TST/REF: directories for test_period, test_datetimes, test_timedeltas

pandas/tests/arrays/datetimes/test_constructors.py

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+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+from pandas.core.arrays.datetimes import sequence_to_dt64ns
+
+
+class TestDatetimeArrayConstructor:
+    def test_from_sequence_invalid_type(self):
+        mi = pd.MultiIndex.from_product([np.arange(5), np.arange(5)])
+        with pytest.raises(TypeError, match="Cannot create a DatetimeArray"):
+            DatetimeArray._from_sequence(mi)
+
+    def test_only_1dim_accepted(self):
+        arr = np.array([0, 1, 2, 3], dtype="M8[h]").astype("M8[ns]")
+
+        with pytest.raises(ValueError, match="Only 1-dimensional"):
+            # 3-dim, we allow 2D to sneak in for ops purposes GH#29853
+            DatetimeArray(arr.reshape(2, 2, 1))
+
+        with pytest.raises(ValueError, match="Only 1-dimensional"):
+            # 0-dim
+            DatetimeArray(arr[[0]].squeeze())
+
+    def test_freq_validation(self):
+        # GH#24623 check that invalid instances cannot be created with the
+        #  public constructor
+        arr = np.arange(5, dtype=np.int64) * 3600 * 10 ** 9
+
+        msg = (
+            "Inferred frequency H from passed values does not "
+            "conform to passed frequency W-SUN"
+        )
+        with pytest.raises(ValueError, match=msg):
+            DatetimeArray(arr, freq="W")
+
+    @pytest.mark.parametrize(
+        "meth",
+        [
+            DatetimeArray._from_sequence,
+            sequence_to_dt64ns,
+            pd.to_datetime,
+            pd.DatetimeIndex,
+        ],
+    )
+    def test_mixing_naive_tzaware_raises(self, meth):
+        # GH#24569
+        arr = np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")])
+
+        msg = (
+            "Cannot mix tz-aware with tz-naive values|"
+            "Tz-aware datetime.datetime cannot be converted "
+            "to datetime64 unless utc=True"
+        )
+
+        for obj in [arr, arr[::-1]]:
+            # check that we raise regardless of whether naive is found
+            #  before aware or vice-versa
+            with pytest.raises(ValueError, match=msg):
+                meth(obj)
+
+    def test_from_pandas_array(self):
+        arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10 ** 9
+
+        result = DatetimeArray._from_sequence(arr)._with_freq("infer")
+
+        expected = pd.date_range("1970-01-01", periods=5, freq="H")._data
+        tm.assert_datetime_array_equal(result, expected)
+
+    def test_mismatched_timezone_raises(self):
+        arr = DatetimeArray(
+            np.array(["2000-01-01T06:00:00"], dtype="M8[ns]"),
+            dtype=DatetimeTZDtype(tz="US/Central"),
+        )
+        dtype = DatetimeTZDtype(tz="US/Eastern")
+        with pytest.raises(TypeError, match="Timezone of the array"):
+            DatetimeArray(arr, dtype=dtype)
+
+    def test_non_array_raises(self):
+        with pytest.raises(ValueError, match="list"):
+            DatetimeArray([1, 2, 3])
+
+    def test_bool_dtype_raises(self):
+        arr = np.array([1, 2, 3], dtype="bool")
+
+        with pytest.raises(
+            ValueError, match="The dtype of 'values' is incorrect.*bool"
+        ):
+            DatetimeArray(arr)
+
+        msg = r"dtype bool cannot be converted to datetime64\[ns\]"
+        with pytest.raises(TypeError, match=msg):
+            DatetimeArray._from_sequence(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            sequence_to_dt64ns(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            pd.DatetimeIndex(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            pd.to_datetime(arr)
+
+    def test_incorrect_dtype_raises(self):
+        with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
+            DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
+
+    def test_freq_infer_raises(self):
+        with pytest.raises(ValueError, match="Frequency inference"):
+            DatetimeArray(np.array([1, 2, 3], dtype="i8"), freq="infer")
+
+    def test_copy(self):
+        data = np.array([1, 2, 3], dtype="M8[ns]")
+        arr = DatetimeArray(data, copy=False)
+        assert arr._data is data
+
+        arr = DatetimeArray(data, copy=True)
+        assert arr._data is not data
+
+
+class TestSequenceToDT64NS:
+    def test_tz_dtype_mismatch_raises(self):
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
+        with pytest.raises(TypeError, match="data is already tz-aware"):
+            sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="UTC"))
+
+    def test_tz_dtype_matches(self):
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
+        result, _, _ = sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="US/Central"))
+        tm.assert_numpy_array_equal(arr._data, result)
+
+    @pytest.mark.parametrize("order", ["F", "C"])
+    def test_2d(self, order):
+        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
+        arr = np.array(dti, dtype=object).reshape(3, 2)
+        if order == "F":
+            arr = arr.T
+
+        res = sequence_to_dt64ns(arr)
+        expected = sequence_to_dt64ns(arr.ravel())
+
+        tm.assert_numpy_array_equal(res[0].ravel(), expected[0])
+        assert res[1] == expected[1]
+        assert res[2] == expected[2]
+
+        res = DatetimeArray._from_sequence(arr)
+        expected = DatetimeArray._from_sequence(arr.ravel()).reshape(arr.shape)
+        tm.assert_datetime_array_equal(res, expected)

