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BUG: Fix infer_dtype result for float with embedded pd.NA #61624

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BUG: Fix infer_dtype result for float with embedded pd.NA #61624

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3f1a734
BUG: Fix infer_dtype result for float with embedded pd.NA (#61621)
heoh Jun 10, 2025
a6b3962
Fix is_float_array stub arguments inconsistent
heoh Jun 10, 2025
33bd950
Fix side effects
heoh Jun 10, 2025
ddd7e3b
Ensure comparison with string
heoh Jun 23, 2025
c35c86d
Merge branch 'main' into fix/infer-dtype-numeric-with-na
heoh Jun 23, 2025
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -711,6 +711,7 @@ Timezones

Numeric
^^^^^^^
- Bug in :func:`api.types.infer_dtype` returning "mixed-integer-float" for float and ``pd.NA`` mix (:issue:`61621`)
- Bug in :meth:`DataFrame.corr` where numerical precision errors resulted in correlations above ``1.0`` (:issue:`61120`)
- Bug in :meth:`DataFrame.cov` raises a ``TypeError`` instead of returning potentially incorrect results or other errors (:issue:`53115`)
- Bug in :meth:`DataFrame.quantile` where the column type was not preserved when ``numeric_only=True`` with a list-like ``q`` produced an empty result (:issue:`59035`)
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2 changes: 1 addition & 1 deletion pandas/_libs/lib.pyi
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Expand Up @@ -60,7 +60,7 @@ def is_time_array(values: np.ndarray, skipna: bool = ...): ...
def is_date_array(values: np.ndarray, skipna: bool = ...): ...
def is_datetime_array(values: np.ndarray, skipna: bool = ...): ...
def is_string_array(values: np.ndarray, skipna: bool = ...): ...
def is_float_array(values: np.ndarray): ...
def is_float_array(values: np.ndarray, skipna: bool = ...): ...
def is_integer_array(values: np.ndarray, skipna: bool = ...): ...
def is_bool_array(values: np.ndarray, skipna: bool = ...): ...
def fast_multiget(
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8 changes: 5 additions & 3 deletions pandas/_libs/lib.pyx
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Expand Up @@ -1751,7 +1751,7 @@ def infer_dtype(value: object, skipna: bool = True) -> str:
return "complex"

elif util.is_float_object(val):
if is_float_array(values):
if is_float_array(values, skipna=skipna):
return "floating"
elif is_integer_float_array(values, skipna=skipna):
if is_integer_na_array(values, skipna=skipna):
Expand Down Expand Up @@ -1953,9 +1953,11 @@ cdef class FloatValidator(Validator):


# Note: only python-exposed for tests
cpdef bint is_float_array(ndarray values):
cpdef bint is_float_array(ndarray values, bint skipna=True):
cdef:
FloatValidator validator = FloatValidator(values.size, values.dtype)
FloatValidator validator = FloatValidator(values.size,
values.dtype,
skipna=skipna)
return validator.validate(values)


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5 changes: 1 addition & 4 deletions pandas/core/dtypes/cast.py
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Expand Up @@ -1086,10 +1086,7 @@ def convert_dtypes(
elif (
infer_objects
and input_array.dtype == object
and (
isinstance(inferred_dtype, str)
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To be sure, this removal is unrelated to the other change here, is that right? That is, we can remove this line on main and it won't break any tests.

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@heoh heoh Jun 23, 2025 •
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That's right. I tested it on my forked repo, and all TC passed. (heoh#7)

At the time of writing the commit, I simply judged it as unnecessary code and removed it.
However, after seeing your question, I took a second look at the convert_dtypes() function. It exists for the integer and boolean cases as well, and I felt that it was intended to explicitly indicate that the inferred_dtype is of type str.
(Previous work: #50094 a4c409e)

Even though the test passes without it, I don't understand pandas well enough yet to anticipate the side effects. For now, I think it's safer to respect the existing code style, so I added it back.(ddd7e3b)

Please feel free to let me know if I need to make any corrections again. Thanks for the review.

and inferred_dtype == "mixed-integer-float"
)
and inferred_dtype == "floating"
):
inferred_dtype = pandas_dtype_func("Float64")

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9 changes: 9 additions & 0 deletions pandas/tests/dtypes/test_inference.py
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Expand Up @@ -1387,6 +1387,15 @@ def test_infer_dtype_period_with_na(self, na_value):
arr = np.array([na_value, Period("2011-01", freq="D"), na_value])
assert lib.infer_dtype(arr, skipna=True) == "period"

@pytest.mark.parametrize("na_value", [pd.NA, np.nan])
def test_infer_dtype_numeric_with_na(self, na_value):
# GH61621
arr = Series([1, 2, na_value], dtype=object)
assert lib.infer_dtype(arr, skipna=True) == "integer"

arr = Series([1.0, 2.0, na_value], dtype=object)
assert lib.infer_dtype(arr, skipna=True) == "floating"

def test_infer_dtype_all_nan_nat_like(self):
arr = np.array([np.nan, np.nan])
assert lib.infer_dtype(arr, skipna=True) == "floating"
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2 changes: 1 addition & 1 deletion pandas/tests/extension/test_arrow.py
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Expand Up @@ -3090,7 +3090,7 @@ def test_infer_dtype_pyarrow_dtype(data, request):
res = lib.infer_dtype(data)
assert res != "unknown-array"

if data._hasna and res in ["floating", "datetime64", "timedelta64"]:
if data._hasna and res in ["datetime64", "timedelta64"]:
mark = pytest.mark.xfail(
reason="in infer_dtype pd.NA is not ignored in these cases "
"even with skipna=True in the list(data) check below"
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