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BUG: Series ops with a rhs of a Timestamp raising exception (#2898)#2899

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jreback merged 1 commit intopandas-dev:masterfromjreback:timedelta_issue
Feb 23, 2013

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jreback
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  • Series ops with a rhs of a Timestamp was throwing an exception (BUG: Series subtraction with Timestamp on rhs #2898)
  • fixed issue in _index.convert_scalar where the rhs was a non-scalar, lhs was dtype of M8[ns], and was trying
    to convert to a scalar (but didn't need conversion)
  • added some utilities in tslib.pyx, inference.pyx to detect and convert timedelta/timedelta64
  • added timedelta64 to checknull routines, representing timedelta64 as NaT
  • aded setitem support to set NaT via np.nan (analagously to datetime64)
  • added ability for Series to detect and set atimedelta64[ns] dtype (if all passed objects are timedelta like)
  • added correct printing of timedelta64[ns](looks like py3.3 changed the default for str%28x%29, so rolled a new one)
In [149]: from datetime import datetime, timedeltaIn [150]: s  = Series(date_range('2012-1-1', periods=3, freq='D'))In [151]: td = Series([ timedelta(days=i) for i in range(3) ])In [152]: df = DataFrame(dict(A = s, B = td))In [153]: dfOut[153]:                     A                B0 2012-01-01 00:00:00          0:00:001 2012-01-02 00:00:00   1 day, 0:00:002 2012-01-03 00:00:00  2 days, 0:00:00In [154]: df['C'] = df['A'] + df['B']In [155]: dfOut[155]:                     A                B                   C0 2012-01-01 00:00:00          0:00:00 2012-01-01 00:00:001 2012-01-02 00:00:00   1 day, 0:00:00 2012-01-03 00:00:002 2012-01-03 00:00:00  2 days, 0:00:00 2012-01-05 00:00:00In [156]: df.dtypesOut[156]: A     datetime64[ns]B    timedelta64[ns]C     datetime64[ns]Dtype: objectIn [60]: s - s.max()Out[60]: 0    -2 days, 0:00:001     -1 day, 0:00:002             0:00:00Dtype: timedelta64[ns]In [61]: s - datetime(2011,1,1,3,5)Out[61]: 0    364 days, 20:55:001    365 days, 20:55:002    366 days, 20:55:00Dtype: timedelta64[ns]In [62]: s + timedelta(minutes=5)Out[62]: 0   2012-01-01 00:05:001   2012-01-02 00:05:002   2012-01-03 00:05:00Dtype: datetime64[ns]In [160]: y = s - s.shift()In [161]: yOut[161]: 0              NaT1   1 day, 0:00:002   1 day, 0:00:00Dtype: timedelta64[ns]The can be set to NaT using np.nan analagously to datetimesIn [162]: y[1] = np.nanIn [163]: yOut[163]: 0              NaT1              NaT2   1 day, 0:00:00Dtype: timedelta64[ns]# works on lhs tooIn [64]: s.max() - sOut[64]: 0    2 days, 0:00:001     1 day, 0:00:002            0:00:00Dtype: timedelta64[ns]In [65]: datetime(2011,1,1,3,5) - sOut[65]: 0    -365 days, 3:05:001    -366 days, 3:05:002    -367 days, 3:05:00Dtype: timedelta64[ns]In [66]: timedelta(minutes=5) + sOut[66]: 0   2012-01-01 00:05:001   2012-01-02 00:05:002   2012-01-03 00:05:00Dtype: datetime64[ns]

@jreback
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anybody have an issue with the API change vaiunique, e.g. that it now returns aSeries rather than an ndarray?

@wesm
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I do. I would rather users usedrop_duplicates thanunique to achieve this

@jreback
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prob is I was using this with a datetime64[ns] columns
since it is not boxed as a ts, I think it's a bit unexpected (though correct)

@jreback
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all fixed up, restored unique behavior

fyi...py3 does funny things with np.timedelta64...., its auto converts them to int when you astype('O')
(which no other python version does!)

@jreback
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@y-p when you have a chance, can you take a look at test_timedelta in tests/test_format.py. I had to put in my own formatter to handle NaT and py3.3 change in the way printing works for timedelta64. It works and returns a string. Not sure if I need to deal with unicode in any way? thanks

@ghost
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you're returning a string tha always contains pure ascii characters,
that should be ok.
python2 would try to convert that to unicode as needed using the system
encoding, and that should succeed on all encodings I've seen.

@jreback
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great thanks

@jreback
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@wesm any problem with merging this in?

     unique of a Series now returns a Series     _index.convert_scalar now will ignore a ndarray object if the lhs is timelike        (eg a non-scalar that is a series/ndarray is passed)BUG: py3 issue with np.datetime64 conversion     added array_timedelta_to_int conversions in tslib.pyx     removed unique changesDOC: added docs to timeseries for timedeltas and v0.11.0 whatsnew     minor fixing of doc refernce in v0.10.0     updated RELASE.rstENH: added ability for Series to set its dtype if it detects all timedelta like objectsENH: added null checking and NaT support for timedelta64     added formatter for timedelta64     added setting via np.nan for NaT values (similar to datetime64[ns] support)FMT: fixed timedelta64[ns] formatting (was breaking on py3.3)     had to roll a new printer (repr_timedelta64) rather than use     default str(x)ENH: increased robustness in detection/conversion of timedelta64     that are intermixed with np.nan,NaT,iNaT
jreback added a commit that referenced this pull requestFeb 23, 2013
BUG: Series ops with a rhs of a Timestamp raising exception (#2898)
@jrebackjreback merged commite9b9ca5 intopandas-dev:masterFeb 23, 2013
@ghost
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Putting example from docs here, for readers of RELEASE.txt

  - Timedeltas are now fully operational (closes GH2898_)    .. ipython:: python        from datetime import datetime, timedelta        s  = Series(date_range('2012-1-1', periods=3, freq='D'))        td = Series([ timedelta(days=i) for i in range(3) ])        df = DataFrame(dict(A = s, B = td))        df        s - s.max()        df['C'] = df['A'] + df['B']        df        df.dtypes        # timedelta are representas as ``NaT``        y = s - s.shift()    y    # can be set via ``np.nan``    y[1] = np.nan    y        # works on lhs too    s.max() - s        # some timedelta numeric operations are supported        td - timedelta(minutes=5,seconds=5,microseconds=5)

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