Test failures with pandas master

[dcherian]

https://dev.azure.com/xarray/xarray/_build/results?buildId=1859&view=logs&jobId=41d90575-019f-5cfd-d78e-c2adebf9a30b&j=41d90575-019f-5cfd-d78e-c2adebf9a30b

2020-01-07T19:19:39.0297443Z =================================== FAILURES ===================================
2020-01-07T19:19:39.0495451Z _________________ test_timedeltaindex_add_cftimeindex[365_day] _________________
2020-01-07T19:19:39.0496702Z 
2020-01-07T19:19:39.0498391Z calendar = '365_day'
2020-01-07T19:19:39.0499052Z 
2020-01-07T19:19:39.0500036Z     @requires_cftime
2020-01-07T19:19:39.0500669Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0501986Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0502556Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0503384Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0503635Z         result = deltas + a
2020-01-07T19:19:39.0504330Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0505283Z         assert result.equals(expected)
2020-01-07T19:19:39.0506265Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0507151Z E       AssertionError: assert False
2020-01-07T19:19:39.0508329Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)
2020-01-07T19:19:39.0509344Z 
2020-01-07T19:19:39.0510351Z xarray/tests/test_cftimeindex.py:724: AssertionError
2020-01-07T19:19:39.0510935Z _________________ test_timedeltaindex_add_cftimeindex[360_day] _________________
2020-01-07T19:19:39.0511748Z 
2020-01-07T19:19:39.0513201Z calendar = '360_day'
2020-01-07T19:19:39.0513958Z 
2020-01-07T19:19:39.0514197Z     @requires_cftime
2020-01-07T19:19:39.0515571Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0517319Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0517575Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0517798Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0519155Z         result = deltas + a
2020-01-07T19:19:39.0519385Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0519619Z         assert result.equals(expected)
2020-01-07T19:19:39.0520075Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0520252Z E       AssertionError: assert False
2020-01-07T19:19:39.0520850Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)
2020-01-07T19:19:39.0521907Z 
2020-01-07T19:19:39.0522818Z xarray/tests/test_cftimeindex.py:724: AssertionError
2020-01-07T19:19:39.0523212Z _________________ test_timedeltaindex_add_cftimeindex[julian] __________________
2020-01-07T19:19:39.0524673Z 
2020-01-07T19:19:39.0525157Z calendar = 'julian'
2020-01-07T19:19:39.0525992Z 
2020-01-07T19:19:39.0526232Z     @requires_cftime
2020-01-07T19:19:39.0526834Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0531142Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0531602Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0531897Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0532131Z         result = deltas + a
2020-01-07T19:19:39.0532351Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0532597Z         assert result.equals(expected)
2020-01-07T19:19:39.0532824Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0533279Z E       AssertionError: assert False
2020-01-07T19:19:39.0534006Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)
2020-01-07T19:19:39.0534981Z 
2020-01-07T19:19:39.0535242Z xarray/tests/test_cftimeindex.py:724: AssertionError
2020-01-07T19:19:39.0536707Z ________________ test_timedeltaindex_add_cftimeindex[all_leap] _________________
2020-01-07T19:19:39.0537052Z 
2020-01-07T19:19:39.0538274Z calendar = 'all_leap'
2020-01-07T19:19:39.0538679Z 
2020-01-07T19:19:39.0538864Z     @requires_cftime
2020-01-07T19:19:39.0539011Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0539145Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0539272Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0539413Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0539531Z         result = deltas + a
2020-01-07T19:19:39.0539644Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0539783Z         assert result.equals(expected)
2020-01-07T19:19:39.0539916Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0540048Z E       AssertionError: assert False
