CLN: Refactor pandas/tests/base - part3

pandas/tests/base/test_ops.py

@@ -1,6 +1,7 @@
 from datetime import datetime, timedelta
 from io import StringIO
 import sys
+from typing import Any
 
 import numpy as np
 import pytest
@@ -30,17 +31,15 @@
     Timestamp,
 )
 import pandas._testing as tm
-from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
 
 
-class Ops:
-    def _allow_na_ops(self, obj):
-        """Whether to skip test cases including NaN"""
-        if (isinstance(obj, Index) and obj.is_boolean()) or not obj._can_hold_na:
-            # don't test boolean / integer dtypes
-            return False
-        return True
+def allow_na_ops(obj: Any) -> bool:
+    """Whether to skip test cases including NaN"""
+    is_bool_index = isinstance(obj, Index) and obj.is_boolean()
+    return not is_bool_index and obj._can_hold_na
+
 
+class Ops:
     def setup_method(self, method):
         self.bool_index = tm.makeBoolIndex(10, name="a")
         self.int_index = tm.makeIntIndex(10, name="a")
@@ -83,74 +82,29 @@ def setup_method(self, method):
 
         self.objs = self.indexes + self.series + self.narrow_series
 
-    def check_ops_properties(self, props, filter=None, ignore_failures=False):
-        for op in props:
-            for o in self.is_valid_objs:
-
-                # if a filter, skip if it doesn't match
-                if filter is not None:
-                    filt = o.index if isinstance(o, Series) else o
-                    if not filter(filt):
-                        continue
-
-                try:
-                    if isinstance(o, Series):
-                        expected = Series(getattr(o.index, op), index=o.index, name="a")
-                    else:
-                        expected = getattr(o, op)
-                except (AttributeError):
-                    if ignore_failures:
-                        continue
-
-                result = getattr(o, op)
-
-                # these could be series, arrays or scalars
-                if isinstance(result, Series) and isinstance(expected, Series):
-                    tm.assert_series_equal(result, expected)
-                elif isinstance(result, Index) and isinstance(expected, Index):
-                    tm.assert_index_equal(result, expected)
-                elif isinstance(result, np.ndarray) and isinstance(
-                    expected, np.ndarray
-                ):
-                    tm.assert_numpy_array_equal(result, expected)
-                else:
-                    assert result == expected
-
-            # freq raises AttributeError on an Int64Index because its not
-            # defined we mostly care about Series here anyhow
-            if not ignore_failures:
-                for o in self.not_valid_objs:
-
-                    # an object that is datetimelike will raise a TypeError,
-                    # otherwise an AttributeError
-                    msg = "no attribute"
-                    err = AttributeError
-                    if issubclass(type(o), DatetimeIndexOpsMixin):
-                        err = TypeError
-                    with pytest.raises(err, match=msg):
-                        getattr(o, op)
-
-    @pytest.mark.parametrize("klass", [Series, DataFrame])
-    def test_binary_ops_docs(self, klass):
-        op_map = {
-            "add": "+",
-            "sub": "-",
-            "mul": "*",
-            "mod": "%",
-            "pow": "**",
-            "truediv": "/",
-            "floordiv": "//",
-        }
-        for op_name in op_map:
-            operand1 = klass.__name__.lower()
-            operand2 = "other"
-            op = op_map[op_name]
-            expected_str = " ".join([operand1, op, operand2])
-            assert expected_str in getattr(klass, op_name).__doc__
-
-            # reverse version of the binary ops
-            expected_str = " ".join([operand2, op, operand1])
-            assert expected_str in getattr(klass, "r" + op_name).__doc__
+
+@pytest.mark.parametrize("klass", [Series, DataFrame])
+@pytest.mark.parametrize(
+    "op_name, op",
+    [
+        ("add", "+"),
+        ("sub", "-"),
+        ("mul", "*"),
+        ("mod", "%"),
+        ("pow", "**"),
+        ("truediv", "/"),
+        ("floordiv", "//"),
+    ],
+)
+def test_binary_ops_docs(klass, op_name, op):
+    operand1 = klass.__name__.lower()
+    operand2 = "other"
+    expected_str = " ".join([operand1, op, operand2])
+    assert expected_str in getattr(klass, op_name).__doc__
+
+    # reverse version of the binary ops
+    expected_str = " ".join([operand2, op, operand1])
+    assert expected_str in getattr(klass, "r" + op_name).__doc__
 
 
 class TestTranspose(Ops):
@@ -313,7 +267,7 @@ def test_value_counts_unique_nunique_null(self, null_obj):
             klass = type(o)
             values = o._ndarray_values
 
-            if not self._allow_na_ops(o):
+            if not allow_na_ops(o):
                 continue
 
             # special assign to the numpy array
@@ -794,7 +748,7 @@ def test_fillna(self):
                 o = orig.copy()
                 klass = type(o)
 
