from typing import Type, Union import numpy as np import pytest from pandas._libs import OutOfBoundsDatetime from pandas.compat.numpy import _np_version_under1p18 import pandas as pd import pandas._testing as tm from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray from pandas.core.indexes.datetimes import DatetimeIndex from pandas.core.indexes.period import Period, PeriodIndex from pandas.core.indexes.timedeltas import TimedeltaIndex # TODO: more freq variants @pytest.fixture(params=["D", "B", "W", "M", "Q", "Y"]) def period_index(request): """ A fixture to provide PeriodIndex objects with different frequencies. Most PeriodArray behavior is already tested in PeriodIndex tests, so here we just test that the PeriodArray behavior matches the PeriodIndex behavior. """ freqstr = request.param # TODO: non-monotone indexes; NaTs, different start dates pi = pd.period_range(start=pd.Timestamp("2000-01-01"), periods=100, freq=freqstr) return pi @pytest.fixture(params=["D", "B", "W", "M", "Q", "Y"]) def datetime_index(request): """ A fixture to provide DatetimeIndex objects with different frequencies. Most DatetimeArray behavior is already tested in DatetimeIndex tests, so here we just test that the DatetimeArray behavior matches the DatetimeIndex behavior. """ freqstr = request.param # TODO: non-monotone indexes; NaTs, different start dates, timezones dti = pd.date_range(start=pd.Timestamp("2000-01-01"), periods=100, freq=freqstr) return dti @pytest.fixture def timedelta_index(request): """ A fixture to provide TimedeltaIndex objects with different frequencies. Most TimedeltaArray behavior is already tested in TimedeltaIndex tests, so here we just test that the TimedeltaArray behavior matches the TimedeltaIndex behavior. """ # TODO: flesh this out return pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"]) class SharedTests: index_cls: Type[Union[DatetimeIndex, PeriodIndex, TimedeltaIndex]] @pytest.fixture def arr1d(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") return arr def test_compare_len1_raises(self): # make sure we raise when comparing with different lengths, specific # to the case where one has length-1, which numpy would broadcast data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls._simple_new(data, freq="D") idx = self.index_cls(arr) with pytest.raises(ValueError, match="Lengths must match"): arr == arr[:1] # test the index classes while we're at it, GH#23078 with pytest.raises(ValueError, match="Lengths must match"): idx <= idx[[0]] @pytest.mark.parametrize("reverse", [True, False]) @pytest.mark.parametrize("as_index", [True, False]) def test_compare_categorical_dtype(self, arr1d, as_index, reverse, ordered): other = pd.Categorical(arr1d, ordered=ordered) if as_index: other = pd.CategoricalIndex(other) left, right = arr1d, other if reverse: left, right = right, left ones = np.ones(arr1d.shape, dtype=bool) zeros = ~ones result = left == right tm.assert_numpy_array_equal(result, ones) result = left != right tm.assert_numpy_array_equal(result, zeros) if not reverse and not as_index: # Otherwise Categorical raises TypeError bc it is not ordered # TODO: we should probably get the same behavior regardless? result = left < right tm.assert_numpy_array_equal(result, zeros) result = left <= right tm.assert_numpy_array_equal(result, ones) result = left > right tm.assert_numpy_array_equal(result, zeros) result = left >= right tm.assert_numpy_array_equal(result, ones) def test_take(self): data = np.arange(100, dtype="i8") * 24 * 3600 * 10 ** 9 np.random.shuffle(data) arr = self.array_cls._simple_new(data, freq="D") idx = self.index_cls._simple_new(arr) takers = [1, 4, 94] result = arr.take(takers) expected = idx.take(takers) tm.assert_index_equal(self.index_cls(result), expected) takers = np.array([1, 4, 94]) result = arr.take(takers) expected = idx.take(takers) tm.assert_index_equal(self.index_cls(result), expected) @pytest.mark.parametrize("fill_value", [2, 2.0, pd.Timestamp.now().time]) def test_take_fill_raises(self, fill_value): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls._simple_new(data, freq="D") msg = f"'fill_value' should be a {self.dtype}. Got '{fill_value}'" with pytest.raises(ValueError, match=msg): arr.take([0, 1], allow_fill=True, fill_value=fill_value) def test_take_fill(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls._simple_new(data, freq="D") result = arr.take([-1, 1], allow_fill=True, fill_value=None) assert result[0] is pd.NaT result = arr.take([-1, 1], allow_fill=True, fill_value=np.nan) assert result[0] is pd.NaT result = arr.take([-1, 1], allow_fill=True, fill_value=pd.NaT) assert result[0] is pd.NaT def test_concat_same_type(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls._simple_new(data, freq="D") idx = self.index_cls(arr) idx = idx.insert(0, pd.NaT) arr = self.array_cls(idx) result = arr._concat_same_type([arr[:-1], arr[1:], arr]) arr2 = arr.astype(object) expected = self.index_cls(np.concatenate([arr2[:-1], arr2[1:], arr2]), None) tm.assert_index_equal(self.index_cls(result), expected) def test_unbox_scalar(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") result = arr._unbox_scalar(arr[0]) assert isinstance(result, int) result = arr._unbox_scalar(pd.NaT) assert isinstance(result, int) msg = f"'value' should be a {self.dtype.__name__}." with pytest.raises(ValueError, match=msg): arr._unbox_scalar("foo") def test_check_compatible_with(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") arr._check_compatible_with(arr[0]) arr._check_compatible_with(arr[:1]) arr._check_compatible_with(pd.NaT) def test_scalar_from_string(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") result = arr._scalar_from_string(str(arr[0])) assert result == arr[0] def test_reduce_invalid(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") with pytest.raises(TypeError, match="cannot perform"): arr._reduce("not a method") @pytest.mark.parametrize("method", ["pad", "backfill"]) def test_fillna_method_doesnt_change_orig(self, method): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") arr[4] = pd.NaT fill_value = arr[3] if method == "pad" else arr[5] result = arr.fillna(method=method) assert result[4] == fill_value # check that the original was not changed assert arr[4] is pd.NaT def test_searchsorted(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") # scalar result = arr.searchsorted(arr[1]) assert result == 1 result = arr.searchsorted(arr[2], side="right") assert result == 3 # own-type result = arr.searchsorted(arr[1:3]) expected = np.array([1, 2], dtype=np.intp) tm.assert_numpy_array_equal(result, expected) result = arr.searchsorted(arr[1:3], side="right") expected = np.array([2, 3], dtype=np.intp) tm.assert_numpy_array_equal(result, expected) # Following numpy convention, NaT goes at the beginning # (unlike NaN which goes at the end) result = arr.searchsorted(pd.NaT) assert result == 0 def test_getitem_2d(self, arr1d): # 2d slicing on a 1D array expected = type(arr1d)(arr1d._data[:, np.newaxis], dtype=arr1d.dtype) result = arr1d[:, np.newaxis] tm.assert_equal(result, expected) # Lookup on a 2D array arr2d = expected expected = type(arr2d)(arr2d._data[:3, 0], dtype=arr2d.dtype) result = arr2d[:3, 0] tm.assert_equal(result, expected) # Scalar lookup result = arr2d[-1, 0] expected = arr1d[-1] assert result == expected def test_setitem(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") arr[0] = arr[1] expected = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 