pandas 如何跨多列使用groupby转换
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How to use groupby transform across multiple columns
提问by Hillary Sanders
I have a big dataframe, and I'm grouping by one to n columns, and want to apply a function on these groups across two columns (e.g. foo and bar).
我有一个大数据框，我按一到 n 列进行分组，并希望对跨两列（例如 foo 和 bar）的这些组应用一个函数。
Here's an example dataframe:
这是一个示例数据框：
foo_function = lambda x: np.sum(x.a+x.b)
df = pd.DataFrame({'a':[1,2,3,4,5,6],
'b':[1,2,3,4,5,6],
'c':['q', 'q', 'q', 'q', 'w', 'w'],
'd':['z','z','z','o','o','o']})
# works with apply, but I want transform:
df.groupby(['c', 'd'])[['a','b']].apply(foo_function)
# transform doesn't work!
df.groupby(['c', 'd'])[['a','b']].transform(foo_function)
TypeError: cannot concatenate a non-NDFrame object
But transformapparently isn't able to combine multiple columns together because it looks at each column separately (unlike apply). What is the next best alternative in terms of speed / elegance? e.g. I could use applyand then create df['new_col']by using pd.match, but that would necessitate matching over sometimes multiple groupby columns (col1 and col2) which seems really hacky / would take a fair amount of code.
但transform显然无法将多列组合在一起，因为它分别查看每一列（与应用不同）。就速度/优雅而言，下一个最佳选择是什么？例如，我可以使用apply然后df['new_col']通过 using创建pd.match，但这有时需要匹配多个 groupby 列（col1 和 col2），这看起来真的很笨拙/需要大量代码。
--> Is there a function that is like groupby().transform that can use functions that work over multiple columns? If this doesn't exist, what's the best hack?
--> 有没有像 groupby().transform 这样的函数可以使用在多列上工作的函数？如果这不存在，最好的黑客是什么？
采纳答案by unutbu
Circa Pandas version 0.18, it appears the original answer (below) no longer works.
大约 Pandas 0.18 版，看来原始答案（如下）不再有效。
Instead, if you need to do a groupby computation across multiple columns, do the multi-column computation first, and then the groupby:
相反，如果您需要跨多列进行 groupby 计算，请先进行多列计算，然后进行 groupby：
df = pd.DataFrame({'a':[1,2,3,4,5,6],
'b':[1,2,3,4,5,6],
'c':['q', 'q', 'q', 'q', 'w', 'w'],
'd':['z','z','z','o','o','o']})
df['e'] = df['a'] + df['b']
df['e'] = (df.groupby(['c', 'd'])['e'].transform('sum'))
print(df)
yields
产量
a b c d e
0 1 1 q z 12
1 2 2 q z 12
2 3 3 q z 12
3 4 4 q o 8
4 5 5 w o 22
5 6 6 w o 22
Original answer:
原答案：
The error message:
错误信息：
TypeError: cannot concatenate a non-NDFrame object
suggests that in order to concatenate, the foo_functionshould return an NDFrame (such as a Series or DataFrame). If you return a Series, then:
建议为了连接，foo_function应该返回一个 NDFrame（例如 Series 或 DataFrame）。如果您返回一个系列，则：
In [99]: df.groupby(['c', 'd']).transform(lambda x: pd.Series(np.sum(x['a']+x['b'])))
Out[99]:
a b
0 12 12
1 12 12
2 12 12
3 8 8
4 22 22
5 22 22
回答by Victor Chubukov
The way I read the question, you want to be able to do something arbitrary with both the individual values from both columns. You just need to make sure to return a dataframe of the same size as you get passed in. I think the best way is to just make a new column, like this:
我阅读问题的方式是，您希望能够对两列中的两个单独值进行任意操作。您只需要确保返回与传入的大小相同的数据框。我认为最好的方法是创建一个新列，如下所示：
df = pd.DataFrame({'a':[1,2,3,4,5,6],
'b':[1,2,3,4,5,6],
'c':['q', 'q', 'q', 'q', 'w', 'w'],
'd':['z','z','z','o','o','o']})
df['e']=0
def f(x):
y=(x['a']+x['b'])/sum(x['b'])
return pd.DataFrame({'e':y,'a':x['a'],'b':x['b']})
df.groupby(['c','d']).transform(f)
:
：
a b e
0 1 1 0.333333
1 2 2 0.666667
2 3 3 1.000000
3 4 4 2.000000
4 5 5 0.909091
5 6 6 1.090909
If you have a very complicated dataframe, you can pick your columns (e.g. df.groupby(['c'])['a','b','e'].transform(f))
如果你有一个非常复杂的数据框，你可以选择你的列（例如df.groupby(['c'])['a','b','e'].transform(f)）
This sure looks very inelegant to me, but it's still much faster than applyon large datasets.
这对我来说确实看起来很不雅，但它仍然比apply大型数据集快得多。
Another alternative is to use set_indexto capture all the columns you need and then pass just one column to transform.
另一种选择是用于set_index捕获您需要的所有列，然后仅将一列传递给transform.
