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BUG: groupby.transform length mismatch under certain specifications #9697
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Note that
works, so the bug must be in the fastpath. |
More information: the use of We're also seeing things like
which makes me think there's definitely been a confusion about the index. |
yep, this https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L2456 |
Switching count -> size seems to fix the original problem. Doesn't pass the tests I made which revealed the index problem above. Shall I submit a PR with the count/size switch here and open a new issue for that one? |
@dsm054 that would be gr8 thxs |
I may be having the same problem in Pandas 0.16.1. I describe it here: I have a Pandas Dataframe with hierarchically indexed columns. Eg:
Giving:
However, when I attempt to normalize on level 1 of the columns as follows:
I get an error:
Weirdly, it works fine if I use up to 3 of the values of level 1 of axis 1, for example:
which just takes the first 3 sets of values from the sample:
Then:
correctly returns:
Any idea why the latter works and the former doesn't? I'm using Pandas version 0.16.1 |
|
I see, that does indeed work, thanks so much for the help. I am a bit confused by the documentation then. The docstring for transform says: Call function producing a like-indexed DataFrame on each group and So I assumed that transform would return a "transformed" dataframe for each group. In any case, now I think I have a better understanding of apply. Thanks again! |
yes, that the result of a transform. a user-defined function needs to return a single (scalar) value. hmm, maybe be better to have a nicer error message / doc. going to make an issue. |
Replicating Example (pandas 15.2 and 0.16.0rc1-32-g5a417ec):
Error:
Traceback (most recent call last):
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