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Make Series[datetime64] - pd.NaT behave like DatetimeIndex - pd.NaT #18960

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Make Series[datetime64] - pd.NaT behave like DatetimeIndex - pd.NaT #18960

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d4b799a
handle tzs, remove tests for old behavior
jbrockmendel Dec 19, 2017
c07f49a
Merge branch 'master' of https://github.com/pandas-dev/pandas into su…
jbrockmendel Dec 27, 2017
49ed387
use isna instead of np.isnat
jbrockmendel Dec 27, 2017
782ea74
Merge branch 'master' of https://github.com/pandas-dev/pandas into su…
jbrockmendel Dec 28, 2017
5540a4b
Merge branch 'master' of https://github.com/pandas-dev/pandas into su…
jbrockmendel Dec 30, 2017
e99b5e1
Merge branch 'master' of https://github.com/pandas-dev/pandas into su…
jbrockmendel Dec 31, 2017
f0da8ec
adhere to convention
jbrockmendel Dec 31, 2017
f275a6c
make code 'simpler'
jbrockmendel Dec 31, 2017
9ac970d
revert simplification
jbrockmendel Dec 31, 2017
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.23.0.txt
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Expand Up @@ -208,6 +208,7 @@ Other API Changes
- In :func:`read_excel`, the ``comment`` argument is now exposed as a named parameter (:issue:`18735`)
- Rearranged the order of keyword arguments in :func:`read_excel()` to align with :func:`read_csv()` (:issue:`16672`)
- The options ``html.border`` and ``mode.use_inf_as_null`` were deprecated in prior versions, these will now show ``FutureWarning`` rather than a ``DeprecationWarning`` (:issue:`19003`)
- Subtracting ``NaT`` from a :class:`Series` with ``dtype='datetime64[ns]'`` returns a ``Series`` with ``dtype='timedelta64[ns]'`` instead of ``dtype='datetime64[ns]'``(:issue:`18808`)

.. _whatsnew_0230.deprecations:

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18 changes: 14 additions & 4 deletions pandas/core/ops.py
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Expand Up @@ -407,8 +407,12 @@ def _validate_datetime(self, lvalues, rvalues, name):

# if tz's must be equal (same or None)
if getattr(lvalues, 'tz', None) != getattr(rvalues, 'tz', None):
raise ValueError("Incompatible tz's on datetime subtraction "
"ops")
if len(rvalues) == 1 and isna(rvalues).all():
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why does this matter if it is exactly len == 1, seems odd

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The issue at hand is that pd.NaT is special because it can play the role of a datetime or a timedelta depending on context. _TimeOp wraps scalar inputs in an array that becomes rvalues. But at a basic level here what we are interested in is "was the original right argument pd.NaT?" What this is doing (and the same you're asking me to do below) is to undo wrapping done by _TimeOp. It is a much less clear way of checking "is the argument pd.NaT?"

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you didn't answer the question, whether I have 1 NaT or all NaT is immaterial, I cann't use the rhs dtype as its ambiguous and must use the left.

# NaT gets a pass
pass
else:
raise ValueError("Incompatible tz's on datetime "
"subtraction ops", rvalues)

else:
raise TypeError('cannot operate on a series without a rhs '
Expand Down Expand Up @@ -505,12 +509,18 @@ def _convert_to_array(self, values, name=None, other=None):
inferred_type = lib.infer_dtype(values)
if (inferred_type in ('datetime64', 'datetime', 'date', 'time') or
is_datetimetz(inferred_type)):

# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
if (supplied_dtype is None and other is not None and
(other.dtype in ('timedelta64[ns]', 'datetime64[ns]')) and
(other.dtype in ('timedelta64[ns]', 'datetime64[ns]')) and
isna(values).all()):
values = np.empty(values.shape, dtype='timedelta64[ns]')
if len(values) == 1 and other.dtype == 'timedelta64[ns]':
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this is not simpler at all.

values = np.empty(values.shape, dtye=other.dtype)
values[:] = iNaT

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I agree, hence the sarcasm quotes and the simpler original implementation.

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Yah, even then this is wrong because it isn't conditioning on the op being sub. Gonna revert back to how it was before this commit.

values = np.empty(values.shape, dtype='timedelta64[ns]')
elif len(values) == 1 and other.dtype == 'datetime64[ns]':
values = np.empty(values.shape, dtype='datetime64[ns]')
else:
values = np.empty(values.shape, dtype='timedelta64[ns]')
values[:] = iNaT

# a datelike
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24 changes: 21 additions & 3 deletions pandas/tests/series/test_operators.py
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Expand Up @@ -960,6 +960,13 @@ def test_timedelta64_ops_nat(self):
assert_series_equal(timedelta_series / nan,
nat_series_dtype_timedelta)

def test_td64_sub_NaT(self):
# GH#18808
ser = Series([NaT, Timedelta('1s')])
res = ser - NaT
expected = Series([NaT, NaT], dtype='timedelta64[ns]')
tm.assert_series_equal(res, expected)

@pytest.mark.parametrize('scalar_td', [timedelta(minutes=5, seconds=4),
Timedelta(minutes=5, seconds=4),
Timedelta('5m4s').to_timedelta64()])
Expand Down Expand Up @@ -1224,13 +1231,10 @@ def test_datetime64_ops_nat(self):
single_nat_dtype_datetime = Series([NaT], dtype='datetime64[ns]')

# subtraction
assert_series_equal(datetime_series - NaT, nat_series_dtype_timestamp)
assert_series_equal(-NaT + datetime_series, nat_series_dtype_timestamp)
with pytest.raises(TypeError):
-single_nat_dtype_datetime + datetime_series

assert_series_equal(nat_series_dtype_timestamp - NaT,
nat_series_dtype_timestamp)
assert_series_equal(-NaT + nat_series_dtype_timestamp,
nat_series_dtype_timestamp)
with pytest.raises(TypeError):
Expand Down Expand Up @@ -1263,6 +1267,20 @@ def test_datetime64_ops_nat(self):
with pytest.raises(TypeError):
nat_series_dtype_timestamp / 1

def test_dt64_sub_NaT(self):
# GH#18808
dti = pd.DatetimeIndex([pd.NaT, pd.Timestamp('19900315')])
ser = pd.Series(dti)
res = ser - pd.NaT
expected = pd.Series([pd.NaT, pd.NaT], dtype='timedelta64[ns]')
tm.assert_series_equal(res, expected)

dti_tz = dti.tz_localize('Asia/Tokyo')
ser_tz = pd.Series(dti_tz)
res = ser_tz - pd.NaT
expected = pd.Series([pd.NaT, pd.NaT], dtype='timedelta64[ns]')
tm.assert_series_equal(res, expected)


class TestSeriesOperators(TestData):
def test_op_method(self):
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