TST: collect Interval tests

pandas/tests/arrays/interval/test_interval.py

@@ -229,178 +229,3 @@ def test_min_max(self, left_right_dtypes, index_or_series_or_array):
         res = arr_na.max(skipna=True)
         assert res == MAX
         assert type(res) == type(MAX)
-
-
-# ----------------------------------------------------------------------------
-# Arrow interaction
-
-
-def test_arrow_extension_type():
-    pa = pytest.importorskip("pyarrow")
-
-    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
-
-    p1 = ArrowIntervalType(pa.int64(), "left")
-    p2 = ArrowIntervalType(pa.int64(), "left")
-    p3 = ArrowIntervalType(pa.int64(), "right")
-
-    assert p1.closed == "left"
-    assert p1 == p2
-    assert p1 != p3
-    assert hash(p1) == hash(p2)
-    assert hash(p1) != hash(p3)
-
-
-def test_arrow_array():
-    pa = pytest.importorskip("pyarrow")
-
-    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
-
-    intervals = pd.interval_range(1, 5, freq=1).array
-
-    result = pa.array(intervals)
-    assert isinstance(result.type, ArrowIntervalType)
-    assert result.type.closed == intervals.closed
-    assert result.type.subtype == pa.int64()
-    assert result.storage.field("left").equals(pa.array([1, 2, 3, 4], type="int64"))
-    assert result.storage.field("right").equals(pa.array([2, 3, 4, 5], type="int64"))
-
-    expected = pa.array([{"left": i, "right": i + 1} for i in range(1, 5)])
-    assert result.storage.equals(expected)
-
-    # convert to its storage type
-    result = pa.array(intervals, type=expected.type)
-    assert result.equals(expected)
-
-    # unsupported conversions
-    with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
-        pa.array(intervals, type="float64")
-
-    with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
-        pa.array(intervals, type=ArrowIntervalType(pa.float64(), "left"))
-
-
-def test_arrow_array_missing():
-    pa = pytest.importorskip("pyarrow")
-
-    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
-
-    arr = IntervalArray.from_breaks([0.0, 1.0, 2.0, 3.0])
-    arr[1] = None
-
-    result = pa.array(arr)
-    assert isinstance(result.type, ArrowIntervalType)
-    assert result.type.closed == arr.closed
-    assert result.type.subtype == pa.float64()
-
-    # fields have missing values (not NaN)
-    left = pa.array([0.0, None, 2.0], type="float64")
-    right = pa.array([1.0, None, 3.0], type="float64")
-    assert result.storage.field("left").equals(left)
-    assert result.storage.field("right").equals(right)
-
-    # structarray itself also has missing values on the array level
-    vals = [
-        {"left": 0.0, "right": 1.0},
-        {"left": None, "right": None},
-        {"left": 2.0, "right": 3.0},
-    ]
-    expected = pa.StructArray.from_pandas(vals, mask=np.array([False, True, False]))
-    assert result.storage.equals(expected)
-
-
-@pytest.mark.filterwarnings(
-    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
-)
-@pytest.mark.parametrize(
-    "breaks",
-    [[0.0, 1.0, 2.0, 3.0], date_range("2017", periods=4, freq="D")],
-    ids=["float", "datetime64[ns]"],
-)
-def test_arrow_table_roundtrip(breaks):
-    pa = pytest.importorskip("pyarrow")
-
-    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
-
-    arr = IntervalArray.from_breaks(breaks)
-    arr[1] = None
-    df = pd.DataFrame({"a": arr})
-
-    table = pa.table(df)
-    assert isinstance(table.field("a").type, ArrowIntervalType)
-    result = table.to_pandas()
-    assert isinstance(result["a"].dtype, pd.IntervalDtype)
-    tm.assert_frame_equal(result, df)
-
-    table2 = pa.concat_tables([table, table])
-    result = table2.to_pandas()
-    expected = pd.concat([df, df], ignore_index=True)
-    tm.assert_frame_equal(result, expected)
-
-    # GH-41040
-    table = pa.table(
-        [pa.chunked_array([], type=table.column(0).type)], schema=table.schema
-    )
-    result = table.to_pandas()
-    tm.assert_frame_equal(result, expected[0:0])
-
-
-@pytest.mark.filterwarnings(
-    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
-)
-@pytest.mark.parametrize(
-    "breaks",
-    [[0.0, 1.0, 2.0, 3.0], date_range("2017", periods=4, freq="D")],
-    ids=["float", "datetime64[ns]"],
-)
-def test_arrow_table_roundtrip_without_metadata(breaks):
-    pa = pytest.importorskip("pyarrow")
-
-    arr = IntervalArray.from_breaks(breaks)
-    arr[1] = None
-    df = pd.DataFrame({"a": arr})
-
-    table = pa.table(df)
-    # remove the metadata
-    table = table.replace_schema_metadata()
-    assert table.schema.metadata is None
-
-    result = table.to_pandas()
-    assert isinstance(result["a"].dtype, pd.IntervalDtype)
-    tm.assert_frame_equal(result, df)
-
-
-def test_from_arrow_from_raw_struct_array():
-    # in case pyarrow lost the Interval extension type (eg on parquet roundtrip
-    # with datetime64[ns] subtype, see GH-45881), still allow conversion
-    # from arrow to IntervalArray
-    pa = pytest.importorskip("pyarrow")
-
-    arr = pa.array([{"left": 0, "right": 1}, {"left": 1, "right": 2}])
-    dtype = pd.IntervalDtype(np.dtype("int64"), closed="neither")
-
-    result = dtype.__from_arrow__(arr)
-    expected = IntervalArray.from_breaks(
-        np.array([0, 1, 2], dtype="int64"), closed="neither"
-    )
-    tm.assert_extension_array_equal(result, expected)
-
-    result = dtype.__from_arrow__(pa.chunked_array([arr]))
-    tm.assert_extension_array_equal(result, expected)
-
-
-@pytest.mark.parametrize("timezone", ["UTC", "US/Pacific", "GMT"])
-def test_interval_index_subtype(timezone, inclusive_endpoints_fixture):
-    # GH 46999
-    dates = date_range("2022", periods=3, tz=timezone)
-    dtype = f"interval[datetime64[ns, {timezone}], {inclusive_endpoints_fixture}]"
-    result = IntervalIndex.from_arrays(
-        ["2022-01-01", "2022-01-02"],
-        ["2022-01-02", "2022-01-03"],
-        closed=inclusive_endpoints_fixture,
-        dtype=dtype,
-    )
-    expected = IntervalIndex.from_arrays(
-        dates[:-1], dates[1:], closed=inclusive_endpoints_fixture
-    )
-    tm.assert_index_equal(result, expected)

