TST: Tests and Helpers for Datetime/Period Arrays (#23502)

Author: jbrockmendel

pandas/_libs/tslibs/offsets.pyx

@@ -346,6 +346,7 @@ class _BaseOffset(object):
 
     def __add__(self, other):
         if getattr(other, "_typ", None) in ["datetimeindex", "periodindex",
+                                            "datetimearray", "periodarray",
                                             "series", "period", "dataframe"]:
             # defer to the other class's implementation
             return other + self

pandas/core/arrays/datetimelike.py

@@ -200,6 +200,9 @@ def astype(self, dtype, copy=True):
     # ------------------------------------------------------------------
     # Null Handling
 
+    def isna(self):
+        return self._isnan
+
     @property  # NB: override with cache_readonly in immutable subclasses
     def _isnan(self):
         """ return if each value is nan"""

pandas/core/arrays/datetimes.py

@@ -117,28 +117,36 @@ def wrapper(self, other):
             return ops.invalid_comparison(self, other, op)
         else:
             if isinstance(other, list):
-                # FIXME: This can break for object-dtype with mixed types
-                other = type(self)(other)
-            elif not isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
+                try:
+                    other = type(self)(other)
+                except ValueError:
+                    other = np.array(other, dtype=np.object_)
+            elif not isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries,
+                                        DatetimeArrayMixin)):
                 # Following Timestamp convention, __eq__ is all-False
                 # and __ne__ is all True, others raise TypeError.
                 return ops.invalid_comparison(self, other, op)
 
             if is_object_dtype(other):
                 result = op(self.astype('O'), np.array(other))
+                o_mask = isna(other)
             elif not (is_datetime64_dtype(other) or
                       is_datetime64tz_dtype(other)):
                 # e.g. is_timedelta64_dtype(other)
                 return ops.invalid_comparison(self, other, op)
             else:
                 self._assert_tzawareness_compat(other)
-                result = meth(self, np.asarray(other))
+                if not hasattr(other, 'asi8'):
+                    # ndarray, Series
+                    other = type(self)(other)
+                result = meth(self, other)
+                o_mask = other._isnan
 
             result = com.values_from_object(result)
 
             # Make sure to pass an array to result[...]; indexing with
             # Series breaks with older version of numpy
-            o_mask = np.array(isna(other))
+            o_mask = np.array(o_mask)
             if o_mask.any():
                 result[o_mask] = nat_result
 
@@ -157,6 +165,7 @@ class DatetimeArrayMixin(dtl.DatetimeLikeArrayMixin):
         _freq
         _data
     """
+    _typ = "datetimearray"
     _bool_ops = ['is_month_start', 'is_month_end',
                  'is_quarter_start', 'is_quarter_end', 'is_year_start',
                  'is_year_end', 'is_leap_year']
@@ -166,6 +175,9 @@ class DatetimeArrayMixin(dtl.DatetimeLikeArrayMixin):
     # by returning NotImplemented
     timetuple = None
 
+    # ensure that operations with numpy arrays defer to our implementation
+    __array_priority__ = 1000
+
     # -----------------------------------------------------------------
     # Constructors
 

pandas/core/arrays/period.py

@@ -403,9 +403,6 @@ def take(self, indices, allow_fill=False, fill_value=None):
 
         return type(self)(new_values, self.freq)
 
-    def isna(self):
-        return self._data == iNaT
-
     def fillna(self, value=None, method=None, limit=None):
         # TODO(#20300)
         # To avoid converting to object, we re-implement here with the changes

pandas/core/arrays/timedeltas.py

@@ -98,6 +98,8 @@ def wrapper(self, other):
 
 
 class TimedeltaArrayMixin(dtl.DatetimeLikeArrayMixin):
+    _typ = "timedeltaarray"
+
     @property
     def _box_func(self):
         return lambda x: Timedelta(x, unit='ns')

