pandas 循环分组数据框中的组
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Looping over groups in a grouped dataframe
提问by Rockbar
Consider this small example:
考虑这个小例子：
data={"X":[1, 2, 3, 4, 5], "Y":[6, 7, 8, 9, 10], "Z": [11, 12, 13, 14, 15])
frame=pd.DataFrame(data,columns=["X","Y","Z"],index=["A","A","A","B","B"])
I want to group framewith
我想frame与
grouped=frame.groupby(frame.index)
Then I want to loop over the groups by:
然后我想通过以下方式遍历组：
for group in grouped:
But I'm stuck on the next step: How can I extract the groupin each loop as a pandas DataFrame so I can further process it?
但我坚持下一步：如何将group每个循环中的数据提取为Pandas数据帧，以便我可以进一步处理它？
采纳答案by cs95
df.groupbyreturns a list of 2-tuples: the index, and the group. You can iterate over each group like this:
df.groupby返回一个 2 元组列表：索引和组。您可以像这样迭代每个组：
for _, g in frame.groupby(frame.index):
.... # do something with `g`
However, if you want to perform some operation on the groups, there are probably better ways than iteration.
但是，如果要对组执行某些操作，可能有比迭代更好的方法。
回答by Scott Boston
Here is an example:
下面是一个例子：
groups = frame.groupby(level=0)
for n,g in groups:
print('This is group '+ str(n)+'.')
print(g)
print('\n')
Output:
输出：
This is group A.
X Y Z
A 1 6 11
A 2 7 12
A 3 8 13
This is group B.
X Y Z
B 4 9 14
B 5 10 15