expected[0] = expected[1] tm.assert_numpy_array_equal(arr.asi8, expected) arr[:2] = arr[-2:] expected[:2] = expected[-2:] tm.assert_numpy_array_equal(arr.asi8, expected) def test_setitem_str_array(self, arr1d): if isinstance(arr1d, DatetimeArray) and arr1d.tz is not None: pytest.xfail(reason="timezone comparisons inconsistent") expected = arr1d.copy() expected[[0, 1]] = arr1d[-2:] arr1d[:2] = [str(x) for x in arr1d[-2:]] tm.assert_equal(arr1d, expected) @pytest.mark.parametrize("as_index", [True, False]) def test_setitem_categorical(self, arr1d, as_index): expected = arr1d.copy()[::-1] if not isinstance(expected, PeriodArray): expected = expected._with_freq(None) cat = pd.Categorical(arr1d) if as_index: cat = pd.CategoricalIndex(cat) arr1d[:] = cat[::-1] tm.assert_equal(arr1d, expected) def test_setitem_raises(self): data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") val = arr[0] with pytest.raises(IndexError, match="index 12 is out of bounds"): arr[12] = val with pytest.raises(TypeError, match="'value' should be a.* 'object'"): arr[0] = object() @pytest.mark.parametrize("box", [list, np.array, pd.Index, pd.Series]) def test_setitem_numeric_raises(self, arr1d, box): # We dont case e.g. int64 to our own dtype for setitem msg = "requires compatible dtype" with pytest.raises(TypeError, match=msg): arr1d[:2] = box([0, 1]) with pytest.raises(TypeError, match=msg): arr1d[:2] = box([0.0, 1.0]) def test_inplace_arithmetic(self): # GH#24115 check that iadd and isub are actually in-place data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") expected = arr + pd.Timedelta(days=1) arr += pd.Timedelta(days=1) tm.assert_equal(arr, expected) expected = arr - pd.Timedelta(days=1) arr -= pd.Timedelta(days=1) tm.assert_equal(arr, expected) def test_shift_fill_int_deprecated(self): # GH#31971 data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9 arr = self.array_cls(data, freq="D") with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result = arr.shift(1, fill_value=1) expected = arr.copy() if self.array_cls is PeriodArray: fill_val = PeriodArray._scalar_type._from_ordinal(1, freq=arr.freq) else: fill_val = arr._scalar_type(1) expected[0] = fill_val expected[1:] = arr[:-1] tm.assert_equal(result, expected) class TestDatetimeArray(SharedTests): index_cls = pd.DatetimeIndex array_cls = DatetimeArray dtype = pd.Timestamp @pytest.fixture def arr1d(self, tz_naive_fixture): tz = tz_naive_fixture dti = pd.date_range("2016-01-01 01:01:00", periods=3, freq="H", tz=tz) dta = dti._data return dta def test_round(self, tz_naive_fixture): # GH#24064 tz = tz_naive_fixture dti = pd.date_range("2016-01-01 01:01:00", periods=3, freq="H", tz=tz) result = dti.round(freq="2T") expected = dti - pd.Timedelta(minutes=1) expected = expected._with_freq(None) tm.assert_index_equal(result, expected) dta = dti._data result = dta.round(freq="2T") expected = expected._data._with_freq(None) tm.assert_datetime_array_equal(result, expected) def test_array_interface(self, datetime_index): arr = DatetimeArray(datetime_index) # default asarray gives the same underlying data (for tz naive) result = np.asarray(arr) expected = arr._data assert result is expected tm.assert_numpy_array_equal(result, expected) result = np.array(arr, copy=False) assert result is expected tm.assert_numpy_array_equal(result, expected) # specifying M8[ns] gives the same result as default result = np.asarray(arr, dtype="datetime64[ns]") expected = arr._data assert result is expected tm.assert_numpy_array_equal(result, expected) result = np.array(arr, dtype="datetime64[ns]", copy=False) assert result is expected tm.assert_numpy_array_equal(result, expected) result = np.array(arr, dtype="datetime64[ns]") assert