REF: move misplaced pd.concat tests (#37232)

Author: jbrockmendel

pandas/tests/frame/test_combine_concat.py

@@ -2925,3 +2925,349 @@ def test_concat_preserves_extension_int64_dtype():
     result = pd.concat([df_a, df_b], ignore_index=True)
     expected = pd.DataFrame({"a": [-1, None], "b": [None, 1]}, dtype="Int64")
     tm.assert_frame_equal(result, expected)
+
+
+class TestSeriesConcat:
+    @pytest.mark.parametrize(
+        "dtype", ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"]
+    )
+    def test_concat_empty_series_dtypes_match_roundtrips(self, dtype):
+        dtype = np.dtype(dtype)
+
+        result = pd.concat([Series(dtype=dtype)])
+        assert result.dtype == dtype
+
+        result = pd.concat([Series(dtype=dtype), Series(dtype=dtype)])
+        assert result.dtype == dtype
+
+    def test_concat_empty_series_dtypes_roundtrips(self):
+
+        # round-tripping with self & like self
+        dtypes = map(np.dtype, ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"])
+
+        def int_result_type(dtype, dtype2):
+            typs = {dtype.kind, dtype2.kind}
+            if not len(typs - {"i", "u", "b"}) and (
+                dtype.kind == "i" or dtype2.kind == "i"
+            ):
+                return "i"
+            elif not len(typs - {"u", "b"}) and (
+                dtype.kind == "u" or dtype2.kind == "u"
+            ):
+                return "u"
+            return None
+
+        def float_result_type(dtype, dtype2):
+            typs = {dtype.kind, dtype2.kind}
+            if not len(typs - {"f", "i", "u"}) and (
+                dtype.kind == "f" or dtype2.kind == "f"
+            ):
+                return "f"
+            return None
+
+        def get_result_type(dtype, dtype2):
+            result = float_result_type(dtype, dtype2)
+            if result is not None:
+                return result
+            result = int_result_type(dtype, dtype2)
+            if result is not None:
+                return result
+            return "O"
+
+        for dtype in dtypes:
+            for dtype2 in dtypes:
+                if dtype == dtype2:
+                    continue
+
+                expected = get_result_type(dtype, dtype2)
+                result = pd.concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype
+                assert result.kind == expected
+
+    @pytest.mark.parametrize(
+        "left,right,expected",
+        [
+            # booleans
+            (np.bool_, np.int32, np.int32),
+            (np.bool_, np.float32, np.object_),
+            # datetime-like
+            ("m8[ns]", np.bool_, np.object_),
+            ("m8[ns]", np.int64, np.object_),
+            ("M8[ns]", np.bool_, np.object_),
+            ("M8[ns]", np.int64, np.object_),
+            # categorical
+            ("category", "category", "category"),
+            ("category", "object", "object"),
+        ],
+    )
+    def test_concat_empty_series_dtypes(self, left, right, expected):
+        result = pd.concat([Series(dtype=left), Series(dtype=right)])
+        assert result.dtype == expected
+
+    def test_concat_empty_series_dtypes_triple(self):
+
+        assert (
+            pd.concat(
+                [Series(dtype="M8[ns]"), Series(dtype=np.bool_), Series(dtype=np.int64)]
+            ).dtype
+            == np.object_
+        )
+
+    def test_concat_empty_series_dtype_category_with_array(self):
+        # GH#18515
+        assert (
+            pd.concat(
+                [Series(np.array([]), dtype="category"), Series(dtype="float64")]
+            ).dtype
+            == "float64"
+        )
+
+    def test_concat_empty_series_dtypes_sparse(self):
+        result = pd.concat(
+            [
+                Series(dtype="float64").astype("Sparse"),
+                Series(dtype="float64").astype("Sparse"),
+            ]
+        )
+        assert result.dtype == "Sparse[float64]"
+
+        result = pd.concat(
+            [Series(dtype="float64").astype("Sparse"), Series(dtype="float64")]
+        )
+        # TODO: release-note: concat sparse dtype
+        expected = pd.SparseDtype(np.float64)
+        assert result.dtype == expected
+
+        result = pd.concat(
+            [Series(dtype="float64").astype("Sparse"), Series(dtype="object")]
+        )
+        # TODO: release-note: concat sparse dtype
