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BUG: using dtype='int64' argument of Series causes ValueError: values cannot be losslessly cast to int64 for integer strings #45017

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BUG: using dtype='int64' argument of Series causes ValueError: values cannot be losslessly cast to int64 for integer strings #45017

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Valueerror
shubham11941140 Dec 22, 2021
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precommit
shubham11941140 Dec 22, 2021
01af816
Merge branch 'master' of https://github.com/pandas-dev/pandas into b7
shubham11941140 Dec 23, 2021
730d5a9
Optimization
shubham11941140 Dec 23, 2021
204c3c9
infer_dtype added
shubham11941140 Dec 23, 2021
814e5ff
precommit
shubham11941140 Dec 23, 2021
a76a34a
Parametrised
shubham11941140 Dec 27, 2021
8603236
changed to any_dtype
shubham11941140 Dec 31, 2021
40b3872
Added xfail
shubham11941140 Jan 1, 2022
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precommit
shubham11941140 Jan 1, 2022
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Removed parametrise
shubham11941140 Jan 4, 2022
55410de
precommit fix
shubham11941140 Jan 4, 2022
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removed obvious line
shubham11941140 Jan 17, 2022
ae6ea47
Checking string dtype directly
shubham11941140 Feb 11, 2022
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Merge branch 'main' into b7
shubham11941140 Mar 7, 2022
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3 changes: 3 additions & 0 deletions pandas/core/dtypes/cast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2096,6 +2096,9 @@ def maybe_cast_to_integer_array(
)
return casted

if lib.infer_dtype(casted) == "integer":
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i think this always holds. The condition we're interested in is whether the casting was lossy.

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Agreed, but this test currently passes the extra test added.

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do the added tests pass w/o this change?

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Yes they do.

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Right, because this condition always holds.

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But before adding this check the testcase in the issue was not passing.

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But before adding this check the testcase in the issue was not passing.

That's a reason to add some check for this, but this particular check is not the right check. ATM this is equivalent to (but slower than) if True:

return casted

# No known cases that get here, but raising explicitly to cover our bases.
raise ValueError(f"values cannot be losslessly cast to {dtype}")

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13 changes: 13 additions & 0 deletions pandas/tests/series/test_constructors.py
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Expand Up @@ -1810,6 +1810,19 @@ def test_constructor_bool_dtype_missing_values(self):
expected = Series(True, index=[0], dtype="bool")
tm.assert_series_equal(result, expected)

@pytest.mark.parametrize("int_dtype", ["int64"])
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use this fixture instead: any_int_dtype

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Done

def test_constructor_int64_dtype(self, int_dtype):
# GH-44923
result = Series(["-1", "0", "1", "2"], dtype=int_dtype)
expected = Series([-1, 0, 1, 2])
tm.assert_series_equal(result, expected)

def test_constructor_float64_dtype(self):
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use any_float_dtype

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Done

# GH-44923
result = Series(["0", "1", "2"], dtype="float64")
expected = Series([0.0, 1.0, 2.0])
tm.assert_series_equal(result, expected)

@pytest.mark.filterwarnings(
"ignore:elementwise comparison failed:DeprecationWarning"
)
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