result is not expected tm.assert_numpy_array_equal(result, expected) # to object dtype result = np.asarray(arr, dtype=object) expected = np.array(list(arr), dtype=object) tm.assert_numpy_array_equal(result, expected) # to other dtype always copies result = np.asarray(arr, dtype="int64") assert result is not arr.asi8 assert not np.may_share_memory(arr, result) expected = arr.asi8.copy() tm.assert_numpy_array_equal(result, expected) # other dtypes handled by numpy for dtype in ["float64", str]: result = np.asarray(arr, dtype=dtype) expected = np.asarray(arr).astype(dtype) tm.assert_numpy_array_equal(result, expected) def test_array_object_dtype(self, tz_naive_fixture): # GH#23524 tz = tz_naive_fixture dti = pd.date_range("2016-01-01", periods=3, tz=tz) arr = DatetimeArray(dti) expected = np.array(list(dti)) result = np.array(arr, dtype=object) tm.assert_numpy_array_equal(result, expected) # also test the DatetimeIndex method while we're at it result = np.array(dti, dtype=object) tm.assert_numpy_array_equal(result, expected) def test_array_tz(self, tz_naive_fixture): # GH#23524 tz = tz_naive_fixture dti = pd.date_range("2016-01-01", periods=3, tz=tz) arr = DatetimeArray(dti) expected = dti.asi8.view("M8[ns]") result = np.array(arr, dtype="M8[ns]") tm.assert_numpy_array_equal(result, expected) result = np.array(arr, dtype="datetime64[ns]") tm.assert_numpy_array_equal(result, expected) # check that we are not making copies when setting copy=False result = np.array(arr, dtype="M8[ns]", copy=False) assert result.base is expected.base assert result.base is not None result = np.array(arr, dtype="datetime64[ns]", copy=False) assert result.base is expected.base assert result.base is not None def test_array_i8_dtype(self, tz_naive_fixture): tz = tz_naive_fixture dti = pd.date_range("2016-01-01", periods=3, tz=tz) arr = DatetimeArray(dti) expected = dti.asi8 result = np.array(arr, dtype="i8") tm.assert_numpy_array_equal(result, expected) result = np.array(arr, dtype=np.int64) tm.assert_numpy_array_equal(result, expected) # check that we are still making copies when setting copy=False result = np.array(arr, dtype="i8", copy=False) assert result.base is not expected.base assert result.base is None def test_from_array_keeps_base(self): # Ensure that DatetimeArray._data.base isn't lost. arr = np.array(["2000-01-01", "2000-01-02"], dtype="M8[ns]") dta = DatetimeArray(arr) assert dta._data is arr dta = DatetimeArray(arr[:0]) assert dta._data.base is arr def test_from_dti(self, tz_naive_fixture): tz = tz_naive_fixture dti = pd.date_range("2016-01-01", periods=3, tz=tz) arr = DatetimeArray(dti) assert list(dti) == list(arr) # Check that Index.__new__ knows what to do with DatetimeArray dti2 = pd.Index(arr) assert isinstance(dti2, pd.DatetimeIndex) assert list(dti2) == list(arr) def test_astype_object(self, tz_naive_fixture): tz = tz_naive_fixture dti = pd.date_range("2016-01-01", periods=3, tz=tz) arr = DatetimeArray(dti) asobj = arr.astype("O") assert isinstance(asobj, np.ndarray) assert asobj.dtype == "O" assert list(asobj) == list(dti) @pytest.mark.parametrize("freqstr", ["D", "B", "W", "M", "Q", "Y"]) def test_to_perioddelta(self, datetime_index, freqstr): # GH#23113 dti = datetime_index arr = DatetimeArray(dti) with tm.assert_produces_warning(FutureWarning): # Deprecation GH#34853 expected = dti.to_perioddelta(freq=freqstr) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): # stacklevel is chosen to be "correct" for DatetimeIndex, not # DatetimeArray result = arr.to_perioddelta(freq=freqstr) assert isinstance(result, TimedeltaArray) # placeholder until these become actual EA subclasses and we can use # an EA-specific tm.assert_ function