+        expected = pd.SparseDtype("object")
+        assert result.dtype == expected
+
+
+class TestDataFrameConcat:
+    def test_concat_multiple_frames_dtypes(self):
+
+        # GH#2759
+        A = DataFrame(data=np.ones((10, 2)), columns=["foo", "bar"], dtype=np.float64)
+        B = DataFrame(data=np.ones((10, 2)), dtype=np.float32)
+        results = pd.concat((A, B), axis=1).dtypes
+        expected = Series(
+            [np.dtype("float64")] * 2 + [np.dtype("float32")] * 2,
+            index=["foo", "bar", 0, 1],
+        )
+        tm.assert_series_equal(results, expected)
+
+    def test_concat_multiple_tzs(self):
+        # GH#12467
+        # combining datetime tz-aware and naive DataFrames
+        ts1 = Timestamp("2015-01-01", tz=None)
+        ts2 = Timestamp("2015-01-01", tz="UTC")
+        ts3 = Timestamp("2015-01-01", tz="EST")
+
+        df1 = DataFrame(dict(time=[ts1]))
+        df2 = DataFrame(dict(time=[ts2]))
+        df3 = DataFrame(dict(time=[ts3]))
+
+        results = pd.concat([df1, df2]).reset_index(drop=True)
+        expected = DataFrame(dict(time=[ts1, ts2]), dtype=object)
+        tm.assert_frame_equal(results, expected)
+
+        results = pd.concat([df1, df3]).reset_index(drop=True)
+        expected = DataFrame(dict(time=[ts1, ts3]), dtype=object)
+        tm.assert_frame_equal(results, expected)
+
+        results = pd.concat([df2, df3]).reset_index(drop=True)
+        expected = DataFrame(dict(time=[ts2, ts3]))
+        tm.assert_frame_equal(results, expected)
+
+    @pytest.mark.parametrize(
+        "t1",
+        [
+            "2015-01-01",
+            pytest.param(
+                pd.NaT,
+                marks=pytest.mark.xfail(
+                    reason="GH23037 incorrect dtype when concatenating"
+                ),
+            ),
+        ],
+    )
+    def test_concat_tz_NaT(self, t1):
+        # GH#22796
+        # Concating tz-aware multicolumn DataFrames
+        ts1 = Timestamp(t1, tz="UTC")
+        ts2 = Timestamp("2015-01-01", tz="UTC")
+        ts3 = Timestamp("2015-01-01", tz="UTC")
+
+        df1 = DataFrame([[ts1, ts2]])
+        df2 = DataFrame([[ts3]])
+
+        result = pd.concat([df1, df2])
+        expected = DataFrame([[ts1, ts2], [ts3, pd.NaT]], index=[0, 0])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_tz_not_aligned(self):
+        # GH#22796
+        ts = pd.to_datetime([1, 2]).tz_localize("UTC")
+        a = pd.DataFrame({"A": ts})
+        b = pd.DataFrame({"A": ts, "B": ts})
+        result = pd.concat([a, b], sort=True, ignore_index=True)
+        expected = pd.DataFrame(
+            {"A": list(ts) + list(ts), "B": [pd.NaT, pd.NaT] + list(ts)}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_tuple_keys(self):
+        # GH#14438
+        df1 = pd.DataFrame(np.ones((2, 2)), columns=list("AB"))
+        df2 = pd.DataFrame(np.ones((3, 2)) * 2, columns=list("AB"))
+        results = pd.concat((df1, df2), keys=[("bee", "bah"), ("bee", "boo")])
+        expected = pd.DataFrame(
+            {
+                "A": {
+                    ("bee", "bah", 0): 1.0,
+                    ("bee", "bah", 1): 1.0,
+                    ("bee", "boo", 0): 2.0,
+                    ("bee", "boo", 1): 2.0,
+                    ("bee", "boo", 2): 2.0,
+                },
+                "B": {
+                    ("bee", "bah", 0): 1.0,
+                    ("bee", "bah", 1): 1.0,
+                    ("bee", "boo", 0): 2.0,
+                    ("bee", "boo", 1): 2.0,
+                    ("bee", "boo", 2): 2.0,
+                },
+            }
+        )
+        tm.assert_frame_equal(results, expected)
+
+    def test_concat_named_keys(self):
+        # GH#14252
+        df = pd.DataFrame({"foo": [1, 2], "bar": [0.1, 0.2]})
+        index = Index(["a", "b"], name="baz")
+        concatted_named_from_keys = pd.concat([df, df], keys=index)
+        expected_named = pd.DataFrame(
+            {"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
+            index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=["baz", None]),
+        )
+        tm.assert_frame_equal(concatted_named_from_keys, expected_named)
+
+        index_no_name = Index(["a", "b"], name=None)
+        concatted_named_from_names = pd.concat(
+            [df, df], keys=index_no_name, names=["baz"]
