I'm trying to take the min and max of a couple Pandas Series objects in the face of NaT. np.minimum and np.maximum work the way I want if the elements are floats. For example:
>>> s1 0 0.0 1 1.8 2 3.6 3 5.4 dtype: float64 >>> s2 0 10.0 1 17.0 2 NaN 3 14.0 dtype: float64 >>> np.maximum(s1, s2) 0 10.0 1 17.0 2 NaN 3 14.0 dtype: float64 >>> np.minimum(s1, s2) 0 0.0 1 1.8 2 NaN 3 5.4 dtype: float64 This doesn't work if s1 and s2 are datetime64[ns] objects: >>> s1 0 2199-12-31 1 2199-12-31 2 2199-12-31 3 2199-12-31 dtype: datetime64[ns] >>> s2 0 NaT 1 2018-10-30 2 NaT 3 NaT dtype: datetime64[ns] >>> np.maximum(s1, s2) 0 2199-12-31 1 2199-12-31 2 2199-12-31 3 2199-12-31 dtype: datetime64[ns] >>> np.minimum(s1, s2) 0 2199-12-31 1 2018-10-30 2 2199-12-31 3 2199-12-31 dtype: datetime64[ns] After doing a bit of reading, I came to realize NaT is only approximately NaN, the latter having a proper floating point representation. Further reading suggested no simple way to have NaT "pollute" these comparisons. What's the correct way to get NaT to propagate in min/max comparisons the way NaN does in a floating point context? Thanks, Skip -- https://mail.python.org/mailman/listinfo/python-list
