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408 lines
14 KiB
408 lines
14 KiB
"""
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Tests for DatetimeArray
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"""
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import numpy as np
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import pytest
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from pandas.core.dtypes.dtypes import DatetimeTZDtype
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays import DatetimeArray
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class TestDatetimeArrayComparisons:
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# TODO: merge this into tests/arithmetic/test_datetime64 once it is
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# sufficiently robust
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def test_cmp_dt64_arraylike_tznaive(self, comparison_op):
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# arbitrary tz-naive DatetimeIndex
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op = comparison_op
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dti = pd.date_range("2016-01-1", freq="MS", periods=9, tz=None)
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arr = DatetimeArray(dti)
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assert arr.freq == dti.freq
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assert arr.tz == dti.tz
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right = dti
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expected = np.ones(len(arr), dtype=bool)
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if comparison_op.__name__ in ["ne", "gt", "lt"]:
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# for these the comparisons should be all-False
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expected = ~expected
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result = op(arr, arr)
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tm.assert_numpy_array_equal(result, expected)
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for other in [
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right,
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np.array(right),
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list(right),
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tuple(right),
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right.astype(object),
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]:
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result = op(arr, other)
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tm.assert_numpy_array_equal(result, expected)
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result = op(other, arr)
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tm.assert_numpy_array_equal(result, expected)
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class TestDatetimeArray:
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def test_astype_to_same(self):
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arr = DatetimeArray._from_sequence(
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["2000"], dtype=DatetimeTZDtype(tz="US/Central")
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)
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result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
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assert result is arr
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@pytest.mark.parametrize("dtype", ["datetime64[ns]", "datetime64[ns, UTC]"])
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@pytest.mark.parametrize(
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"other", ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, CET]"]
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)
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def test_astype_copies(self, dtype, other):
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# https://github.com/pandas-dev/pandas/pull/32490
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ser = pd.Series([1, 2], dtype=dtype)
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orig = ser.copy()
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warn = None
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if (dtype == "datetime64[ns]") ^ (other == "datetime64[ns]"):
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# deprecated in favor of tz_localize
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warn = FutureWarning
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with tm.assert_produces_warning(warn):
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t = ser.astype(other)
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t[:] = pd.NaT
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tm.assert_series_equal(ser, orig)
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@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
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def test_astype_int(self, dtype):
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arr = DatetimeArray._from_sequence([pd.Timestamp("2000"), pd.Timestamp("2001")])
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result = arr.astype(dtype)
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if np.dtype(dtype).kind == "u":
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expected_dtype = np.dtype("uint64")
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else:
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expected_dtype = np.dtype("int64")
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expected = arr.astype(expected_dtype)
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assert result.dtype == expected_dtype
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tm.assert_numpy_array_equal(result, expected)
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def test_tz_setter_raises(self):
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arr = DatetimeArray._from_sequence(
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["2000"], dtype=DatetimeTZDtype(tz="US/Central")
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)
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with pytest.raises(AttributeError, match="tz_localize"):
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arr.tz = "UTC"
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def test_setitem_str_impute_tz(self, tz_naive_fixture):
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# Like for getitem, if we are passed a naive-like string, we impute
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# our own timezone.
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tz = tz_naive_fixture
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data = np.array([1, 2, 3], dtype="M8[ns]")
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dtype = data.dtype if tz is None else DatetimeTZDtype(tz=tz)
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arr = DatetimeArray(data, dtype=dtype)
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expected = arr.copy()
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ts = pd.Timestamp("2020-09-08 16:50").tz_localize(tz)
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setter = str(ts.tz_localize(None))
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# Setting a scalar tznaive string
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expected[0] = ts
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arr[0] = setter
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tm.assert_equal(arr, expected)
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# Setting a listlike of tznaive strings
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expected[1] = ts
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arr[:2] = [setter, setter]
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tm.assert_equal(arr, expected)
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def test_setitem_different_tz_raises(self):
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data = np.array([1, 2, 3], dtype="M8[ns]")
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arr = DatetimeArray(data, copy=False, dtype=DatetimeTZDtype(tz="US/Central"))
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with pytest.raises(TypeError, match="Cannot compare tz-naive and tz-aware"):
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arr[0] = pd.Timestamp("2000")
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ts = pd.Timestamp("2000", tz="US/Eastern")
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with pytest.raises(ValueError, match="US/Central"):
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with tm.assert_produces_warning(
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FutureWarning, match="mismatched timezones"
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):
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arr[0] = ts
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# once deprecation is enforced
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# assert arr[0] == ts.tz_convert("US/Central")
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def test_setitem_clears_freq(self):
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a = DatetimeArray(pd.date_range("2000", periods=2, freq="D", tz="US/Central"))
