revert bits

pandas/_libs/tslib.pyx

@@ -456,8 +456,6 @@ cpdef array_to_datetime(
     np.ndarray
         May be datetime64[ns] or object dtype
     tzinfo or None
-    str
-        Inferred resolution
     """
     cdef:
         Py_ssize_t i, n = values.size

pandas/_libs/tslibs/strptime.pyx

@@ -59,7 +59,6 @@ from pandas._libs.tslibs.nattype cimport (
 )
 from pandas._libs.tslibs.np_datetime cimport (
     NPY_DATETIMEUNIT,
-    NPY_FR_ns,
     get_datetime64_unit,
     import_pandas_datetime,
     npy_datetimestruct,
@@ -249,6 +248,7 @@ cdef class DatetimeParseState:
         #  datetime.
         self.found_naive_str = False
         self.found_other = False
+
         self.creso = creso
         self.creso_ever_changed = False
 
@@ -305,7 +305,7 @@ def array_strptime(
     fmt : string-like regex
     exact : matches must be exact if True, search if False
     errors : string specifying error handling, {'raise', 'ignore', 'coerce'}
-    creso : NPY_DATETIMEUNIT, default NPY_FR_ns
+    creso : NPY_DATETIMEUNIT, default NPY_FR_GENERIC
         Set to NPY_FR_GENERIC to infer a resolution.
     """
 
@@ -627,7 +627,7 @@ cdef tzinfo _parse_with_format(
             elif len(s) <= 6:
                 item_reso[0] = NPY_DATETIMEUNIT.NPY_FR_us
             else:
-                item_reso[0] = NPY_FR_ns
+                item_reso[0] = NPY_DATETIMEUNIT.NPY_FR_ns
             # Pad to always return nanoseconds
             s += "0" * (9 - len(s))
             us = long(s)

pandas/tests/tslibs/test_array_to_datetime.py

@@ -15,7 +15,6 @@
     tslib,
 )
 from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
-from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
 
 from pandas import Timestamp
 import pandas._testing as tm
@@ -307,63 +306,3 @@ def test_datetime_subclass(data, expected):
 
     expected = np.array(expected, dtype="M8[us]")
     tm.assert_numpy_array_equal(result, expected)
-
-
-class TestArrayToDatetimeResolutionInference:
-    # TODO: tests that include tzs, ints
-
-    def test_infer_homogeoneous_datetimes(self):
-        dt = datetime(2023, 10, 27, 18, 3, 5, 678000)
-        arr = np.array([dt, dt, dt], dtype=object)
-        result, tz = tslib.array_to_datetime(arr, creso=creso_infer)
-        assert tz is None
-        expected = np.array([dt, dt, dt], dtype="M8[us]")
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_infer_homogeoneous_date_objects(self):
-        dt = datetime(2023, 10, 27, 18, 3, 5, 678000)
-        dt2 = dt.date()
-        arr = np.array([None, dt2, dt2, dt2], dtype=object)
-        result, tz = tslib.array_to_datetime(arr, creso=creso_infer)
-        assert tz is None
-        expected = np.array([np.datetime64("NaT"), dt2, dt2, dt2], dtype="M8[s]")
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_infer_homogeoneous_dt64(self):
-        dt = datetime(2023, 10, 27, 18, 3, 5, 678000)
-        dt64 = np.datetime64(dt, "ms")
-        arr = np.array([None, dt64, dt64, dt64], dtype=object)
-        result, tz = tslib.array_to_datetime(arr, creso=creso_infer)
-        assert tz is None
-        expected = np.array([np.datetime64("NaT"), dt64, dt64, dt64], dtype="M8[ms]")
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_infer_homogeoneous_timestamps(self):
-        dt = datetime(2023, 10, 27, 18, 3, 5, 678000)
-        ts = Timestamp(dt).as_unit("ns")
-        arr = np.array([None, ts, ts, ts], dtype=object)
-        result, tz = tslib.array_to_datetime(arr, creso=creso_infer)
-        assert tz is None
-        expected = np.array([np.datetime64("NaT")] + [ts.asm8] * 3, dtype="M8[ns]")
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_infer_homogeoneous_datetimes_strings(self):
-        item = "2023-10-27 18:03:05.678000"
-        arr = np.array([None, item, item, item], dtype=object)
-        result, tz = tslib.array_to_datetime(arr, creso=creso_infer)
-        assert tz is None
-        expected = np.array([np.datetime64("NaT"), item, item, item], dtype="M8[us]")
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_infer_heterogeneous(self):
-        dtstr = "2023-10-27 18:03:05.678000"
-
-        arr = np.array([dtstr, dtstr[:-3], dtstr[:-7], None], dtype=object)
-        result, tz = tslib.array_to_datetime(arr, creso=creso_infer)
-        assert tz is None
-        expected = np.array(arr, dtype="M8[us]")
-        tm.assert_numpy_array_equal(result, expected)
-
-        result, tz = tslib.array_to_datetime(arr[::-1], creso=creso_infer)
-        assert tz is None
-        tm.assert_numpy_array_equal(result, expected[::-1])

pandas/tests/util/test_hashing.py

@@ -243,16 +243,14 @@ def test_categorical_consistency(s1, categorize):
     tm.assert_series_equal(h1, h3)
 
 
-def test_categorical_with_nan_consistency():
-    c = pd.Categorical.from_codes(
-        [-1, 0, 1, 2, 3, 4], categories=pd.date_range("2012-01-01", periods=5, name="B")
-    )
-    expected = hash_array(c, categorize=False)
-
-    c = pd.Categorical.from_codes(
-        [-1, 0], categories=[pd.Timestamp("2012-01-01").as_unit("ns")]
-    )
-    result = hash_array(c, categorize=False)
+def test_categorical_with_nan_consistency(unit):
+    dti = pd.date_range("2012-01-01", periods=5, name="B", unit=unit)
+    cat = pd.Categorical.from_codes([-1, 0, 1, 2, 3, 4], categories=dti)
+    expected = hash_array(cat, categorize=False)
+
+    ts = pd.Timestamp("2012-01-01").as_unit(unit)
+    cat2 = pd.Categorical.from_codes([-1, 0], categories=[ts])
+    result = hash_array(cat2, categorize=False)
 
     assert result[0] in expected
     assert result[1] in expected