循环访问分组数据框中的组 [英] Looping over groups in a grouped dataframe

问题描述

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"])

我要与frame分组

grouped=frame.groupby(frame.index)

然后我要通过以下方式遍历组:

Then I want to loop over the groups by:

for group in grouped:

但是我坚持下一步:如何在每个循环中将group提取为pandas DataFrame，以便我可以对其进行进一步处理?

But I'm stuck on the next step: How can I extract the group in each loop as a pandas DataFrame so I can further process it?

推荐答案

df.groupby返回2元组的列表:索引和组.您可以像这样遍历每个组:

df.groupby returns a list of 2-tuples: the index, and the group. You can iterate over each group like this:

for _, g in frame.groupby(frame.index):
    .... # do something with `g`

但是，如果要对组执行某些操作，可能有比迭代更好的方法.

However, if you want to perform some operation on the groups, there are probably better ways than iteration.

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