TST/REF: fixturize (#43918)

Author: jbrockmendel

pandas/tests/indexes/categorical/test_indexing.py

@@ -300,8 +300,9 @@ def test_get_indexer_same_categories_different_order(self):
 
 
 class TestWhere:
-    @pytest.mark.parametrize("klass", [list, tuple, np.array, pd.Series])
-    def test_where(self, klass):
+    def test_where(self, listlike_box_with_tuple):
+        klass = listlike_box_with_tuple
+
         i = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
         cond = [True] * len(i)
         expected = i

pandas/tests/indexes/common.py

@@ -378,8 +378,9 @@ def test_numpy_repeat(self, simple_index):
         with pytest.raises(ValueError, match=msg):
             np.repeat(idx, rep, axis=0)
 
-    @pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
-    def test_where(self, klass, simple_index):
+    def test_where(self, listlike_box_with_tuple, simple_index):
+        klass = listlike_box_with_tuple
+
         idx = simple_index
         if isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
             # where does not preserve freq

pandas/tests/indexes/conftest.py

@@ -1,5 +1,11 @@
+import numpy as np
 import pytest
 
+from pandas import (
+    Series,
+    array,
+)
+
 
 @pytest.fixture(params=[None, False])
 def sort(request):
@@ -25,3 +31,21 @@ def freq_sample(request):
     timedelta_range..
     """
     return request.param
+
+
+@pytest.fixture(params=[list, np.array, array, Series])
+def listlike_box(request):
+    """
+    Types that may be passed as the indexer to searchsorted.
+    """
+    return request.param
+
+
+# TODO: not clear if this _needs_ to be different from listlike_box or
+#  if that is just a historical artifact
+@pytest.fixture(params=[list, tuple, np.array, Series])
+def listlike_box_with_tuple(request):
+    """
+    Types that may be passed as the indexer to searchsorted.
+    """
+    return request.param

pandas/tests/indexes/datetimes/test_datetime.py

@@ -17,29 +17,6 @@
 
 
 class TestDatetimeIndex:
-    def test_time_loc(self):  # GH8667
-        from datetime import time
-
-        from pandas._libs.index import _SIZE_CUTOFF
-
-        ns = _SIZE_CUTOFF + np.array([-100, 100], dtype=np.int64)
-        key = time(15, 11, 30)
-        start = key.hour * 3600 + key.minute * 60 + key.second
-        step = 24 * 3600
-
-        for n in ns:
-            idx = date_range("2014-11-26", periods=n, freq="S")
-            ts = pd.Series(np.random.randn(n), index=idx)
-            i = np.arange(start, n, step)
-
-            tm.assert_numpy_array_equal(ts.index.get_loc(key), i, check_dtype=False)
-            tm.assert_series_equal(ts[key], ts.iloc[i])
-
-            left, right = ts.copy(), ts.copy()
-            left[key] *= -10
-            right.iloc[i] *= -10
-            tm.assert_series_equal(left, right)
-
     def test_time_overflow_for_32bit_machines(self):
         # GH8943.  On some machines NumPy defaults to np.int32 (for example,
         # 32-bit Linux machines).  In the function _generate_regular_range
@@ -78,13 +55,6 @@ def test_week_of_month_frequency(self):
         expected = DatetimeIndex(dates, freq="WOM-1SAT")
         tm.assert_index_equal(result, expected)
 
-    def test_stringified_slice_with_tz(self):
-        # GH#2658
-        start = "2013-01-07"
-        idx = date_range(start=start, freq="1d", periods=10, tz="US/Eastern")
-        df = DataFrame(np.arange(10), index=idx)
-        df["2013-01-14 23:44:34.437768-05:00":]  # no exception here
-
     def test_append_nondatetimeindex(self):
         rng = date_range("1/1/2000", periods=10)
         idx = Index(["a", "b", "c", "d"])
@@ -137,16 +107,6 @@ def test_misc_coverage(self):
         result = rng.groupby(rng.day)
         assert isinstance(list(result.values())[0][0], Timestamp)
 
-    def test_string_index_series_name_converted(self):
-        # #1644
-        df = DataFrame(np.random.randn(10, 4), index=date_range("1/1/2000", periods=10))
-
-        result = df.loc["1/3/2000"]
-        assert result.name == df.index[2]
-
-        result = df.T["1/3/2000"]
-        assert result.name == df.index[2]
-
     def test_groupby_function_tuple_1677(self):
         df = DataFrame(np.random.rand(100), index=date_range("1/1/2000", periods=100))
         monthly_group = df.groupby(lambda x: (x.year, x.month))

pandas/tests/indexes/datetimes/test_indexing.py

@@ -507,6 +507,30 @@ def test_get_loc_time_obj(self):
             with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                 idx.get_loc(time(12, 30), method="pad")
 
