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1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
Original file line number Diff line number Diff line change
Expand Up @@ -1326,6 +1326,7 @@ Other
^^^^^
- Bug in :class:`DataFrame` when passing a ``dict`` with a NA scalar and ``columns`` that would always return ``np.nan`` (:issue:`57205`)
- Bug in :class:`Series` ignoring errors when trying to convert :class:`Series` input data to the given ``dtype`` (:issue:`60728`)
- Bug in :func:`array` where it did not always raise an error when the passed data was not like 1D. (:issue:`63112`)
- Bug in :func:`eval` on :class:`ExtensionArray` on including division ``/`` failed with a ``TypeError``. (:issue:`58748`)
- Bug in :func:`eval` where method calls on binary operations like ``(x + y).dropna()`` would raise ``AttributeError: 'BinOp' object has no attribute 'value'`` (:issue:`61175`)
- Bug in :func:`eval` where the names of the :class:`Series` were not preserved when using ``engine="numexpr"``. (:issue:`10239`)
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1 change: 1 addition & 0 deletions pandas/_libs/lib.pyi
Original file line number Diff line number Diff line change
Expand Up @@ -165,6 +165,7 @@ def maybe_indices_to_slice(
indices: npt.NDArray[np.intp],
max_len: int,
) -> slice | npt.NDArray[np.intp]: ...
def is_all_scalar(obj: list | tuple) -> bool: ...
def is_all_arraylike(obj: list) -> bool: ...

# -----------------------------------------------------------------
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18 changes: 18 additions & 0 deletions pandas/_libs/lib.pyx
Original file line number Diff line number Diff line change
Expand Up @@ -862,6 +862,24 @@ cpdef ndarray[object] ensure_string_array(
return result


def is_all_scalar(obj: list | tuple) -> bool:
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Still not fond of doing linear search. But since it doesn't cause a performance regression, I guess it's fine.

cdef:
Py_ssize_t i, n = len(obj)
object temp

all_scalars = True

for i in range(n):
temp = obj[i]
if isinstance(temp, (bytes, str)):
continue
elif hasattr(temp, "__iter__"):
all_scalars = False
break

return all_scalars


def is_all_arraylike(obj: list) -> bool:
"""
Should we treat these as levels of a MultiIndex, as opposed to Index items?
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5 changes: 5 additions & 0 deletions pandas/core/construction.py
Original file line number Diff line number Diff line change
Expand Up @@ -321,6 +321,11 @@ def array(
return data.copy()
return data

# to avoid returning an array of string representation of objects.
if isinstance(dtype, StringDtype) and isinstance(data, (list, tuple)):
if not lib.is_all_scalar(data):
raise TypeError("Values must be a 1D list-like")

if isinstance(dtype, ExtensionDtype):
cls = dtype.construct_array_type()
return cls._from_sequence(data, dtype=dtype, copy=copy)
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20 changes: 20 additions & 0 deletions pandas/tests/arrays/test_array.py
Original file line number Diff line number Diff line change
Expand Up @@ -460,6 +460,26 @@ def test_nd_raises(data):
pd.array(data, dtype="int64")


@pytest.mark.parametrize(
"data",
[
# string 2D
[["a"], ["b"]],
# int 2D
[[1], [2]],
# float 2D
[[1.0], [2.0]],
# mixed 2D
[[1, 2], ["a", "b"]],
# mixed 3D
[[[1]], [["a"]], [[3.14]]],
],
)
def test_not_1D_like_raises(data):
with pytest.raises(TypeError, match="Values must be a 1D list-like"):
pd.array(data, dtype=pd.StringDtype())


def test_scalar_raises():
with pytest.raises(ValueError, match="Cannot pass scalar '1'"):
pd.array(1)
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