DateTimeIndex appending with loc inconsistency in handling numpy datetime64

[dashesy]

This is in Pandas 0.15.2 (but I also tried it on '0.15.2-148-g484f668'):

df = pd.DataFrame()
df.loc[np.datetime64(datetime.datetime.now()),'one'] = 100
df.loc[np.datetime64(datetime.datetime.now()),'one'] = 100

This is the output:

                               one
2015-02-18 14:50:05.606510     100
1970-01-17 11:37:51.007748755  100

It seems the the first usage of loc correctly works around limitations of datetime64 but I cannot explain the second one.

[jnmclarty]

I had a quick peek. Inside _NDFrameIndexer._setitem_with_indexer(), this line here: self.obj._data = self.obj.reindex_axis(labels, i)._data is where the labels go from good to bad. The first reference to labels inside NDFrame.reindex_axis(...) is erroneous...

This code might help somebody more senior than I:

from pandas import DataFrame
from numpy import datetime64 as d64
from datetime import datetime as dt

df = DataFrame()

df.loc[d64(dt(2015,1,1,1,1,1)),'C1'] = 'GOOD'
df.loc[d64(dt(2015,1,1,1,1,1)),'C1'] = 'BAD'
df.loc[d64(dt(2015,1,1,1,1,2)),'C1'] = 'sec'
df.loc[d64(dt(2015,1,1,1,2,1)),'C1'] = 'min'
df.loc[d64(dt(2015,1,1,2,1,1)),'C1'] = 'hr'
df.loc[d64(dt(2015,1,2,1,1,1)),'C1'] = 'day'
df.loc[d64(dt(2015,2,1,1,1,1)),'C1'] = 'mth'

>>> df
                             C1
2015-01-01 01:01:01         BAD
1970-01-17 10:27:54.062000  sec
1970-01-17 10:27:54.121000  min
1970-01-17 10:27:57.661000   hr
1970-01-17 10:29:20.461000  day
1970-01-17 11:12:32.461000  mth

Notice the date component dropped, And the month/day/hour/minute/second specified is incrementing hour/minute/... respectively. Also notice that "GOOD" is over-written, by "BAD".

[jreback]

@jnmclarty this is now fixed in master

[jnmclarty]

@jreback Yah, saw that. Good stuff.