122 lines
3.3 KiB
Python
122 lines
3.3 KiB
Python
import pytest
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from pandas.core.dtypes.dtypes import PeriodDtype
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays import (
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PeriodArray,
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period_array,
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)
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pa = pytest.importorskip("pyarrow", minversion="1.0.1")
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def test_arrow_extension_type():
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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p1 = ArrowPeriodType("D")
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p2 = ArrowPeriodType("D")
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p3 = ArrowPeriodType("M")
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assert p1.freq == "D"
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assert p1 == p2
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assert not p1 == p3
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assert hash(p1) == hash(p2)
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assert not hash(p1) == hash(p3)
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@pytest.mark.parametrize(
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"data, freq",
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[
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(pd.date_range("2017", periods=3), "D"),
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(pd.date_range("2017", periods=3, freq="A"), "A-DEC"),
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],
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)
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def test_arrow_array(data, freq):
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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periods = period_array(data, freq=freq)
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result = pa.array(periods)
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assert isinstance(result.type, ArrowPeriodType)
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assert result.type.freq == freq
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expected = pa.array(periods.asi8, type="int64")
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assert result.storage.equals(expected)
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# convert to its storage type
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result = pa.array(periods, type=pa.int64())
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assert result.equals(expected)
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# unsupported conversions
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msg = "Not supported to convert PeriodArray to 'double' type"
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with pytest.raises(TypeError, match=msg):
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pa.array(periods, type="float64")
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with pytest.raises(TypeError, match="different 'freq'"):
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pa.array(periods, type=ArrowPeriodType("T"))
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def test_arrow_array_missing():
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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arr = PeriodArray([1, 2, 3], freq="D")
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arr[1] = pd.NaT
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result = pa.array(arr)
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assert isinstance(result.type, ArrowPeriodType)
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assert result.type.freq == "D"
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expected = pa.array([1, None, 3], type="int64")
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assert result.storage.equals(expected)
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def test_arrow_table_roundtrip():
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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arr = PeriodArray([1, 2, 3], freq="D")
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arr[1] = pd.NaT
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df = pd.DataFrame({"a": arr})
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table = pa.table(df)
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assert isinstance(table.field("a").type, ArrowPeriodType)
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, PeriodDtype)
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tm.assert_frame_equal(result, df)
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table2 = pa.concat_tables([table, table])
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result = table2.to_pandas()
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expected = pd.concat([df, df], ignore_index=True)
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tm.assert_frame_equal(result, expected)
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def test_arrow_load_from_zero_chunks():
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# GH-41040
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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arr = PeriodArray([], freq="D")
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df = pd.DataFrame({"a": arr})
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table = pa.table(df)
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assert isinstance(table.field("a").type, ArrowPeriodType)
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table = pa.table(
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[pa.chunked_array([], type=table.column(0).type)], schema=table.schema
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)
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, PeriodDtype)
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tm.assert_frame_equal(result, df)
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def test_arrow_table_roundtrip_without_metadata():
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arr = PeriodArray([1, 2, 3], freq="H")
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arr[1] = pd.NaT
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df = pd.DataFrame({"a": arr})
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table = pa.table(df)
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# remove the metadata
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table = table.replace_schema_metadata()
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assert table.schema.metadata is None
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, PeriodDtype)
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tm.assert_frame_equal(result, df)
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