+    def test_get_loc_time_obj2(self):
+        # GH#8667
+
+        from pandas._libs.index import _SIZE_CUTOFF
+
+        ns = _SIZE_CUTOFF + np.array([-100, 100], dtype=np.int64)
+        key = time(15, 11, 30)
+        start = key.hour * 3600 + key.minute * 60 + key.second
+        step = 24 * 3600
+
+        for n in ns:
+            idx = date_range("2014-11-26", periods=n, freq="S")
+            ts = pd.Series(np.random.randn(n), index=idx)
+            locs = np.arange(start, n, step, dtype=np.intp)
+
+            result = ts.index.get_loc(key)
+            tm.assert_numpy_array_equal(result, locs)
+            tm.assert_series_equal(ts[key], ts.iloc[locs])
+
+            left, right = ts.copy(), ts.copy()
+            left[key] *= -10
+            right.iloc[locs] *= -10
+            tm.assert_series_equal(left, right)
+
     def test_get_loc_time_nat(self):
         # GH#35114
         # Case where key's total microseconds happens to match iNaT % 1e6 // 1000
@@ -705,7 +729,7 @@ def test_maybe_cast_slice_duplicate_monotonic(self):
         assert result == expected
 
 
-class TestDatetimeIndex:
+class TestGetValue:
     def test_get_value(self):
         # specifically make sure we have test for np.datetime64 key
         dti = date_range("2016-01-01", periods=3)

pandas/tests/indexes/datetimes/test_partial_slicing.py

@@ -19,6 +19,23 @@
 
 
 class TestSlicing:
+    def test_string_index_series_name_converted(self):
+        # GH#1644
+        df = DataFrame(np.random.randn(10, 4), index=date_range("1/1/2000", periods=10))
+
+        result = df.loc["1/3/2000"]
+        assert result.name == df.index[2]
+
+        result = df.T["1/3/2000"]
+        assert result.name == df.index[2]
+
+    def test_stringified_slice_with_tz(self):
+        # GH#2658
+        start = "2013-01-07"
+        idx = date_range(start=start, freq="1d", periods=10, tz="US/Eastern")
+        df = DataFrame(np.arange(10), index=idx)
+        df["2013-01-14 23:44:34.437768-05:00":]  # no exception here
+
     def test_return_type_doesnt_depend_on_monotonicity(self):
         # GH#24892 we get Series back regardless of whether our DTI is monotonic
         dti = date_range(start="2015-5-13 23:59:00", freq="min", periods=3)

pandas/tests/indexes/interval/test_base.py

@@ -1,10 +1,7 @@
 import numpy as np
 import pytest
 
-from pandas import (
-    IntervalIndex,
-    Series,
-)
+from pandas import IntervalIndex
 import pandas._testing as tm
 from pandas.tests.indexes.common import Base
 
@@ -46,8 +43,9 @@ def test_take(self, closed):
         expected = IntervalIndex.from_arrays([0, 0, 1], [1, 1, 2], closed=closed)
         tm.assert_index_equal(result, expected)
 
-    @pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
-    def test_where(self, simple_index, klass):
+    def test_where(self, simple_index, listlike_box_with_tuple):
+        klass = listlike_box_with_tuple
+
         idx = simple_index
         cond = [True] * len(idx)
         expected = idx

pandas/tests/indexes/interval/test_interval.py

@@ -934,15 +934,14 @@ def test_dir():
     assert "str" not in result
 
 
-@pytest.mark.parametrize("klass", [list, np.array, pd.array, pd.Series])
-def test_searchsorted_different_argument_classes(klass):
+def test_searchsorted_different_argument_classes(listlike_box):
     # https://github.com/pandas-dev/pandas/issues/32762
     values = IntervalIndex([Interval(0, 1), Interval(1, 2)])
-    result = values.searchsorted(klass(values))
+    result = values.searchsorted(listlike_box(values))
     expected = np.array([0, 1], dtype=result.dtype)
     tm.assert_numpy_array_equal(result, expected)
 
-    result = values._data.searchsorted(klass(values))
+    result = values._data.searchsorted(listlike_box(values))
     tm.assert_numpy_array_equal(result, expected)
 
 

pandas/tests/indexes/multi/test_indexing.py

@@ -720,13 +720,12 @@ def test_where(self):
         with pytest.raises(NotImplementedError, match=msg):
             i.where(True)
 
