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fix : include datatype in memo key in sanitization function #62226

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fix : include datatype in memo key in sanitization function #62226

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6047584
fix : remove memo usage
vignesh14052002 Aug 31, 2025
6eb738a
fix linting
vignesh14052002 Aug 31, 2025
3078807
include type in memo key to handle 0,1,True and False conflict
vignesh14052002 Sep 3, 2025
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7 changes: 4 additions & 3 deletions pandas/_libs/parsers.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -2129,12 +2129,13 @@ def sanitize_objects(ndarray[object] values, set na_values) -> int:

for i in range(n):
val = values[i]
memo_key = (val, type(val))
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Perf impact? I suspect this hashing is slower

if val in na_values:
values[i] = onan
na_count += 1
elif val in memo:
values[i] = memo[val]
elif memo_key in memo:
values[i] = memo[memo_key]
else:
memo[val] = val
memo[memo_key] = val

return na_count
11 changes: 11 additions & 0 deletions pandas/tests/io/parser/common/test_common_basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@

from pandas._config import using_string_dtype

from pandas._libs import parsers as libparsers
from pandas.compat import HAS_PYARROW
from pandas.errors import (
EmptyDataError,
Expand Down Expand Up @@ -830,3 +831,13 @@ def test_read_seek(all_parsers):
actual = parser.read_csv(file)
expected = parser.read_csv(StringIO(content))
tm.assert_frame_equal(actual, expected)


def test_dtype_conversion_in_sanitization():
# GH60088
values = np.array([1, True], dtype=object)
expected = np.array([1, True], dtype=object)
libparsers.sanitize_objects(values, na_values=set())
for v, e in zip(values, expected):
assert v == e
assert type(v) == type(e)
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