pandas/core/dtypes/generic.py

@@ -53,6 +53,10 @@ def _check(cls, inst):
                                         ('sparse_array', 'sparse_series'))
 ABCCategorical = create_pandas_abc_type("ABCCategorical", "_typ",
                                         ("categorical"))
+ABCDatetimeArray = create_pandas_abc_type("ABCDatetimeArray", "_typ",
+                                          ("datetimearray"))
+ABCTimedeltaArray = create_pandas_abc_type("ABCTimedeltaArray", "_typ",
+                                           ("timedeltaarray"))
 ABCPeriodArray = create_pandas_abc_type("ABCPeriodArray", "_typ",
                                         ("periodarray", ))
 ABCPeriod = create_pandas_abc_type("ABCPeriod", "_typ", ("period", ))

pandas/tests/arithmetic/conftest.py

@@ -5,6 +5,7 @@
 import pandas as pd
 
 from pandas.compat import long
+from pandas.core.arrays import PeriodArray, DatetimeArrayMixin as DatetimeArray
 
 
 @pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
@@ -171,3 +172,21 @@ def box_df_broadcast_failure(request):
     the DataFrame operation tries to broadcast incorrectly.
     """
     return request.param
+
+
+@pytest.fixture(params=[pd.Index, pd.Series, pd.DataFrame, PeriodArray],
+                ids=lambda x: x.__name__)
+def box_with_period(request):
+    """
+    Like `box`, but specific to PeriodDtype for also testing PeriodArray
+    """
+    return request.param
+
+
+@pytest.fixture(params=[pd.Index, pd.Series, pd.DataFrame, DatetimeArray],
+                ids=lambda x: x.__name__)
+def box_with_datetime(request):
+    """
+    Like `box`, but specific to datetime64 for also testing DatetimeArray
+    """
+    return request.param

pandas/tests/arithmetic/test_datetime64.py

@@ -1037,10 +1037,10 @@ def test_dti_add_sub_float(self, op, other):
         with pytest.raises(TypeError):
             op(dti, other)
 
-    def test_dti_add_timestamp_raises(self, box):
+    def test_dti_add_timestamp_raises(self, box_with_datetime):
         # GH#22163 ensure DataFrame doesn't cast Timestamp to i8
         idx = DatetimeIndex(['2011-01-01', '2011-01-02'])
-        idx = tm.box_expected(idx, box)
+        idx = tm.box_expected(idx, box_with_datetime)
         msg = "cannot add"
         with tm.assert_raises_regex(TypeError, msg):
             idx + Timestamp('2011-01-01')
@@ -1152,16 +1152,17 @@ def test_dti_add_intarray_no_freq(self, box):
     # -------------------------------------------------------------
     # Binary operations DatetimeIndex and timedelta-like
 
-    def test_dti_add_timedeltalike(self, tz_naive_fixture, two_hours, box):
+    def test_dti_add_timedeltalike(self, tz_naive_fixture, two_hours,
+                                   box_with_datetime):
         # GH#22005, GH#22163 check DataFrame doesn't raise TypeError
         tz = tz_naive_fixture
         rng = pd.date_range('2000-01-01', '2000-02-01', tz=tz)
-        rng = tm.box_expected(rng, box)
+        rng = tm.box_expected(rng, box_with_datetime)
 
         result = rng + two_hours
         expected = pd.date_range('2000-01-01 02:00',
                                  '2000-02-01 02:00', tz=tz)
-        expected = tm.box_expected(expected, box)
+        expected = tm.box_expected(expected, box_with_datetime)
         tm.assert_equal(result, expected)
 
     def test_dti_iadd_timedeltalike(self, tz_naive_fixture, two_hours):
@@ -1431,13 +1432,13 @@ def test_sub_dti_dti(self):
         tm.assert_index_equal(result, expected)
 
     @pytest.mark.parametrize('freq', [None, 'D'])
-    def test_sub_period(self, freq, box):
+    def test_sub_period(self, freq, box_with_datetime):
         # GH#13078
         # not supported, check TypeError
         p = pd.Period('2011-01-01', freq='D')
 
         idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02'], freq=freq)
-        idx = tm.box_expected(idx, box)
+        idx = tm.box_expected(idx, box_with_datetime)
 
         with pytest.raises(TypeError):
             idx - p
@@ -1779,7 +1780,7 @@ def test_dti_with_offset_series(self, tz_naive_fixture, names):
             res3 = dti - other
         tm.assert_series_equal(res3, expected_sub)
 