pandas/tests/arrays/datetimes/test_reductions.py

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+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+from pandas import NaT
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+
+
+class TestReductions:
+    @pytest.fixture
+    def arr1d(self, tz_naive_fixture):
+        tz = tz_naive_fixture
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence(
+            [
+                "2000-01-03",
+                "2000-01-03",
+                "NaT",
+                "2000-01-02",
+                "2000-01-05",
+                "2000-01-04",
+            ],
+            dtype=dtype,
+        )
+        return arr
+
+    def test_min_max(self, arr1d):
+        arr = arr1d
+        tz = arr.tz
+
+        result = arr.min()
+        expected = pd.Timestamp("2000-01-02", tz=tz)
+        assert result == expected
+
+        result = arr.max()
+        expected = pd.Timestamp("2000-01-05", tz=tz)
+        assert result == expected
+
+        result = arr.min(skipna=False)
+        assert result is pd.NaT
+
+        result = arr.max(skipna=False)
+        assert result is pd.NaT
+
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_min_max_empty(self, skipna, tz):
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence([], dtype=dtype)
+        result = arr.min(skipna=skipna)
+        assert result is pd.NaT
+
+        result = arr.max(skipna=skipna)
+        assert result is pd.NaT
+
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_median_empty(self, skipna, tz):
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence([], dtype=dtype)
+        result = arr.median(skipna=skipna)
+        assert result is pd.NaT
+
+        arr = arr.reshape(0, 3)
+        result = arr.median(axis=0, skipna=skipna)
+        expected = type(arr)._from_sequence([pd.NaT, pd.NaT, pd.NaT], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+        result = arr.median(axis=1, skipna=skipna)
+        expected = type(arr)._from_sequence([], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+    def test_median(self, arr1d):
+        arr = arr1d
+
+        result = arr.median()
+        assert result == arr[0]
+        result = arr.median(skipna=False)
+        assert result is pd.NaT
+
+        result = arr.dropna().median(skipna=False)
+        assert result == arr[0]
+
+        result = arr.median(axis=0)
+        assert result == arr[0]
+
+    def test_median_axis(self, arr1d):
+        arr = arr1d
+        assert arr.median(axis=0) == arr.median()
+        assert arr.median(axis=0, skipna=False) is pd.NaT
+
+        msg = r"abs\(axis\) must be less than ndim"
+        with pytest.raises(ValueError, match=msg):
+            arr.median(axis=1)
+
+    @pytest.mark.filterwarnings("ignore:All-NaN slice encountered:RuntimeWarning")
+    def test_median_2d(self, arr1d):
+        arr = arr1d.reshape(1, -1)
+
+        # axis = None
+        assert arr.median() == arr1d.median()
+        assert arr.median(skipna=False) is pd.NaT
+
+        # axis = 0
+        result = arr.median(axis=0)
+        expected = arr1d
+        tm.assert_equal(result, expected)
+
+        # Since column 3 is all-NaT, we get NaT there with or without skipna
+        result = arr.median(axis=0, skipna=False)
+        expected = arr1d
+        tm.assert_equal(result, expected)
+
+        # axis = 1
+        result = arr.median(axis=1)
+        expected = type(arr)._from_sequence([arr1d.median()])
+        tm.assert_equal(result, expected)
+
+        result = arr.median(axis=1, skipna=False)
+        expected = type(arr)._from_sequence([pd.NaT], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+    def test_mean(self, arr1d):
+        arr = arr1d
+
+        # manually verified result
+        expected = arr[0] + 0.4 * pd.Timedelta(days=1)
+
+        result = arr.mean()
+        assert result == expected
+        result = arr.mean(skipna=False)
+        assert result is pd.NaT
+
+        result = arr.dropna().mean(skipna=False)
+        assert result == expected
+
+        result = arr.mean(axis=0)
+        assert result == expected
+
+    def test_mean_2d(self):
+        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
+        dta = dti._data.reshape(3, 2)
+
+        result = dta.mean(axis=0)
+        expected = dta[1]
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = dta.mean(axis=1)
+        expected = dta[:, 0] + pd.Timedelta(hours=12)
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = dta.mean(axis=None)
+        expected = dti.mean()
+        assert result == expected
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_mean_empty(self, arr1d, skipna):
+        arr = arr1d[:0]
+
+        assert arr.mean(skipna=skipna) is NaT
+
+        arr2d = arr.reshape(0, 3)
+        result = arr2d.mean(axis=0, skipna=skipna)
+        expected = DatetimeArray._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr2d.mean(axis=1, skipna=skipna)
+        expected = arr  # i.e. 1D, empty
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr2d.mean(axis=None, skipna=skipna)
+        assert result is NaT