2020-01-07T19:19:39.0540781Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)
2020-01-07T19:19:39.0540948Z 
2020-01-07T19:19:39.0541089Z xarray/tests/test_cftimeindex.py:724: AssertionError
2020-01-07T19:19:39.0541346Z _________________ test_timedeltaindex_add_cftimeindex[366_day] _________________
2020-01-07T19:19:39.0541454Z 
2020-01-07T19:19:39.0542123Z calendar = '366_day'
2020-01-07T19:19:39.0542355Z 
2020-01-07T19:19:39.0542476Z     @requires_cftime
2020-01-07T19:19:39.0542613Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0542733Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0542852Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0543006Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0543123Z         result = deltas + a
2020-01-07T19:19:39.0543239Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0543372Z         assert result.equals(expected)
2020-01-07T19:19:39.0543499Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0543616Z E       AssertionError: assert False
2020-01-07T19:19:39.0544641Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)
2020-01-07T19:19:39.0545191Z 
2020-01-07T19:19:39.0545399Z xarray/tests/test_cftimeindex.py:724: AssertionError
2020-01-07T19:19:39.0545554Z ________________ test_timedeltaindex_add_cftimeindex[gregorian] ________________
2020-01-07T19:19:39.0545679Z 
2020-01-07T19:19:39.0546078Z calendar = 'gregorian'
2020-01-07T19:19:39.0546251Z 
2020-01-07T19:19:39.0546622Z     @requires_cftime
2020-01-07T19:19:39.0546774Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0546940Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0547086Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0547379Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0547529Z         result = deltas + a
2020-01-07T19:19:39.0547669Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0547989Z         assert result.equals(expected)
2020-01-07T19:19:39.0548101Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0548210Z E       AssertionError: assert False
2020-01-07T19:19:39.0548869Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)
2020-01-07T19:19:39.0549023Z 
2020-01-07T19:19:39.0549154Z xarray/tests/test_cftimeindex.py:724: AssertionError
2020-01-07T19:19:39.0549296Z ___________ test_timedeltaindex_add_cftimeindex[proleptic_gregorian] ___________
2020-01-07T19:19:39.0549400Z 
2020-01-07T19:19:39.0549680Z calendar = 'proleptic_gregorian'
2020-01-07T19:19:39.0550189Z 
2020-01-07T19:19:39.0550314Z     @requires_cftime
2020-01-07T19:19:39.0550430Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0550568Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0550686Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0550806Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0550946Z         result = deltas + a
2020-01-07T19:19:39.0551062Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0551198Z         assert result.equals(expected)
2020-01-07T19:19:39.0551337Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0551452Z E       AssertionError: assert False
2020-01-07T19:19:39.0552276Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)
2020-01-07T19:19:39.0552425Z 
2020-01-07T19:19:39.0552548Z xarray/tests/test_cftimeindex.py:724: AssertionError
2020-01-07T19:19:39.0552973Z ____ test_cf_datetime_nan[num_dates1-days since 2000-01-01-expected_list1] _____
2020-01-07T19:19:39.0553281Z 
2020-01-07T19:19:39.0553765Z num_dates = [nan, 0], units = 'days since 2000-01-01'
2020-01-07T19:19:39.0554887Z expected_list = ['NaT', '2000-01-01T00:00:00Z']
2020-01-07T19:19:39.0555104Z 
2020-01-07T19:19:39.0555251Z     @arm_xfail
2020-01-07T19:19:39.0555406Z     @requires_cftime
2020-01-07T19:19:39.0555551Z     @pytest.mark.parametrize(
2020-01-07T19:19:39.0555693Z         ["num_dates", "units", "expected_list"],
2020-01-07T19:19:39.0555849Z         [
2020-01-07T19:19:39.0556225Z             ([np.nan], "days since 2000-01-01", ["NaT"]),
2020-01-07T19:19:39.0556674Z             ([np.nan, 0], "days since 2000-01-01", ["NaT", "2000-01-01T00:00:00Z"]),
2020-01-07T19:19:39.0556881Z             (