-    @pytest.mark.parametrize("klass", [list, tuple, np.array, pd.Series])
-    def test_where_array_like(self, klass):
-        i = MultiIndex.from_tuples([("A", 1), ("A", 2)])
+    def test_where_array_like(self, listlike_box_with_tuple):
+        mi = MultiIndex.from_tuples([("A", 1), ("A", 2)])
         cond = [False, True]
         msg = r"\.where is not supported for MultiIndex operations"
         with pytest.raises(NotImplementedError, match=msg):
-            i.where(klass(cond))
+            mi.where(listlike_box_with_tuple(cond))
 
 
 class TestContains:

pandas/tests/indexes/numeric/test_indexing.py

@@ -397,15 +397,14 @@ class TestWhere:
             UInt64Index(np.arange(5, dtype="uint64")),
         ],
     )
-    @pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
-    def test_where(self, klass, index):
+    def test_where(self, listlike_box_with_tuple, index):
         cond = [True] * len(index)
         expected = index
-        result = index.where(klass(cond))
+        result = index.where(listlike_box_with_tuple(cond))
 
         cond = [False] + [True] * (len(index) - 1)
         expected = Float64Index([index._na_value] + index[1:].tolist())
-        result = index.where(klass(cond))
+        result = index.where(listlike_box_with_tuple(cond))
         tm.assert_index_equal(result, expected)
 
     def test_where_uint64(self):

pandas/tests/indexes/period/test_indexing.py

@@ -602,17 +602,16 @@ def test_get_indexer2(self):
 
 
 class TestWhere:
-    @pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
-    def test_where(self, klass):
+    def test_where(self, listlike_box_with_tuple):
         i = period_range("20130101", periods=5, freq="D")
         cond = [True] * len(i)
         expected = i
-        result = i.where(klass(cond))
+        result = i.where(listlike_box_with_tuple(cond))
         tm.assert_index_equal(result, expected)
 
         cond = [False] + [True] * (len(i) - 1)
         expected = PeriodIndex([NaT] + i[1:].tolist(), freq="D")
-        result = i.where(klass(cond))
+        result = i.where(listlike_box_with_tuple(cond))
         tm.assert_index_equal(result, expected)
 
     def test_where_other(self):

pandas/tests/indexes/period/test_searchsorted.py

@@ -7,8 +7,6 @@
     NaT,
     Period,
     PeriodIndex,
-    Series,
-    array,
 )
 import pandas._testing as tm
 
@@ -37,17 +35,16 @@ def test_searchsorted(self, freq):
         with pytest.raises(IncompatibleFrequency, match=msg):
             pidx.searchsorted(Period("2014-01-01", freq="5D"))
 
-    @pytest.mark.parametrize("klass", [list, np.array, array, Series])
-    def test_searchsorted_different_argument_classes(self, klass):
+    def test_searchsorted_different_argument_classes(self, listlike_box):
         pidx = PeriodIndex(
             ["2014-01-01", "2014-01-02", "2014-01-03", "2014-01-04", "2014-01-05"],
             freq="D",
         )
-        result = pidx.searchsorted(klass(pidx))
+        result = pidx.searchsorted(listlike_box(pidx))
         expected = np.arange(len(pidx), dtype=result.dtype)
         tm.assert_numpy_array_equal(result, expected)
 
-        result = pidx._data.searchsorted(klass(pidx))
+        result = pidx._data.searchsorted(listlike_box(pidx))
         tm.assert_numpy_array_equal(result, expected)
 
     def test_searchsorted_invalid(self):

pandas/tests/indexes/timedeltas/test_searchsorted.py

@@ -2,23 +2,20 @@
 import pytest
 
 from pandas import (
-    Series,
     TimedeltaIndex,
     Timestamp,
-    array,
 )
 import pandas._testing as tm
 
 
 class TestSearchSorted:
-    @pytest.mark.parametrize("klass", [list, np.array, array, Series])
-    def test_searchsorted_different_argument_classes(self, klass):
+    def test_searchsorted_different_argument_classes(self, listlike_box):
         idx = TimedeltaIndex(["1 day", "2 days", "3 days"])
-        result = idx.searchsorted(klass(idx))
+        result = idx.searchsorted(listlike_box(idx))
         expected = np.arange(len(idx), dtype=result.dtype)
         tm.assert_numpy_array_equal(result, expected)
 
-        result = idx._data.searchsorted(klass(idx))
+        result = idx._data.searchsorted(listlike_box(idx))
         tm.assert_numpy_array_equal(result, expected)
 
     @pytest.mark.parametrize(