REF: parametrize indexing tests

pandas/tests/indexing/test_floats.py

@@ -22,16 +22,9 @@ def check(self, result, original, indexer, getitem):
 
         tm.assert_almost_equal(result, expected)
 
-    def test_scalar_error(self):
-
-        # GH 4892
-        # float_indexers should raise exceptions
-        # on appropriate Index types & accessors
-        # this duplicates the code below
-        # but is specifically testing for the error
-        # message
-
-        for index in [
+    @pytest.mark.parametrize(
+        "index_func",
+        [
             tm.makeStringIndex,
             tm.makeUnicodeIndex,
             tm.makeCategoricalIndex,
@@ -40,22 +33,31 @@ def test_scalar_error(self):
             tm.makePeriodIndex,
             tm.makeIntIndex,
             tm.makeRangeIndex,
-        ]:
+        ],
+    )
+    def test_scalar_error(self, index_func):
 
-            i = index(5)
+        # GH 4892
+        # float_indexers should raise exceptions
+        # on appropriate Index types & accessors
+        # this duplicates the code below
+        # but is specifically testing for the error
+        # message
 
-            s = Series(np.arange(len(i)), index=i)
+        i = index_func(5)
 
-            msg = "Cannot index by location index"
-            with pytest.raises(TypeError, match=msg):
-                s.iloc[3.0]
+        s = Series(np.arange(len(i)), index=i)
 
-            msg = (
-                "cannot do positional indexing on {klass} with these "
-                r"indexers \[3\.0\] of {kind}".format(klass=type(i), kind=str(float))
-            )
-            with pytest.raises(TypeError, match=msg):
-                s.iloc[3.0] = 0
+        msg = "Cannot index by location index"
+        with pytest.raises(TypeError, match=msg):
+            s.iloc[3.0]
+
+        msg = (
+            "cannot do positional indexing on {klass} with these "
+            r"indexers \[3\.0\] of {kind}".format(klass=type(i), kind=str(float))
+        )
+        with pytest.raises(TypeError, match=msg):
+            s.iloc[3.0] = 0
 
     def test_scalar_non_numeric(self):
 

pandas/tests/indexing/test_iloc.py

@@ -15,6 +15,44 @@
 
 
 class TestiLoc(Base):
+    def test_iloc_getitem_int(self):
+        # integer
+        self.check_result(
+            "iloc",
+            2,
+            "iloc",
+            2,
+            typs=["labels", "mixed", "ts", "floats", "empty"],
+            fails=IndexError,
+        )
+
+    def test_iloc_getitem_neg_int(self):
+        # neg integer
+        self.check_result(
+            "iloc",
+            -1,
+            "iloc",
+            -1,
+            typs=["labels", "mixed", "ts", "floats", "empty"],
+            fails=IndexError,
+        )
+
+    def test_iloc_getitem_list_int(self):
+        self.check_result(
+            "iloc",
+            [0, 1, 2],
+            "iloc",
+            [0, 1, 2],
+            typs=["labels", "mixed", "ts", "floats", "empty"],
+            fails=IndexError,
+        )
+
+        # array of ints (GH5006), make sure that a single indexer is returning
+        # the correct type
+
+
+class TestiLoc2:
+    # TODO: better name, just separating out things that dont rely on base class
     def test_iloc_exceeds_bounds(self):
 
         # GH6296
@@ -135,28 +173,6 @@ def test_iloc_non_integer_raises(self, index, columns, index_vals, column_vals):
         with pytest.raises(IndexError, match=msg):
             df.iloc[index_vals, column_vals]
 
-    def test_iloc_getitem_int(self):
-        # integer
-        self.check_result(
-            "iloc",
-            2,
-            "iloc",
-            2,
-            typs=["labels", "mixed", "ts", "floats", "empty"],
-            fails=IndexError,
-        )
-
-    def test_iloc_getitem_neg_int(self):
-        # neg integer
-        self.check_result(
-            "iloc",
-            -1,
-            "iloc",
-            -1,
-            typs=["labels", "mixed", "ts", "floats", "empty"],
-            fails=IndexError,
-        )
-
     @pytest.mark.parametrize("dims", [1, 2])
     def test_iloc_getitem_invalid_scalar(self, dims):
         # GH 21982
@@ -183,19 +199,6 @@ def test_iloc_array_not_mutating_negative_indices(self):
         df.iloc[:, array_with_neg_numbers]
         tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy)
 