2020-01-07T19:19:39.0557026Z                 [np.nan, 0, 1],
2020-01-07T19:19:39.0557374Z                 "days since 2000-01-01",
2020-01-07T19:19:39.0558208Z                 ["NaT", "2000-01-01T00:00:00Z", "2000-01-02T00:00:00Z"],
2020-01-07T19:19:39.0558355Z             ),
2020-01-07T19:19:39.0558466Z         ],
2020-01-07T19:19:39.0558591Z     )
2020-01-07T19:19:39.0559984Z     def test_cf_datetime_nan(num_dates, units, expected_list):
2020-01-07T19:19:39.0560153Z         with warnings.catch_warnings():
2020-01-07T19:19:39.0560559Z             warnings.filterwarnings("ignore", "All-NaN")
2020-01-07T19:19:39.0560733Z             actual = coding.times.decode_cf_datetime(num_dates, units)
2020-01-07T19:19:39.0561076Z         # use pandas because numpy will deprecate timezone-aware conversions
2020-01-07T19:19:39.0561235Z         expected = pd.to_datetime(expected_list)
2020-01-07T19:19:39.0561532Z >       assert_array_equal(expected, actual)
2020-01-07T19:19:39.0561669Z E       AssertionError: 
2020-01-07T19:19:39.0561785Z E       Arrays are not equal
2020-01-07T19:19:39.0561899Z E       
2020-01-07T19:19:39.0562138Z E       Mismatched elements: 2 / 2 (100%)
2020-01-07T19:19:39.0562505Z E        x: array([NaT, Timestamp('2000-01-01 00:00:00+0000', tz='UTC')], dtype=object)
2020-01-07T19:19:39.0563375Z E        y: array([                          'NaT', '2000-01-01T00:00:00.000000000'],
2020-01-07T19:19:39.0563710Z E             dtype='datetime64[ns]')
2020-01-07T19:19:39.0564285Z 
2020-01-07T19:19:39.0564914Z xarray/tests/test_coding_times.py:455: AssertionError
2020-01-07T19:19:39.0565686Z ____ test_cf_datetime_nan[num_dates2-days since 2000-01-01-expected_list2] _____
2020-01-07T19:19:39.0565858Z 
2020-01-07T19:19:39.0566229Z num_dates = [nan, 0, 1], units = 'days since 2000-01-01'
2020-01-07T19:19:39.0566709Z expected_list = ['NaT', '2000-01-01T00:00:00Z', '2000-01-02T00:00:00Z']
2020-01-07T19:19:39.0566894Z 
2020-01-07T19:19:39.0567039Z     @arm_xfail
2020-01-07T19:19:39.0567178Z     @requires_cftime
2020-01-07T19:19:39.0567320Z     @pytest.mark.parametrize(
2020-01-07T19:19:39.0567486Z         ["num_dates", "units", "expected_list"],
2020-01-07T19:19:39.0567660Z         [
2020-01-07T19:19:39.0568035Z             ([np.nan], "days since 2000-01-01", ["NaT"]),
2020-01-07T19:19:39.0568760Z             ([np.nan, 0], "days since 2000-01-01", ["NaT", "2000-01-01T00:00:00Z"]),
2020-01-07T19:19:39.0569295Z             (
2020-01-07T19:19:39.0569436Z                 [np.nan, 0, 1],
2020-01-07T19:19:39.0569737Z                 "days since 2000-01-01",
2020-01-07T19:19:39.0570253Z                 ["NaT", "2000-01-01T00:00:00Z", "2000-01-02T00:00:00Z"],
2020-01-07T19:19:39.0570426Z             ),
2020-01-07T19:19:39.0570560Z         ],
2020-01-07T19:19:39.0570962Z     )
2020-01-07T19:19:39.0571083Z     def test_cf_datetime_nan(num_dates, units, expected_list):
2020-01-07T19:19:39.0571247Z         with warnings.catch_warnings():
2020-01-07T19:19:39.0571569Z             warnings.filterwarnings("ignore", "All-NaN")
2020-01-07T19:19:39.0571732Z             actual = coding.times.decode_cf_datetime(num_dates, units)
2020-01-07T19:19:39.0572075Z         # use pandas because numpy will deprecate timezone-aware conversions
2020-01-07T19:19:39.0572306Z         expected = pd.to_datetime(expected_list)
2020-01-07T19:19:39.0572449Z >       assert_array_equal(expected, actual)
2020-01-07T19:19:39.0572573Z E       AssertionError: 
2020-01-07T19:19:39.0572694Z E       Arrays are not equal
2020-01-07T19:19:39.0572812Z E       
2020-01-07T19:19:39.0572933Z E       Mismatched elements: 3 / 3 (100%)
2020-01-07T19:19:39.0573443Z E        x: array([NaT, Timestamp('2000-01-01 00:00:00+0000', tz='UTC'),
2020-01-07T19:19:39.0573826Z E              Timestamp('2000-01-02 00:00:00+0000', tz='UTC')], dtype=object)
2020-01-07T19:19:39.0575539Z E        y: array([                          'NaT', '2000-01-01T00:00:00.000000000',
2020-01-07T19:19:39.0576050Z E              '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
2020-01-07T19:19:39.0576213Z 
2020-01-07T19:19:39.0576721Z xarray/tests/test_coding_times.py:455: AssertionError

