Pandas gives integer from datetime index with item()

Question

I have 2 dataframes, taken from a larger frame (with df.head(x) ), both with the same index:

print df
                     val
DT                                                                                                      
2017-03-06 00:00:00  1.06207
2017-03-06 00:02:00  1.06180
2017-03-06 00:04:00  1.06167
2017-03-06 00:06:00  1.06141
2017-03-06 00:08:00  1.06122
...                      ...
2017-03-10 21:50:00  1.06719
2017-03-10 21:52:00  1.06719
2017-03-10 21:54:00  1.06697
2017-03-10 21:56:00  1.06713
2017-03-10 21:58:00  1.06740

a and b are then taken from df

print a.index
print b.index

DatetimeIndex(['2017-03-06 00:32:00'], dtype='datetime64[ns]', name=u'DT', freq=None)
DatetimeIndex(['2017-03-06 00:18:00'], dtype='datetime64[ns]', name=u'DT', freq=None)

But, when I use a.index.item(), I get it in the format 1488759480000000000. That means when I go to take a slice from df based on a and b , I get an empty dataframe

>>> df[a.index.item() : b.index.item()]
Empty DataFrame

and further to that, when I try to convert them both:

df[a.index.to_pydatetime() : b.index.to_pydatetime()]

TypeError: Cannot convert input [[datetime.datetime(2017, 3, 6, 0, 18)]] of type <type 'numpy.ndarray'> to Timestamp

This is infuriating, surely there should be continuity of objects when using item(). Can anyone give me some pointers?

@jezrael TypeError: cannot do slice indexing on with these indexers [1488759480000000000] of — ajsp, Jul 20 '19 at 12:27

jezrael · Accepted Answer · 2019-07-20T12:34:14.697

1

You can use loc with first value of a and b:

df.loc[a.index[0] : b.index[0]]

Your solution working if convert to Timestamp:

print (df.loc[pd.Timestamp(a.index.item()): pd.Timestamp(b.index.item())])

edited Jul 20 '19 at 12:34

answered Jul 20 '19 at 12:27

jezrael

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Pandas gives integer from datetime index with item()

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