-    def test_iloc_getitem_list_int(self):
-        self.check_result(
-            "iloc",
-            [0, 1, 2],
-            "iloc",
-            [0, 1, 2],
-            typs=["labels", "mixed", "ts", "floats", "empty"],
-            fails=IndexError,
-        )
-
-        # array of ints (GH5006), make sure that a single indexer is returning
-        # the correct type
-
     def test_iloc_getitem_neg_int_can_reach_first_index(self):
         # GH10547 and GH10779
         # negative integers should be able to reach index 0
@@ -286,7 +289,9 @@ def test_iloc_getitem_slice_dups(self):
         tm.assert_frame_equal(df.iloc[10:, 2:], df1)
 
     def test_iloc_setitem(self):
-        df = self.frame_ints
+        df = DataFrame(
+            np.random.randn(4, 4), index=np.arange(0, 8, 2), columns=np.arange(0, 12, 3)
+        )
 
         df.iloc[1, 1] = 1
         result = df.iloc[1, 1]

pandas/tests/indexing/test_indexing.py

@@ -17,13 +17,13 @@
 from pandas.core.generic import NDFrame
 from pandas.core.indexers import validate_indices
 from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice
-from pandas.tests.indexing.common import Base, _mklbl
+from pandas.tests.indexing.common import _mklbl
 
 # ------------------------------------------------------------------------
 # Indexing test cases
 
 
-class TestFancy(Base):
+class TestFancy:
     """ pure get/set item & fancy indexing """
 
     def test_setitem_ndarray_1d(self):
@@ -750,7 +750,7 @@ def test_index_type_coercion(self):
                 assert s2.index.is_object()
 
 
-class TestMisc(Base):
+class TestMisc:
     def test_float_index_to_mixed(self):
         df = DataFrame({0.0: np.random.rand(10), 1.0: np.random.rand(10)})
         df["a"] = 10
@@ -875,21 +875,21 @@ def test_indexing_dtypes_on_empty(self):
         assert df2.loc[:, "a"].dtype == np.int64
         tm.assert_series_equal(df2.loc[:, "a"], df2.iloc[:, 0])
 
-    def test_range_in_series_indexing(self):
+    @pytest.mark.parametrize("size", [5, 999999, 1000000])
+    def test_range_in_series_indexing(self, size):
         # range can cause an indexing error
         # GH 11652
-        for x in [5, 999999, 1000000]:
-            s = Series(index=range(x), dtype=np.float64)
-            s.loc[range(1)] = 42
-            tm.assert_series_equal(s.loc[range(1)], Series(42.0, index=[0]))
+        s = Series(index=range(size), dtype=np.float64)
+        s.loc[range(1)] = 42
+        tm.assert_series_equal(s.loc[range(1)], Series(42.0, index=[0]))
 
-            s.loc[range(2)] = 43
-            tm.assert_series_equal(s.loc[range(2)], Series(43.0, index=[0, 1]))
+        s.loc[range(2)] = 43
+        tm.assert_series_equal(s.loc[range(2)], Series(43.0, index=[0, 1]))
 
-    def test_non_reducing_slice(self):
-        df = DataFrame([[0, 1], [2, 3]])
-
-        slices = [
+    @pytest.mark.parametrize(
+        "slc",
+        [
+            # FIXME: dont leave commented-out
             # pd.IndexSlice[:, :],
             pd.IndexSlice[:, 1],
             pd.IndexSlice[1, :],
@@ -902,10 +902,13 @@ def test_non_reducing_slice(self):
             [0, 1],
             np.array([0, 1]),
             Series([0, 1]),
-        ]
-        for slice_ in slices:
-            tslice_ = _non_reducing_slice(slice_)
-            assert isinstance(df.loc[tslice_], DataFrame)
+        ],
+    )
+    def test_non_reducing_slice(self, slc):
+        df = DataFrame([[0, 1], [2, 3]])
+
+        tslice_ = _non_reducing_slice(slc)
+        assert isinstance(df.loc[tslice_], DataFrame)
 
     def test_list_slice(self):
         # like dataframe getitem
@@ -965,37 +968,37 @@ class TestSeriesNoneCoercion:
         (["foo", "bar", "baz"], [None, "bar", "baz"]),
     ]
 
-    def test_coercion_with_setitem(self):
-        for start_data, expected_result in self.EXPECTED_RESULTS:
-            start_series = Series(start_data)
-            start_series[0] = None
+    @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
+    def test_coercion_with_setitem(self, start_data, expected_result):
+        start_series = Series(start_data)
+        start_series[0] = None
 
-            expected_series = Series(expected_result)
-            tm.assert_series_equal(start_series, expected_series)
+        expected_series = Series(expected_result)
+        tm.assert_series_equal(start_series, expected_series)
 