tm.assert_index_equal(pd.Index(result), pd.Index(expected)) @pytest.mark.parametrize("freqstr", ["D", "B", "W", "M", "Q", "Y"]) def test_to_period(self, datetime_index, freqstr): dti = datetime_index arr = DatetimeArray(dti) expected = dti.to_period(freq=freqstr) result = arr.to_period(freq=freqstr) assert isinstance(result, PeriodArray) # placeholder until these become actual EA subclasses and we can use # an EA-specific tm.assert_ function tm.assert_index_equal(pd.Index(result), pd.Index(expected)) @pytest.mark.parametrize("propname", pd.DatetimeIndex._bool_ops) def test_bool_properties(self, datetime_index, propname): # in this case _bool_ops is just `is_leap_year` dti = datetime_index arr = DatetimeArray(dti) assert dti.freq == arr.freq result = getattr(arr, propname) expected = np.array(getattr(dti, propname), dtype=result.dtype) tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("propname", pd.DatetimeIndex._field_ops) def test_int_properties(self, datetime_index, propname): if propname in ["week", "weekofyear"]: # GH#33595 Deprecate week and weekofyear return dti = datetime_index arr = DatetimeArray(dti) result = getattr(arr, propname) expected = np.array(getattr(dti, propname), dtype=result.dtype) tm.assert_numpy_array_equal(result, expected) def test_take_fill_valid(self, datetime_index, tz_naive_fixture): dti = datetime_index.tz_localize(tz_naive_fixture) arr = DatetimeArray(dti) now = pd.Timestamp.now().tz_localize(dti.tz) result = arr.take([-1, 1], allow_fill=True, fill_value=now) assert result[0] == now msg = f"'fill_value' should be a {self.dtype}. Got '0 days 00:00:00'." with pytest.raises(ValueError, match=msg): # fill_value Timedelta invalid arr.take([-1, 1], allow_fill=True, fill_value=now - now) msg = f"'fill_value' should be a {self.dtype}. Got '2014Q1'." with pytest.raises(ValueError, match=msg): # fill_value Period invalid arr.take([-1, 1], allow_fill=True, fill_value=pd.Period("2014Q1")) tz = None if dti.tz is not None else "US/Eastern" now = pd.Timestamp.now().tz_localize(tz) msg = "Cannot compare tz-naive and tz-aware datetime-like objects" with pytest.raises(TypeError, match=msg): # Timestamp with mismatched tz-awareness arr.take([-1, 1], allow_fill=True, fill_value=now) value = pd.NaT.value msg = f"'fill_value' should be a {self.dtype}. Got '{value}'." with pytest.raises(ValueError, match=msg): # require NaT, not iNaT, as it could be confused with an integer arr.take([-1, 1], allow_fill=True, fill_value=value) value = np.timedelta64("NaT", "ns") msg = f"'fill_value' should be a {self.dtype}. Got '{str(value)}'." with pytest.raises(ValueError, match=msg): # require appropriate-dtype if we have a NA value arr.take([-1, 1], allow_fill=True, fill_value=value) def test_concat_same_type_invalid(self, datetime_index): # different timezones dti = datetime_index arr = DatetimeArray(dti) if arr.tz is None: other = arr.tz_localize("UTC") else: other = arr.tz_localize(None) with pytest.raises(ValueError, match="to_concat must have the same"): arr._concat_same_type([arr, other]) def test_concat_same_type_different_freq(self): # we *can* concatenate DTI with different freqs. a = DatetimeArray(pd.date_range("2000", periods=2, freq="D", tz="US/Central")) b = DatetimeArray(pd.date_range("2000", periods=2, freq="H", tz="US/Central")) result = DatetimeArray._concat_same_type([a, b]) expected = DatetimeArray( pd.to_datetime( [ "2000-01-01 00:00:00", "2000-01-02 00:00:00", "2000-01-01 00:00:00", "2000-01-01 01:00:00", ] ).tz_localize("US/Central") ) tm.assert_datetime_array_equal(result, expected) def test_strftime(self, datetime_index): arr = DatetimeArray(datetime_index) result = arr.strftime("%Y %b") expected = np.array([ts.strftime("%Y %b") for ts in arr], dtype=object) tm.assert_numpy_array_equal(result, expected) def test_strftime_nat(self): # GH 29578 arr = DatetimeArray(DatetimeIndex(["2019-01-01", pd.NaT])) result = arr.strftime("%Y-%m-%d") expected = np.array(["2019-01-01", np.nan], dtype=object) tm.assert_numpy_array_equal(result, expected) class TestTimedeltaArray(SharedTests): index_cls = pd.TimedeltaIndex array_cls = TimedeltaArray dtype = pd.Timedelta def test_from_tdi(self): tdi = pd.TimedeltaIndex(["1 Day", "3 Hours"]) arr = TimedeltaArray(tdi) assert list(arr) == list(tdi) # Check that Index.