-                if not self._allow_na_ops(o):
+                if not allow_na_ops(o):
                     continue
 
                 if needs_i8_conversion(o):

pandas/tests/indexes/common.py

@@ -298,7 +298,7 @@ def test_ensure_copied_data(self, indices):
         elif isinstance(indices, (RangeIndex, MultiIndex, CategoricalIndex)):
             # RangeIndex cannot be initialized from data
             # MultiIndex and CategoricalIndex are tested separately
-            return
+            pytest.skip()
 
         index_type = type(indices)
         result = index_type(indices.values, copy=True, **init_kwargs)
@@ -461,7 +461,7 @@ def test_set_ops_error_cases(self, case, method, indices):
 
     def test_intersection_base(self, indices):
         if isinstance(indices, CategoricalIndex):
-            return
+            pytest.skip()
 
         first = indices[:5]
         second = indices[:3]

pandas/tests/indexes/conftest.py

@@ -14,7 +14,7 @@
     "uint": tm.makeUIntIndex(100),
     "range": tm.makeRangeIndex(100),
     "float": tm.makeFloatIndex(100),
-    "bool": Index([True, False]),
+    "bool": tm.makeBoolIndex(2),
     "categorical": tm.makeCategoricalIndex(100),
     "interval": tm.makeIntervalIndex(100),
     "empty": Index([]),

pandas/tests/indexes/datetimes/test_ops.py

@@ -7,37 +7,16 @@
 from pandas.core.dtypes.generic import ABCDateOffset
 
 import pandas as pd
-from pandas import (
-    DatetimeIndex,
-    Index,
-    PeriodIndex,
-    Series,
-    Timestamp,
-    bdate_range,
-    date_range,
-)
+from pandas import DatetimeIndex, Index, Series, Timestamp, bdate_range, date_range
 import pandas._testing as tm
-from pandas.tests.base.test_ops import Ops
 
 from pandas.tseries.offsets import BDay, BMonthEnd, CDay, Day, Hour
 
 START, END = datetime(2009, 1, 1), datetime(2010, 1, 1)
 
 
-class TestDatetimeIndexOps(Ops):
-    def setup_method(self, method):
-        super().setup_method(method)
-        mask = lambda x: (isinstance(x, DatetimeIndex) or isinstance(x, PeriodIndex))
-        self.is_valid_objs = [o for o in self.objs if mask(o)]
-        self.not_valid_objs = [o for o in self.objs if not mask(o)]
-
-    def test_ops_properties(self):
-        f = lambda x: isinstance(x, DatetimeIndex)
-        self.check_ops_properties(DatetimeIndex._field_ops, f)
-        self.check_ops_properties(DatetimeIndex._object_ops, f)
-        self.check_ops_properties(DatetimeIndex._bool_ops, f)
-
-    def test_ops_properties_basic(self):
+class TestDatetimeIndexOps:
+    def test_ops_properties_basic(self, datetime_series):
 
         # sanity check that the behavior didn't change
         # GH#7206

pandas/tests/indexes/period/test_ops.py

@@ -2,25 +2,11 @@
 import pytest
 
 import pandas as pd
-from pandas import DatetimeIndex, Index, NaT, PeriodIndex, Series
+from pandas import Index, NaT, PeriodIndex, Series
 import pandas._testing as tm
-from pandas.core.arrays import PeriodArray
-from pandas.tests.base.test_ops import Ops
 
 
-class TestPeriodIndexOps(Ops):
-    def setup_method(self, method):
-        super().setup_method(method)
-        mask = lambda x: (isinstance(x, DatetimeIndex) or isinstance(x, PeriodIndex))
-        self.is_valid_objs = [o for o in self.objs if mask(o)]
-        self.not_valid_objs = [o for o in self.objs if not mask(o)]
-
-    def test_ops_properties(self):
-        f = lambda x: isinstance(x, PeriodIndex)
-        self.check_ops_properties(PeriodArray._field_ops, f)
-        self.check_ops_properties(PeriodArray._object_ops, f)
-        self.check_ops_properties(PeriodArray._bool_ops, f)
-
+class TestPeriodIndexOps:
     def test_resolution(self):
         for freq, expected in zip(
             ["A", "Q", "M", "D", "H", "T", "S", "L", "U"],

pandas/tests/indexes/timedeltas/test_ops.py

@@ -8,26 +8,13 @@
 import pandas as pd
 from pandas import Series, TimedeltaIndex, timedelta_range
 import pandas._testing as tm
-from pandas.tests.base.test_ops import Ops
 
 from pandas.tseries.offsets import Day, Hour
 
 
-class TestTimedeltaIndexOps(Ops):
-    def setup_method(self, method):
-        super().setup_method(method)
-        mask = lambda x: isinstance(x, TimedeltaIndex)
-        self.is_valid_objs = [o for o in self.objs if mask(o)]
-        self.not_valid_objs = []
-
-    def test_ops_properties(self):
-        f = lambda x: isinstance(x, TimedeltaIndex)
-        self.check_ops_properties(TimedeltaIndex._field_ops, f)
-        self.check_ops_properties(TimedeltaIndex._object_ops, f)
-
+class TestTimedeltaIndexOps:
     def test_value_counts_unique(self):
         # GH 7735
-
         idx = timedelta_range("1 days 09:00:00", freq="H", periods=10)
         # create repeated values, 'n'th element is repeated by n+1 times
         idx = TimedeltaIndex(np.repeat(idx.values, range(1, len(idx) + 1)))