BUG: groupby segfaults when passed a function of a timestamp which raises a TypeError

[dhirschfeld]

It is a dumb thing to do, but pandas probably shouldn't segfault regardless:

In [1]: dates = pd.date_range('01-Jan-2013', periods=12, freq='MS')
   ...: ts = pd.TimeSeries(randn(12), index=dates)

In [2]: ts.groupby(lambda key: key[0:4]).first()
It seems the kernel died unexpectedly. Use 'Restart kernel' to continue using this console.

Tested with pandas 10.1 on win32/win64, Python 2.7 and pandas 0.11.0.dev-2f7b0e4 on win32, Python 2.7.

[ghost]

Short answer: added a fix to #3031.

In [11]: N=12
    ...: dates = pd.date_range('01-Jan-2013', periods=N, freq='MS')
    ...: ts = pd.TimeSeries(randn(N), index=dates)
    ...: ts.groupby(lambda key: key[0:4]).first()
---------------------------------------------------------------------------

AssertionError: Grouper result violates len(labels) == len(data)
result: [2013-01-01 00:00:00, 2013-02-01 00:00:00, 2013-03-01 00:00:00, 2013-04-01 00:00:00]

Very long answer:
I think this is a case where pandas is too clever by half.

When your grouper is a function, it's applied using index.map.
index.map tries really hard to guess what you want, and so if the function
throws an exception when applied to the elements, it's tried again on the entire
index as a sequence and the result is used as the grouping result.

Presumably, lambda key: str(key)[0:4] is what you wanted, but you're using key[0:4] when
key is a Timestamp, so that failes, the fallback path is triggered and you end
up getting the first 4 elements of the index, rather then a sequence of the first
4 chars of each element.
That produces a label/data len mismatch, which is uncaught by the cython code
down the road, which borks (similar to #3011 from a few days ago, but a different code path).

The fix in #3011, still doesn't address the corner case where an erroneous lambda throws an exception
on the element, but returns a result of the correct length when applied to the entire index:

In [17]: N=5
    ...: dates = pd.date_range('01-Jan-2013', periods=N, freq='MS')
    ...: ts = pd.TimeSeries(randn(N), index=dates)
    ...: ts.groupby(lambda key: key[0:len(ts.index)]).count()
Out[17]: 
2013-01-01    1
2013-02-01    1
2013-03-01    1
2013-04-01    1
2013-05-01    1
dtype: int64

compare with lambda key: str(key)[0:len(ts.index)]:

In [16]: N=5
    ...: dates = pd.date_range('01-Jan-2013', periods=N, freq='MS')
    ...: ts = pd.TimeSeries(randn(N), index=dates)
    ...: ts.groupby(lambda key: str(key)[0:len(ts.index)]).count()
Out[16]: 
2013-    5
dtype: int64

That could give someone a nasty surprise.

Comments in the code suggest that in the hands of a trained professional
this is a feature, not a bug. well, ok then.

EDIT: this behaviour is specific to DatetimeIndex, and it's actually the other way around, first
the index itself is tried, then each element, code

[ghost]

fixed in master.