[spencerkclark]

So there are two classes of failures here:

1. With pandas master, adding a CFTimeIndex to a TimedeltaIndex no longer returns a CFTimeIndex; instead it returns a generic Index of cftime objects.

Failure

2020-01-07T19:19:39.0297443Z =================================== FAILURES ===================================
2020-01-07T19:19:39.0495451Z _________________ test_timedeltaindex_add_cftimeindex[365_day] _________________
2020-01-07T19:19:39.0496702Z 
2020-01-07T19:19:39.0498391Z calendar = '365_day'
2020-01-07T19:19:39.0499052Z 
2020-01-07T19:19:39.0500036Z     @requires_cftime
2020-01-07T19:19:39.0500669Z     @pytest.mark.parametrize("calendar", _CFTIME_CALENDARS)
2020-01-07T19:19:39.0501986Z     def test_timedeltaindex_add_cftimeindex(calendar):
2020-01-07T19:19:39.0502556Z         a = xr.cftime_range("2000", periods=5, calendar=calendar)
2020-01-07T19:19:39.0503384Z         deltas = pd.TimedeltaIndex([timedelta(days=2) for _ in range(5)])
2020-01-07T19:19:39.0503635Z         result = deltas + a
2020-01-07T19:19:39.0504330Z         expected = a.shift(2, "D")
2020-01-07T19:19:39.0505283Z         assert result.equals(expected)
2020-01-07T19:19:39.0506265Z >       assert isinstance(result, CFTimeIndex)
2020-01-07T19:19:39.0507151Z E       AssertionError: assert False
2020-01-07T19:19:39.0508329Z E        +  where False = isinstance(Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,\n       2000-01-06 00:00:00, 2000-01-07 00:00:00],\n      dtype='object'), CFTimeIndex)

2. With pandas master, casting a DatetimeIndex (i.e. the result of a call to to_datetime) to an array now returns a NumPy array of timezone-aware Timestamp objects, rather than timezone-naive NumPy array of dtype datetime64[ns].

Failure

2020-01-07T19:19:39.0552973Z ____ test_cf_datetime_nan[num_dates1-days since 2000-01-01-expected_list1] _____
2020-01-07T19:19:39.0553281Z 
2020-01-07T19:19:39.0553765Z num_dates = [nan, 0], units = 'days since 2000-01-01'
2020-01-07T19:19:39.0554887Z expected_list = ['NaT', '2000-01-01T00:00:00Z']
2020-01-07T19:19:39.0555104Z 
2020-01-07T19:19:39.0555251Z     @arm_xfail
2020-01-07T19:19:39.0555406Z     @requires_cftime
2020-01-07T19:19:39.0555551Z     @pytest.mark.parametrize(
2020-01-07T19:19:39.0555693Z         ["num_dates", "units", "expected_list"],
2020-01-07T19:19:39.0555849Z         [
2020-01-07T19:19:39.0556225Z             ([np.nan], "days since 2000-01-01", ["NaT"]),
2020-01-07T19:19:39.0556674Z             ([np.nan, 0], "days since 2000-01-01", ["NaT", "2000-01-01T00:00:00Z"]),
2020-01-07T19:19:39.0556881Z             (
2020-01-07T19:19:39.0557026Z                 [np.nan, 0, 1],
2020-01-07T19:19:39.0557374Z                 "days since 2000-01-01",
2020-01-07T19:19:39.0558208Z                 ["NaT", "2000-01-01T00:00:00Z", "2000-01-02T00:00:00Z"],
2020-01-07T19:19:39.0558355Z             ),
2020-01-07T19:19:39.0558466Z         ],
2020-01-07T19:19:39.0558591Z     )
2020-01-07T19:19:39.0559984Z     def test_cf_datetime_nan(num_dates, units, expected_list):
2020-01-07T19:19:39.0560153Z         with warnings.catch_warnings():
2020-01-07T19:19:39.0560559Z             warnings.filterwarnings("ignore", "All-NaN")
2020-01-07T19:19:39.0560733Z             actual = coding.times.decode_cf_datetime(num_dates, units)
2020-01-07T19:19:39.0561076Z         # use pandas because numpy will deprecate timezone-aware conversions
2020-01-07T19:19:39.0561235Z         expected = pd.to_datetime(expected_list)
2020-01-07T19:19:39.0561532Z >       assert_array_equal(expected, actual)
2020-01-07T19:19:39.0561669Z E       AssertionError: 
2020-01-07T19:19:39.0561785Z E       Arrays are not equal
2020-01-07T19:19:39.0561899Z E       
2020-01-07T19:19:39.0562138Z E       Mismatched elements: 2 / 2 (100%)
2020-01-07T19:19:39.0562505Z E        x: array([NaT, Timestamp('2000-01-01 00:00:00+0000', tz='UTC')], dtype=object)
2020-01-07T19:19:39.0563375Z E        y: array([                          'NaT', '2000-01-01T00:00:00.000000000'],
2020-01-07T19:19:39.0563710Z E             dtype='datetime64[ns]')
2020-01-07T19:19:39.0564285Z 
2020-01-07T19:19:39.0564914Z xarray/tests/test_coding_times.py:455: AssertionError