__new__ knows what to do with TimedeltaArray tdi2 = pd.Index(arr) assert isinstance(tdi2, pd.TimedeltaIndex) assert list(tdi2) == list(arr) def test_astype_object(self): tdi = pd.TimedeltaIndex(["1 Day", "3 Hours"]) arr = TimedeltaArray(tdi) asobj = arr.astype("O") assert isinstance(asobj, np.ndarray) assert asobj.dtype == "O" assert list(asobj) == list(tdi) def test_to_pytimedelta(self, timedelta_index): tdi = timedelta_index arr = TimedeltaArray(tdi) expected = tdi.to_pytimedelta() result = arr.to_pytimedelta() tm.assert_numpy_array_equal(result, expected) def test_total_seconds(self, timedelta_index): tdi = timedelta_index arr = TimedeltaArray(tdi) expected = tdi.total_seconds() result = arr.total_seconds() tm.assert_numpy_array_equal(result, expected.values) @pytest.mark.parametrize("propname", pd.TimedeltaIndex._field_ops) def test_int_properties(self, timedelta_index, propname): tdi = timedelta_index arr = TimedeltaArray(tdi) result = getattr(arr, propname) expected = np.array(getattr(tdi, propname), dtype=result.dtype) tm.assert_numpy_array_equal(result, expected) def test_array_interface(self, timedelta_index): arr = TimedeltaArray(timedelta_index) # default asarray gives the same underlying data result = np.asarray(arr) expected = arr._data assert result is expected tm.assert_numpy_array_equal(result, expected) result = np.array(arr, copy=False) assert result is expected tm.assert_numpy_array_equal(result, expected) # specifying m8[ns] gives the same result as default result = np.asarray(arr, dtype="timedelta64[ns]") expected = arr._data assert result is expected tm.assert_numpy_array_equal(result, expected) result = np.array(arr, dtype="timedelta64[ns]", copy=False) assert result is expected tm.assert_numpy_array_equal(result, expected) result = np.array(arr, dtype="timedelta64[ns]") assert result is not expected tm.assert_numpy_array_equal(result, expected) # to object dtype result = np.asarray(arr, dtype=object) expected = np.array(list(arr), dtype=object) tm.assert_numpy_array_equal(result, expected) # to other dtype always copies result = np.asarray(arr, dtype="int64") assert result is not arr.asi8 assert not np.may_share_memory(arr, result) expected = arr.asi8.copy() tm.assert_numpy_array_equal(result, expected) # other dtypes handled by numpy for dtype in ["float64", str]: result = np.asarray(arr, dtype=dtype) expected = np.asarray(arr).astype(dtype) tm.assert_numpy_array_equal(result, expected) def test_take_fill_valid(self, timedelta_index): tdi = timedelta_index arr = TimedeltaArray(tdi) td1 = pd.Timedelta(days=1) result = arr.take([-1, 1], allow_fill=True, fill_value=td1) assert result[0] == td1 now = pd.Timestamp.now() value = now msg = f"'fill_value' should be a {self.dtype}. Got '{value}'." with pytest.raises(ValueError, match=msg): # fill_value Timestamp invalid arr.take([0, 1], allow_fill=True, fill_value=value) value = now.to_period("D") msg = f"'fill_value' should be a {self.dtype}. Got '{value}'." with pytest.raises(ValueError, match=msg): # fill_value Period invalid arr.take([0, 1], allow_fill=True, fill_value=value) value = np.datetime64("NaT", "ns") msg = f"'fill_value' should be a {self.dtype}. Got '{str(value)}'." with pytest.raises(ValueError, match=msg): # require appropriate-dtype if we have a NA value arr.take([-1, 1], allow_fill=True, fill_value=value) class TestPeriodArray(SharedTests): index_cls = pd.PeriodIndex array_cls = PeriodArray dtype = pd.Period @pytest.fixture def arr1d(self, period_index): return period_index._data def test_from_pi(self, period_index): pi = period_index arr = PeriodArray(pi) assert list(arr) == list(pi) # Check that Index.