-    def test_coercion_with_loc_setitem(self):
-        for start_data, expected_result in self.EXPECTED_RESULTS:
-            start_series = Series(start_data)
-            start_series.loc[0] = None
+    @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
+    def test_coercion_with_loc_setitem(self, start_data, expected_result):
+        start_series = Series(start_data)
+        start_series.loc[0] = None
 
-            expected_series = Series(expected_result)
-            tm.assert_series_equal(start_series, expected_series)
+        expected_series = Series(expected_result)
+        tm.assert_series_equal(start_series, expected_series)
 
-    def test_coercion_with_setitem_and_series(self):
-        for start_data, expected_result in self.EXPECTED_RESULTS:
-            start_series = Series(start_data)
-            start_series[start_series == start_series[0]] = None
+    @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
+    def test_coercion_with_setitem_and_series(self, start_data, expected_result):
+        start_series = Series(start_data)
+        start_series[start_series == start_series[0]] = None
 
-            expected_series = Series(expected_result)
-            tm.assert_series_equal(start_series, expected_series)
+        expected_series = Series(expected_result)
+        tm.assert_series_equal(start_series, expected_series)
 
-    def test_coercion_with_loc_and_series(self):
-        for start_data, expected_result in self.EXPECTED_RESULTS:
-            start_series = Series(start_data)
-            start_series.loc[start_series == start_series[0]] = None
+    @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
+    def test_coercion_with_loc_and_series(self, start_data, expected_result):
+        start_series = Series(start_data)
+        start_series.loc[start_series == start_series[0]] = None
 
-            expected_series = Series(expected_result)
-            tm.assert_series_equal(start_series, expected_series)
+        expected_series = Series(expected_result)
+        tm.assert_series_equal(start_series, expected_series)
 
 
 class TestDataframeNoneCoercion:
@@ -1012,31 +1015,35 @@ class TestDataframeNoneCoercion:
         (["foo", "bar", "baz"], [None, "bar", "baz"]),
     ]
 
-    def test_coercion_with_loc(self):
-        for start_data, expected_result in self.EXPECTED_SINGLE_ROW_RESULTS:
-            start_dataframe = DataFrame({"foo": start_data})
-            start_dataframe.loc[0, ["foo"]] = None
+    @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS)
+    def test_coercion_with_loc(self, expected):
+        start_data, expected_result = expected
+
+        start_dataframe = DataFrame({"foo": start_data})
+        start_dataframe.loc[0, ["foo"]] = None
+
+        expected_dataframe = DataFrame({"foo": expected_result})
+        tm.assert_frame_equal(start_dataframe, expected_dataframe)
+
+    @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS)
+    def test_coercion_with_setitem_and_dataframe(self, expected):
+        start_data, expected_result = expected
 
-            expected_dataframe = DataFrame({"foo": expected_result})
-            tm.assert_frame_equal(start_dataframe, expected_dataframe)
+        start_dataframe = DataFrame({"foo": start_data})
+        start_dataframe[start_dataframe["foo"] == start_dataframe["foo"][0]] = None
 
-    def test_coercion_with_setitem_and_dataframe(self):
-        for start_data, expected_result in self.EXPECTED_SINGLE_ROW_RESULTS:
-            start_dataframe = DataFrame({"foo": start_data})
-            start_dataframe[start_dataframe["foo"] == start_dataframe["foo"][0]] = None
+        expected_dataframe = DataFrame({"foo": expected_result})
+        tm.assert_frame_equal(start_dataframe, expected_dataframe)
 
-            expected_dataframe = DataFrame({"foo": expected_result})
-            tm.assert_frame_equal(start_dataframe, expected_dataframe)
+    @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS)
+    def test_none_coercion_loc_and_dataframe(self, expected):
+        start_data, expected_result = expected
 
-    def test_none_coercion_loc_and_dataframe(self):
-        for start_data, expected_result in self.EXPECTED_SINGLE_ROW_RESULTS:
-            start_dataframe = DataFrame({"foo": start_data})
-            start_dataframe.loc[
-                start_dataframe["foo"] == start_dataframe["foo"][0]
-            ] = None
+        start_dataframe = DataFrame({"foo": start_data})
+        start_dataframe.loc[start_dataframe["foo"] == start_dataframe["foo"][0]] = None
 
-            expected_dataframe = DataFrame({"foo": expected_result})
-            tm.assert_frame_equal(start_dataframe, expected_dataframe)
+        expected_dataframe = DataFrame({"foo": expected_result})
+        tm.assert_frame_equal(start_dataframe, expected_dataframe)
 
     def test_none_coercion_mixed_dtypes(self):
         start_dataframe = DataFrame(