BUG: Series.argsort() fails with datetime64[ns] with NaT / dtypes are odd #2967
Description
pretty simple fix though, just need to make sure that the result array
is typed float64 (to accomodate the nans) or int64 (if no nans)
(rather than the same as input type)
(which could give weird results in some cases), e.g. you wouldn't
want a float array back just because you fed it nans....
heres the inspiration question
http://stackoverflow.com/questions/15207279/return-sorted-indexes-skipping-nan-values-in-pandas
In [29]: s = pd.Series([pd.Timestamp('201301%02d'% (i+1)) for i in range(5)])
In [30]: s
Out[30]:
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
3 2013-01-04 00:00:00
4 2013-01-05 00:00:00
dtype: datetime64[ns]
In [31]: s.argsort()
Out[31]:
0 0
1 1
2 2
3 3
4 4
dtype: int64
In [32]: s.shift().argsort()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-32-61a586f08c06> in <module>()
----> 1 s.shift().argsort()
/mnt/home/jreback/pandas/pandas/core/series.pyc in argsort(self, axis, kind, order)
2119 result = values.copy()
2120 notmask = -mask
-> 2121 result[notmask] = np.argsort(values[notmask], kind=kind)
2122 return Series(result, index=self.index, name=self.name)
2123 else:
TypeError: array cannot be safely cast to required type