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a[0] = pd.Timestamp("2000", tz="US/Central")
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assert a.freq is None
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@pytest.mark.parametrize(
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"obj",
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[
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pd.Timestamp("2021-01-01"),
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pd.Timestamp("2021-01-01").to_datetime64(),
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pd.Timestamp("2021-01-01").to_pydatetime(),
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],
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)
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def test_setitem_objects(self, obj):
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# make sure we accept datetime64 and datetime in addition to Timestamp
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dti = pd.date_range("2000", periods=2, freq="D")
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arr = dti._data
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arr[0] = obj
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assert arr[0] == obj
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def test_repeat_preserves_tz(self):
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dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
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arr = DatetimeArray(dti)
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repeated = arr.repeat([1, 1])
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# preserves tz and values, but not freq
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expected = DatetimeArray(arr.asi8, freq=None, dtype=arr.dtype)
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tm.assert_equal(repeated, expected)
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def test_value_counts_preserves_tz(self):
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dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
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arr = DatetimeArray(dti).repeat([4, 3])
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result = arr.value_counts()
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# Note: not tm.assert_index_equal, since `freq`s do not match
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assert result.index.equals(dti)
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arr[-2] = pd.NaT
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result = arr.value_counts(dropna=False)
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expected = pd.Series([4, 2, 1], index=[dti[0], dti[1], pd.NaT])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("method", ["pad", "backfill"])
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def test_fillna_preserves_tz(self, method):
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dti = pd.date_range("2000-01-01", periods=5, freq="D", tz="US/Central")
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arr = DatetimeArray(dti, copy=True)
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arr[2] = pd.NaT
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fill_val = dti[1] if method == "pad" else dti[3]
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expected = DatetimeArray._from_sequence(
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[dti[0], dti[1], fill_val, dti[3], dti[4]],
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dtype=DatetimeTZDtype(tz="US/Central"),
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)
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result = arr.fillna(method=method)
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tm.assert_extension_array_equal(result, expected)
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# assert that arr and dti were not modified in-place
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assert arr[2] is pd.NaT
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assert dti[2] == pd.Timestamp("2000-01-03", tz="US/Central")
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def test_fillna_2d(self):
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dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
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dta = dti._data.reshape(3, 2).copy()
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dta[0, 1] = pd.NaT
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dta[1, 0] = pd.NaT
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res1 = dta.fillna(method="pad")
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expected1 = dta.copy()
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expected1[1, 0] = dta[0, 0]
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tm.assert_extension_array_equal(res1, expected1)
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res2 = dta.fillna(method="backfill")
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expected2 = dta.copy()
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expected2 = dta.copy()
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expected2[1, 0] = dta[2, 0]
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expected2[0, 1] = dta[1, 1]
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tm.assert_extension_array_equal(res2, expected2)
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# with different ordering for underlying ndarray; behavior should
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# be unchanged
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dta2 = dta._from_backing_data(dta._ndarray.copy(order="F"))
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assert dta2._ndarray.flags["F_CONTIGUOUS"]
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assert not dta2._ndarray.flags["C_CONTIGUOUS"]
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tm.assert_extension_array_equal(dta, dta2)
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res3 = dta2.fillna(method="pad")
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tm.assert_extension_array_equal(res3, expected1)
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res4 = dta2.fillna(method="backfill")
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tm.assert_extension_array_equal(res4, expected2)
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# test the DataFrame method while we're here
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df = pd.DataFrame(dta)
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res = df.fillna(method="pad")
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expected = pd.DataFrame(expected1)
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tm.assert_frame_equal(res, expected)
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res = df.fillna(method="backfill")
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expected = pd.DataFrame(expected2)
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tm.assert_frame_equal(res, expected)
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def test_array_interface_tz(self):
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tz = "US/Central"
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data = DatetimeArray(pd.date_range("2017", periods=2, tz=tz))
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result = np.asarray(data)
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expected = np.array(
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[
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pd.Timestamp("2017-01-01T00:00:00", tz=tz),
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pd.Timestamp("2017-01-02T00:00:00", tz=tz),
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],
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dtype=object,
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)
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tm.assert_numpy_array_equal(result, expected)
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result = np.asarray(data, dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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result = np.asarray(data, dtype="M8[ns]")
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expected = np.array(
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["2017-01-01T06:00:00", "2017-01-02T06:00:00"], dtype="M8[ns]"
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)
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tm.assert_numpy_array_equal(result, expected)
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def test_array_interface(self):
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data = DatetimeArray(pd.date_range("2017", periods=2))
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expected = np.array(
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["2017-01-01T00:00:00", "2017-01-02T00:00:00"], dtype="datetime64[ns]"
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)
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result = np.asarray(data)
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tm.assert_numpy_array_equal(result, expected)
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result = np.asarray(data, dtype=object)
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expected = np.array(