__new__ knows what to do with PeriodArray pi2 = pd.Index(arr) assert isinstance(pi2, pd.PeriodIndex) assert list(pi2) == list(arr) def test_astype_object(self, period_index): pi = period_index arr = PeriodArray(pi) asobj = arr.astype("O") assert isinstance(asobj, np.ndarray) assert asobj.dtype == "O" assert list(asobj) == list(pi) def test_take_fill_valid(self, period_index): pi = period_index arr = PeriodArray(pi) value = pd.NaT.value msg = f"'fill_value' should be a {self.dtype}. Got '{value}'." with pytest.raises(ValueError, match=msg): # require NaT, not iNaT, as it could be confused with an integer arr.take([-1, 1], allow_fill=True, fill_value=value) value = np.timedelta64("NaT", "ns") msg = f"'fill_value' should be a {self.dtype}. Got '{str(value)}'." with pytest.raises(ValueError, match=msg): # require appropriate-dtype if we have a NA value arr.take([-1, 1], allow_fill=True, fill_value=value) @pytest.mark.parametrize("how", ["S", "E"]) def test_to_timestamp(self, how, period_index): pi = period_index arr = PeriodArray(pi) expected = DatetimeArray(pi.to_timestamp(how=how)) result = arr.to_timestamp(how=how) assert isinstance(result, DatetimeArray) # placeholder until these become actual EA subclasses and we can use # an EA-specific tm.assert_ function tm.assert_index_equal(pd.Index(result), pd.Index(expected)) def test_to_timestamp_out_of_bounds(self): # GH#19643 previously overflowed silently pi = pd.period_range("1500", freq="Y", periods=3) msg = "Out of bounds nanosecond timestamp: 1500-01-01 00:00:00" with pytest.raises(OutOfBoundsDatetime, match=msg): pi.to_timestamp() with pytest.raises(OutOfBoundsDatetime, match=msg): pi._data.to_timestamp() @pytest.mark.parametrize("propname", PeriodArray._bool_ops) def test_bool_properties(self, period_index, propname): # in this case _bool_ops is just `is_leap_year` pi = period_index arr = PeriodArray(pi) result = getattr(arr, propname) expected = np.array(getattr(pi, propname)) tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("propname", PeriodArray._field_ops) def test_int_properties(self, period_index, propname): pi = period_index arr = PeriodArray(pi) result = getattr(arr, propname) expected = np.array(getattr(pi, propname)) tm.assert_numpy_array_equal(result, expected) def test_array_interface(self, period_index): arr = PeriodArray(period_index) # default asarray gives objects result = np.asarray(arr) expected = np.array(list(arr), dtype=object) tm.assert_numpy_array_equal(result, expected) # to object dtype (same as default) result = np.asarray(arr, dtype=object) tm.assert_numpy_array_equal(result, expected) result = np.asarray(arr, dtype="int64") tm.assert_numpy_array_equal(result, arr.asi8) # to other dtypes msg = r"float\(\) argument must be a string or a number, not 'Period'" with pytest.raises(TypeError, match=msg): np.asarray(arr, dtype="float64") result = np.asarray(arr, dtype="S20") expected = np.asarray(arr).astype("S20") tm.assert_numpy_array_equal(result, expected) def test_strftime(self, period_index): arr = PeriodArray(period_index) result = arr.strftime("%Y") expected = np.array([per.strftime("%Y") for per in arr], dtype=object) tm.assert_numpy_array_equal(result, expected) def test_strftime_nat(self): # GH 29578 arr = PeriodArray(PeriodIndex(["2019-01-01", pd.NaT], dtype="period[D]")) result = arr.strftime("%Y-%m-%d") expected = np.array(["2019-01-01", np.nan], dtype=object) tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize( "array,casting_nats", [ ( pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data, (pd.NaT, np.timedelta64("NaT", "ns")), ), ( pd.date_range("2000-01-01", periods=3, freq="D")._data, (pd.NaT, np.datetime64("NaT", "ns")), ), (pd.period_range("2000-01-01", periods=3, freq="D")._data, (pd.NaT,)), ], ids=lambda x: type(x).