-    def test_dti_add_offset_tzaware(self, tz_aware_fixture, box):
+    def test_dti_add_offset_tzaware(self, tz_aware_fixture, box_with_datetime):
         # GH#21610, GH#22163 ensure DataFrame doesn't return object-dtype
         timezone = tz_aware_fixture
         if timezone == 'US/Pacific':
@@ -1792,8 +1793,8 @@ def test_dti_add_offset_tzaware(self, tz_aware_fixture, box):
         expected = DatetimeIndex(['2010-11-01 05:00', '2010-11-01 06:00',
                                   '2010-11-01 07:00'], freq='H', tz=timezone)
 
-        dates = tm.box_expected(dates, box)
-        expected = tm.box_expected(expected, box)
+        dates = tm.box_expected(dates, box_with_datetime)
+        expected = tm.box_expected(expected, box_with_datetime)
 
         # TODO: parametrize over the scalar being added?  radd?  sub?
         offset = dates + pd.offsets.Hour(5)

pandas/tests/arithmetic/test_period.py

@@ -579,15 +579,15 @@ def test_pi_add_offset_n_gt1(self, box):
         result = per.freq + pi
         tm.assert_equal(result, expected)
 
-    def test_pi_add_offset_n_gt1_not_divisible(self, box):
+    def test_pi_add_offset_n_gt1_not_divisible(self, box_with_period):
         # GH#23215
         # PeriodIndex with freq.n > 1 add offset with offset.n % freq.n != 0
 
         pi = pd.PeriodIndex(['2016-01'], freq='2M')
-        pi = tm.box_expected(pi, box)
+        pi = tm.box_expected(pi, box_with_period)
 
         expected = pd.PeriodIndex(['2016-04'], freq='2M')
-        expected = tm.box_expected(expected, box)
+        expected = tm.box_expected(expected, box_with_period)
 
         result = pi + to_offset('3M')
         tm.assert_equal(result, expected)
@@ -901,10 +901,10 @@ def test_pi_ops(self):
         tm.assert_index_equal(result, exp)
 
     @pytest.mark.parametrize('ng', ["str", 1.5])
-    def test_pi_ops_errors(self, ng, box):
+    def test_pi_ops_errors(self, ng, box_with_period):
         idx = PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04'],
                           freq='M', name='idx')
-        obj = tm.box_expected(idx, box)
+        obj = tm.box_expected(idx, box_with_period)
 
         msg = r"unsupported operand type\(s\)"
         with tm.assert_raises_regex(TypeError, msg):

pandas/tests/arrays/test_datetimes.py

@@ -0,0 +1,43 @@
+"""
+Tests for DatetimeArray
+"""
+import operator
+
+import numpy as np
+
+import pandas as pd
+from pandas.core.arrays import DatetimeArrayMixin as DatetimeArray
+import pandas.util.testing as tm
+
+
+class TestDatetimeArrayComparisons(object):
+    # TODO: merge this into tests/arithmetic/test_datetime64 once it is
+    #  sufficiently robust
+
+    def test_cmp_dt64_arraylike_tznaive(self, all_compare_operators):
+        # arbitrary tz-naive DatetimeIndex
+        opname = all_compare_operators.strip('_')
+        op = getattr(operator, opname)
+
+        dti = pd.date_range('2016-01-1', freq='MS', periods=9, tz=None)
+        arr = DatetimeArray(dti)
+        assert arr.freq == dti.freq
+        assert arr.tz == dti.tz
+
+        right = dti
+
+        expected = np.ones(len(arr), dtype=bool)
+        if opname in ['ne', 'gt', 'lt']:
+            # for these the comparisons should be all-False
+            expected = ~expected
+
+        result = op(arr, arr)
+        tm.assert_numpy_array_equal(result, expected)
+        for other in [right, np.array(right)]:
+            # TODO: add list and tuple, and object-dtype once those
+            #  are fixed in the constructor
+            result = op(arr, other)
+            tm.assert_numpy_array_equal(result, expected)
+
+            result = op(other, arr)
+            tm.assert_numpy_array_equal(result, expected)