BUG: nanops on empty Series have wrong type

[ischwabacher]

In [1]: import pandas as pd

In [2]: pd.Series(dtype='m8[ns]').sum()
Out[2]: 0

In [3]: pd.Series(dtype='m8[ns]').mean()
Out[3]: 
0   NaT
dtype: timedelta64[ns]

In [4]: pd.Series(dtype='m8[ns]').median()
Out[4]: nan

In [5]: pd.Series(dtype=float).sum()
Out[5]: 0

In [6]: pd.Series([0], dtype=float).sum()
Out[6]: 0.0

It looks like _wrap_results is doing double duty, both converting an output to the right time dtype, and wrapping that result in a series (I don't know why it does this), and in some cases results aren't getting wrapped. But the wrapper ignores other dtypes, which doesn't make sense to me; aside from the unfortunate fact that numpy provides no _NA_integer equivalent, I as a user expect the dtype of a result to depend only on the dtype of the inputs, not on their shapes or values, so it would make sense to have a more general wrapper somewhere.

I'm not sure how I'd implement such a wrapper, because ideally it would be able to handle situations like that the standard deviation of a Series of datetime64s is a timedelta64 or such. Maybe that's a bit ambitious, though, since it's inching towards a general notion of symbolic units; in such a case the user can unwrap, operate and wrap manually.

It looks like there are several points in pandas/core/nanops.py where a result is returned without wrapping, and I could submit a narrow PR for just this issue but I want to wait until I have time to really go over nanops.py and get all of these.

[jreback]

This is just an edge case in creation of the empty array.

as you can see these work correctly (just not on an empty array).

In [12]: pd.to_timedelta(np.arange(5),unit='s')
Out[12]: 
0   00:00:00
1   00:00:01
2   00:00:02
3   00:00:03
4   00:00:04
dtype: timedelta64[ns]

In [13]: pd.to_timedelta(np.arange(5),unit='s').sum()
Out[13]: 
0   00:00:10
dtype: timedelta64[ns]

In [14]: pd.to_timedelta(np.arange(5),unit='s').mean()
Out[14]: 
0   00:00:02
dtype: timedelta64[ns]

In [15]: pd.to_timedelta(np.arange(5),unit='s').median()
Out[15]: 
0   00:00:02
dtype: timedelta64[ns]

[jreback]

tests for these types of things are in tseries/tests/tests_timeseries and/or tseries/test.tests_timedeltas

[ischwabacher]

I still don't get why these tests don't belong in pandas/pandas/tests/test_nanops.py.

[jreback]

nanops only deals with very low level things. eg.. it knows nothing about wrapping the return results.

[ischwabacher]

The thing that's confusing me is that _wrap_results is in nanops.py, and when I run this operation through pdb, I don't see any wrapping code other than _wrap_results. It's always possible that I ned past a decorator that did the wrapping, but Substitution doesn't seem to have the right signature to do the job, and I think that and functools.wraps are the only decorators I didn't check.

[ischwabacher]

Edited title and code sample to more accurately reflect the issue.

[ischwabacher]

I was going back over this and noticed that _wrap_results contains this comment explaining why scalar timedelta64 results of reduction operations get wrapped in length-1 Series:

this is a scalar timedelta result!

we have series convert then take the element (scalar)

as series will do the right thing in py3 (and deal with numpy

1.6.2 bug in that it results dtype of timedelta64[us]

I don't know what the py3 issue is, but since we're dropping numpy <1.7 compatibility, can we get rid of this behavior? Having to strip the wrapper from a timedelta64 result but not other results causes a lot of friction and encourages using the "wrong" representation just to avoid having separate code paths.