__name__, ) def test_casting_nat_setitem_array(array, casting_nats): expected = type(array)._from_sequence([pd.NaT, array[1], array[2]]) for nat in casting_nats: arr = array.copy() arr[0] = nat tm.assert_equal(arr, expected) @pytest.mark.parametrize( "array,non_casting_nats", [ ( pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data, (np.datetime64("NaT", "ns"), pd.NaT.value), ), ( pd.date_range("2000-01-01", periods=3, freq="D")._data, (np.timedelta64("NaT", "ns"), pd.NaT.value), ), ( pd.period_range("2000-01-01", periods=3, freq="D")._data, (np.datetime64("NaT", "ns"), np.timedelta64("NaT", "ns"), pd.NaT.value), ), ], ids=lambda x: type(x).__name__, ) def test_invalid_nat_setitem_array(array, non_casting_nats): msg = ( "'value' should be a '(Timestamp|Timedelta|Period)', 'NaT', or array of those. " "Got '(timedelta64|datetime64|int)' instead." ) for nat in non_casting_nats: with pytest.raises(TypeError, match=msg): array[0] = nat @pytest.mark.parametrize( "array", [ pd.date_range("2000", periods=4).array, pd.timedelta_range("2000", periods=4).array, ], ) def test_to_numpy_extra(array): if _np_version_under1p18: # np.isnan(NaT) raises, so use pandas' isnan = pd.isna else: isnan = np.isnan array[0] = pd.NaT original = array.copy() result = array.to_numpy() assert isnan(result[0]) result = array.to_numpy(dtype="int64") assert result[0] == -9223372036854775808 result = array.to_numpy(dtype="int64", na_value=0) assert result[0] == 0 result = array.to_numpy(na_value=array[1].to_numpy()) assert result[0] == result[1] result = array.to_numpy(na_value=array[1].to_numpy(copy=False)) assert result[0] == result[1] tm.assert_equal(array, original) @pytest.mark.parametrize("as_index", [True, False]) @pytest.mark.parametrize( "values", [ pd.to_datetime(["2020-01-01", "2020-02-01"]), pd.TimedeltaIndex([1, 2], unit="D"), pd.PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"), ], ) @pytest.mark.parametrize( "klass", [ list, np.array, pd.array, pd.Series, pd.Index, pd.Categorical, pd.CategoricalIndex, ], ) def test_searchsorted_datetimelike_with_listlike(values, klass, as_index): # https://github.com/pandas-dev/pandas/issues/32762 if not as_index: values = values._data result = values.searchsorted(klass(values)) expected = np.array([0, 1], dtype=result.dtype) tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize( "values", [ pd.to_datetime(["2020-01-01", "2020-02-01"]), pd.TimedeltaIndex([1, 2], unit="D"), pd.PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"), ], ) @pytest.mark.parametrize( "arg", [[1, 2], ["a", "b"], [pd.Timestamp("2020-01-01", tz="Europe/London")] * 2] ) def test_searchsorted_datetimelike_with_listlike_invalid_dtype(values, arg): # https://github.com/pandas-dev/pandas/issues/32762 msg = "[Unexpected type|Cannot compare]" with pytest.raises(TypeError, match=msg): values.searchsorted(arg) @pytest.mark.parametrize("klass", [list, tuple, np.array, pd.Series]) def test_period_index_construction_from_strings(klass): # https://github.com/pandas-dev/pandas/issues/26109 strings = ["2020Q1", "2020Q2"] * 2 data = klass(strings) result = PeriodIndex(data, freq="Q") expected = PeriodIndex([Period(s) for s in strings]) tm.assert_index_equal(result, expected)