import re
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import numpy as np
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import pytest
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import pandas as pd
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class TestSetitemValidation:
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def _check_setitem_invalid(self, arr, invalid):
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msg = f"Invalid value '{str(invalid)}' for dtype {arr.dtype}"
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msg = re.escape(msg)
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with pytest.raises(TypeError, match=msg):
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arr[0] = invalid
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with pytest.raises(TypeError, match=msg):
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arr[:] = invalid
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with pytest.raises(TypeError, match=msg):
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arr[[0]] = invalid
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# FIXME: don't leave commented-out
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# with pytest.raises(TypeError):
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# arr[[0]] = [invalid]
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# with pytest.raises(TypeError):
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# arr[[0]] = np.array([invalid], dtype=object)
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# Series non-coercion, behavior subject to change
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ser = pd.Series(arr)
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with pytest.raises(TypeError, match=msg):
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ser[0] = invalid
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# TODO: so, so many other variants of this...
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_invalid_scalars = [
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1 + 2j,
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"True",
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"1",
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"1.0",
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pd.NaT,
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np.datetime64("NaT"),
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np.timedelta64("NaT"),
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]
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@pytest.mark.parametrize(
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"invalid", _invalid_scalars + [1, 1.0, np.int64(1), np.float64(1)]
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)
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def test_setitem_validation_scalar_bool(self, invalid):
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arr = pd.array([True, False, None], dtype="boolean")
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self._check_setitem_invalid(arr, invalid)
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@pytest.mark.parametrize("invalid", _invalid_scalars + [True, 1.5, np.float64(1.5)])
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def test_setitem_validation_scalar_int(self, invalid, any_int_ea_dtype):
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arr = pd.array([1, 2, None], dtype=any_int_ea_dtype)
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self._check_setitem_invalid(arr, invalid)
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@pytest.mark.parametrize("invalid", _invalid_scalars + [True])
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def test_setitem_validation_scalar_float(self, invalid, float_ea_dtype):
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arr = pd.array([1, 2, None], dtype=float_ea_dtype)
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self._check_setitem_invalid(arr, invalid)
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