1
'ABC1', 'ABC4' 0 1 1 2
'ABC2', 'ABC3' 2 2 1 0
'ABC2', 'ABC4' 1 1 1 1
'ABC3', 'ABC4' 1 1 2 1
可以使用itertools($ itertools.combinations(mygenes, 2)
)轻松获得基因对的集合,但是我无法弄清楚如何根据值对特定行进行汇总.有人可以建议吗?谢谢
The set of gene pairs can be easily obtained with itertools ($itertools.combinations(mygenes, 2)
), but I can't figure out how to perform aggregation of specific rows based on their values. Can anyone advise? Thank you
推荐答案
我想不出一种聪明的矢量化方法来做到这一点,但是除非性能是真正的瓶颈,否则我倾向于使用最简单的有意义的方法.在这种情况下,我可能会set_index("Gene")
然后使用loc
选择行:
I can't think of a clever vectorized way to do this, but unless performance is a real bottleneck I tend to use the simplest thing which makes sense. In this case, I might set_index("Gene")
and then use loc
to pick out the rows:
>>> df = df.set_index("Gene")
>>> cc = list(combinations(mygenes,2))
>>> out = pd.DataFrame([df.loc[c,:].sum() for c in cc], index=cc)
>>> out
case1 case2 control1 control2
(ABC1, ABC2) 1 2 0 1
(ABC1, ABC3) 1 2 1 1
(ABC1, ABC4) 0 1 1 2
(ABC2, ABC3) 2 2 1 0
(ABC2, ABC4) 1 1 1 1
(ABC3, ABC4) 1 1 2 1
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