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[pd.Timestamp("2017-01-01T00:00:00"), pd.Timestamp("2017-01-02T00:00:00")],
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dtype=object,
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)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("index", [True, False])
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def test_searchsorted_different_tz(self, index):
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data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
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arr = DatetimeArray(data, freq="D").tz_localize("Asia/Tokyo")
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if index:
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arr = pd.Index(arr)
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expected = arr.searchsorted(arr[2])
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result = arr.searchsorted(arr[2].tz_convert("UTC"))
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assert result == expected
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expected = arr.searchsorted(arr[2:6])
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result = arr.searchsorted(arr[2:6].tz_convert("UTC"))
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize("index", [True, False])
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def test_searchsorted_tzawareness_compat(self, index):
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data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
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arr = DatetimeArray(data, freq="D")
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if index:
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arr = pd.Index(arr)
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mismatch = arr.tz_localize("Asia/Tokyo")
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msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
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with pytest.raises(TypeError, match=msg):
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arr.searchsorted(mismatch[0])
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with pytest.raises(TypeError, match=msg):
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arr.searchsorted(mismatch)
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with pytest.raises(TypeError, match=msg):
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mismatch.searchsorted(arr[0])
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with pytest.raises(TypeError, match=msg):
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mismatch.searchsorted(arr)
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@pytest.mark.parametrize(
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"other",
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[
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1,
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np.int64(1),
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1.0,
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np.timedelta64("NaT"),
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pd.Timedelta(days=2),
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"invalid",
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np.arange(10, dtype="i8") * 24 * 3600 * 10**9,
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np.arange(10).view("timedelta64[ns]") * 24 * 3600 * 10**9,
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pd.Timestamp("2021-01-01").to_period("D"),
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],
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)
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@pytest.mark.parametrize("index", [True, False])
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def test_searchsorted_invalid_types(self, other, index):
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data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
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arr = DatetimeArray(data, freq="D")
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if index:
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arr = pd.Index(arr)
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msg = "|".join(
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[
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"searchsorted requires compatible dtype or scalar",
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"value should be a 'Timestamp', 'NaT', or array of those. Got",
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]
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)
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with pytest.raises(TypeError, match=msg):
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arr.searchsorted(other)
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def test_shift_fill_value(self):
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dti = pd.date_range("2016-01-01", periods=3)
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dta = dti._data
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expected = DatetimeArray(np.roll(dta._data, 1))
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fv = dta[-1]
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for fill_value in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
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result = dta.shift(1, fill_value=fill_value)
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tm.assert_datetime_array_equal(result, expected)
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dta = dta.tz_localize("UTC")
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expected = expected.tz_localize("UTC")
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fv = dta[-1]
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for fill_value in [fv, fv.to_pydatetime()]:
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result = dta.shift(1, fill_value=fill_value)
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tm.assert_datetime_array_equal(result, expected)
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def test_shift_value_tzawareness_mismatch(self):
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dti = pd.date_range("2016-01-01", periods=3)
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dta = dti._data
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fv = dta[-1].tz_localize("UTC")
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for invalid in [fv, fv.to_pydatetime()]:
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with pytest.raises(TypeError, match="Cannot compare"):
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dta.shift(1, fill_value=invalid)
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dta = dta.tz_localize("UTC")
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fv = dta[-1].tz_localize(None)
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for invalid in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
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with pytest.raises(TypeError, match="Cannot compare"):
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dta.shift(1, fill_value=invalid)
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def test_shift_requires_tzmatch(self):
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# since filling is setitem-like, we require a matching timezone,
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# not just matching tzawawreness
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dti = pd.date_range("2016-01-01", periods=3, tz="UTC")
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dta = dti._data
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fill_value = pd.Timestamp("2020-10-18 18:44", tz="US/Pacific")
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msg = "Timezones don't match. 'UTC' != 'US/Pacific'"
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with pytest.raises(ValueError, match=msg):
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with tm.assert_produces_warning(
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FutureWarning, match="mismatched timezones"
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):
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dta.shift(1, fill_value=fill_value)
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# once deprecation is enforced
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# expected = dta.shift(1, fill_value=fill_value.tz_convert("UTC"))
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# tm.assert_equal(result, expected)
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def test_tz_localize_t2d(self):
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dti = pd.date_range("1994-05-12", periods=12, tz="US/Pacific")
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dta = dti._data.reshape(3, 4)
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result = dta.tz_localize(None)
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expected = dta.ravel().tz_localize(None).reshape(dta.shape)
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tm.assert_datetime_array_equal(result, expected)
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roundtrip = expected.tz_localize("US/Pacific")
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tm.assert_datetime_array_equal(roundtrip, dta)
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