(2) is simple. Basically due to a planned change¹ in pandas, the test needs to be edited. In xarray we still expect decode_cf_datetime to return timezone-naive dates. Therefore we need to make sure that we are comparing against a timezone-naive reference.

(1) is trickier; I'm not sure if it's something we should raise in the pandas issue tracker. Essentially we rely on TimedeltaIndex.__add__(other) to return NotImplemented when other is a CFTimeIndex; this way it will resort to using CFTimeIndex.__radd__ instead. It looks like recent code changes in pandas broke this.

---

¹See this FutureWarning:

FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
	To accept the future behavior, pass 'dtype=object'.
	To keep the old behavior, pass 'dtype="datetime64[ns]"'.
  exec(code_obj, self.user_global_ns, self.user_ns)

[dcherian]

(1) is trickier; I'm not sure if it's something we should raise in the pandas issue tracker. Essentially we rely on TimedeltaIndex.add(other) to return NotImplemented when other is a CFTimeIndex; this way it will resort to using CFTimeIndex.radd instead. It looks like recent code changes in pandas broke this.

cc @TomAugspurger

[TomAugspurger]

@jbrockmendel likely knows more about the index arithmetic issue.

 In [22]: import xarray as xr

In [23]: import pandas as pd

In [24]: idx = pd.timedelta_range("1D", periods=5, freq="D")

In [25]: a = xr.cftime_range("2000", periods=5)

In [26]: idx + a
/Users/taugspurger/sandbox/pandas/pandas/core/arrays/datetimelike.py:1204: PerformanceWarning: Adding/subtracting array of DateOffsets to TimedeltaArray not vectorized
  PerformanceWarning,
Out[26]:
Index([2000-01-02 00:00:00, 2000-01-04 00:00:00, 2000-01-06 00:00:00,
       2000-01-08 00:00:00, 2000-01-10 00:00:00],
      dtype='object')

In [27]: a + idx
Out[27]:
CFTimeIndex([2000-01-02 00:00:00, 2000-01-04 00:00:00, 2000-01-06 00:00:00,
             2000-01-08 00:00:00, 2000-01-10 00:00:00],
            dtype='object')

[jbrockmendel]

we recently changed datetimelike arithmetic to send all object-dtype arrays through _addsub_object_array (previously _addsub_offsetlike). Previously I think idx.__add__(a) would return NotImplemented. So we probably want to get the NotImplemented behavior back.

[dcherian]

Thanks @jbrockmendel should we open a pandas issue?

[TomAugspurger]

Opened pandas-dev/pandas#31109.

[dcherian]

Thanks @TomAugspurger

[dcherian]

should be closed by pandas-dev/pandas#31136 . I think the tests will turn green once the wheels update

[TomAugspurger]

FYI, we had some failures in our nightly wheel builds so they weren't updated in a while. MacPython/pandas-wheels#70 fixed that, so you'll hopefully get a new wheel tonight.

…

On Tue, Jan 28, 2020 at 5:09 PM Deepak Cherian ***@***.***> wrote: should be closed by pandas-dev/pandas#31136 <pandas-dev/pandas#31136> . I think the tests will turn green once the wheels update — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#3673?email_source=notifications&email_token=AAKAOISQMX62U3JJPLTYVEDRAC3JRA5CNFSM4KEMIFRKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEKFLHSQ#issuecomment-579515338>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAKAOIT7GKDFDJV4LFZA4YDRAC3JRANCNFSM4KEMIFRA> .

[spencerkclark]

Thanks for the fixes @TomAugspurger!