+        )
+        tm.assert_frame_equal(concatted_named_from_names, expected_named)
+
+        concatted_unnamed = pd.concat([df, df], keys=index_no_name)
+        expected_unnamed = pd.DataFrame(
+            {"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
+            index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=[None, None]),
+        )
+        tm.assert_frame_equal(concatted_unnamed, expected_unnamed)
+
+    def test_concat_axis_parameter(self):
+        # GH#14369
+        df1 = pd.DataFrame({"A": [0.1, 0.2]}, index=range(2))
+        df2 = pd.DataFrame({"A": [0.3, 0.4]}, index=range(2))
+
+        # Index/row/0 DataFrame
+        expected_index = pd.DataFrame({"A": [0.1, 0.2, 0.3, 0.4]}, index=[0, 1, 0, 1])
+
+        concatted_index = pd.concat([df1, df2], axis="index")
+        tm.assert_frame_equal(concatted_index, expected_index)
+
+        concatted_row = pd.concat([df1, df2], axis="rows")
+        tm.assert_frame_equal(concatted_row, expected_index)
+
+        concatted_0 = pd.concat([df1, df2], axis=0)
+        tm.assert_frame_equal(concatted_0, expected_index)
+
+        # Columns/1 DataFrame
+        expected_columns = pd.DataFrame(
+            [[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=["A", "A"]
+        )
+
+        concatted_columns = pd.concat([df1, df2], axis="columns")
+        tm.assert_frame_equal(concatted_columns, expected_columns)
+
+        concatted_1 = pd.concat([df1, df2], axis=1)
+        tm.assert_frame_equal(concatted_1, expected_columns)
+
+        series1 = pd.Series([0.1, 0.2])
+        series2 = pd.Series([0.3, 0.4])
+
+        # Index/row/0 Series
+        expected_index_series = pd.Series([0.1, 0.2, 0.3, 0.4], index=[0, 1, 0, 1])
+
+        concatted_index_series = pd.concat([series1, series2], axis="index")
+        tm.assert_series_equal(concatted_index_series, expected_index_series)
+
+        concatted_row_series = pd.concat([series1, series2], axis="rows")
+        tm.assert_series_equal(concatted_row_series, expected_index_series)
+
+        concatted_0_series = pd.concat([series1, series2], axis=0)
+        tm.assert_series_equal(concatted_0_series, expected_index_series)
+
+        # Columns/1 Series
+        expected_columns_series = pd.DataFrame(
+            [[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=[0, 1]
+        )
+
+        concatted_columns_series = pd.concat([series1, series2], axis="columns")
+        tm.assert_frame_equal(concatted_columns_series, expected_columns_series)
+
+        concatted_1_series = pd.concat([series1, series2], axis=1)
+        tm.assert_frame_equal(concatted_1_series, expected_columns_series)
+
+        # Testing ValueError
+        with pytest.raises(ValueError, match="No axis named"):
+            pd.concat([series1, series2], axis="something")
+
+    def test_concat_numerical_names(self):
+        # GH#15262, GH#12223
+        df = pd.DataFrame(
+            {"col": range(9)},
+            dtype="int32",
+            index=(
+                pd.MultiIndex.from_product(
+                    [["A0", "A1", "A2"], ["B0", "B1", "B2"]], names=[1, 2]
+                )
+            ),
+        )
+        result = pd.concat((df.iloc[:2, :], df.iloc[-2:, :]))
+        expected = pd.DataFrame(
+            {"col": [0, 1, 7, 8]},
+            dtype="int32",
+            index=pd.MultiIndex.from_tuples(
+                [("A0", "B0"), ("A0", "B1"), ("A2", "B1"), ("A2", "B2")], names=[1, 2]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_astype_dup_col(self):
+        # GH#23049
+        df = pd.DataFrame([{"a": "b"}])
+        df = pd.concat([df, df], axis=1)
+
+        result = df.astype("category")
+        expected = pd.DataFrame(
+            np.array(["b", "b"]).reshape(1, 2), columns=["a", "a"]
+        ).astype("category")
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_datetime_datetime64_frame(self):
+        # GH#2624
+        rows = []
+        rows.append([datetime(2010, 1, 1), 1])
+        rows.append([datetime(2010, 1, 2), "hi"])
+
+        df2_obj = DataFrame.from_records(rows, columns=["date", "test"])
+
+        ind = date_range(start="2000/1/1", freq="D", periods=10)
+        df1 = DataFrame({"date": ind, "test": range(10)})
+
+        # it works!
+        pd.concat([df1, df2_obj])