pandas/tests/arrays/interval/test_interval_pyarrow.py

@@ -0,0 +1,160 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
+
+
+def test_arrow_extension_type():
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    p1 = ArrowIntervalType(pa.int64(), "left")
+    p2 = ArrowIntervalType(pa.int64(), "left")
+    p3 = ArrowIntervalType(pa.int64(), "right")
+
+    assert p1.closed == "left"
+    assert p1 == p2
+    assert p1 != p3
+    assert hash(p1) == hash(p2)
+    assert hash(p1) != hash(p3)
+
+
+def test_arrow_array():
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    intervals = pd.interval_range(1, 5, freq=1).array
+
+    result = pa.array(intervals)
+    assert isinstance(result.type, ArrowIntervalType)
+    assert result.type.closed == intervals.closed
+    assert result.type.subtype == pa.int64()
+    assert result.storage.field("left").equals(pa.array([1, 2, 3, 4], type="int64"))
+    assert result.storage.field("right").equals(pa.array([2, 3, 4, 5], type="int64"))
+
+    expected = pa.array([{"left": i, "right": i + 1} for i in range(1, 5)])
+    assert result.storage.equals(expected)
+
+    # convert to its storage type
+    result = pa.array(intervals, type=expected.type)
+    assert result.equals(expected)
+
+    # unsupported conversions
+    with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
+        pa.array(intervals, type="float64")
+
+    with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
+        pa.array(intervals, type=ArrowIntervalType(pa.float64(), "left"))
+
+
+def test_arrow_array_missing():
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    arr = IntervalArray.from_breaks([0.0, 1.0, 2.0, 3.0])
+    arr[1] = None
+
+    result = pa.array(arr)
+    assert isinstance(result.type, ArrowIntervalType)
+    assert result.type.closed == arr.closed
+    assert result.type.subtype == pa.float64()
+
+    # fields have missing values (not NaN)
+    left = pa.array([0.0, None, 2.0], type="float64")
+    right = pa.array([1.0, None, 3.0], type="float64")
+    assert result.storage.field("left").equals(left)
+    assert result.storage.field("right").equals(right)
+
+    # structarray itself also has missing values on the array level
+    vals = [
+        {"left": 0.0, "right": 1.0},
+        {"left": None, "right": None},
+        {"left": 2.0, "right": 3.0},
+    ]
+    expected = pa.StructArray.from_pandas(vals, mask=np.array([False, True, False]))
+    assert result.storage.equals(expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+@pytest.mark.parametrize(
+    "breaks",
+    [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
+    ids=["float", "datetime64[ns]"],
+)
+def test_arrow_table_roundtrip(breaks):
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    arr = IntervalArray.from_breaks(breaks)
+    arr[1] = None
+    df = pd.DataFrame({"a": arr})
+
+    table = pa.table(df)
+    assert isinstance(table.field("a").type, ArrowIntervalType)
+    result = table.to_pandas()
+    assert isinstance(result["a"].dtype, pd.IntervalDtype)
+    tm.assert_frame_equal(result, df)
+
+    table2 = pa.concat_tables([table, table])
+    result = table2.to_pandas()
+    expected = pd.concat([df, df], ignore_index=True)
+    tm.assert_frame_equal(result, expected)
+
+    # GH#41040
+    table = pa.table(
+        [pa.chunked_array([], type=table.column(0).type)], schema=table.schema
+    )
+    result = table.to_pandas()
+    tm.assert_frame_equal(result, expected[0:0])
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+@pytest.mark.parametrize(
+    "breaks",
+    [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
+    ids=["float", "datetime64[ns]"],
+)
+def test_arrow_table_roundtrip_without_metadata(breaks):
+    pa = pytest.importorskip("pyarrow")
+
+    arr = IntervalArray.from_breaks(breaks)
+    arr[1] = None
+    df = pd.DataFrame({"a": arr})
+
+    table = pa.table(df)
+    # remove the metadata
+    table = table.replace_schema_metadata()
+    assert table.schema.metadata is None
+
+    result = table.to_pandas()
+    assert isinstance(result["a"].dtype, pd.IntervalDtype)
+    tm.assert_frame_equal(result, df)
+
+
+def test_from_arrow_from_raw_struct_array():
+    # in case pyarrow lost the Interval extension type (eg on parquet roundtrip
+    # with datetime64[ns] subtype, see GH-45881), still allow conversion
+    # from arrow to IntervalArray
+    pa = pytest.importorskip("pyarrow")
+
+    arr = pa.array([{"left": 0, "right": 1}, {"left": 1, "right": 2}])
+    dtype = pd.IntervalDtype(np.dtype("int64"), closed="neither")
+
+    result = dtype.__from_arrow__(arr)
+    expected = IntervalArray.from_breaks(
+        np.array([0, 1, 2], dtype="int64"), closed="neither"
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)