import numpy as np
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import pytest
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from pandas.core.dtypes.cast import construct_1d_arraylike_from_scalar
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from pandas.core.dtypes.dtypes import CategoricalDtype
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from pandas import (
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Categorical,
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Timedelta,
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)
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import pandas._testing as tm
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def test_cast_1d_array_like_from_scalar_categorical():
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# see gh-19565
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#
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# Categorical result from scalar did not maintain
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# categories and ordering of the passed dtype.
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cats = ["a", "b", "c"]
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cat_type = CategoricalDtype(categories=cats, ordered=False)
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expected = Categorical(["a", "a"], categories=cats)
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result = construct_1d_arraylike_from_scalar("a", len(expected), cat_type)
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tm.assert_categorical_equal(result, expected)
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def test_cast_1d_array_like_from_timestamp(fixed_now_ts):
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# check we dont lose nanoseconds
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ts = fixed_now_ts + Timedelta(1)
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res = construct_1d_arraylike_from_scalar(ts, 2, np.dtype("M8[ns]"))
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assert res[0] == ts
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def test_cast_1d_array_like_from_timedelta():
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# check we dont lose nanoseconds
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td = Timedelta(1)
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res = construct_1d_arraylike_from_scalar(td, 2, np.dtype("m8[ns]"))
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assert res[0] == td
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def test_cast_1d_array_like_mismatched_datetimelike():
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td = np.timedelta64("NaT", "ns")
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dt = np.datetime64("NaT", "ns")
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with pytest.raises(TypeError, match="Cannot cast"):
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construct_1d_arraylike_from_scalar(td, 2, dt.dtype)
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with pytest.raises(TypeError, match="Cannot cast"):
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construct_1d_arraylike_from_scalar(np.timedelta64(4, "ns"), 2, dt.dtype)
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with pytest.raises(TypeError, match="Cannot cast"):
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construct_1d_arraylike_from_scalar(dt, 2, td.dtype)
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with pytest.raises(TypeError, match="Cannot cast"):
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construct_1d_arraylike_from_scalar(np.datetime64(4, "ns"), 2, td.dtype)
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