BUG: default value for dtype_backend is not applied by default

[pumpkinlink]

BUG: default value for dtype_backend is not applied by default

[phofl]

the doc string is incorrect, sorry about this

the behaviour is correct

[rhshadrach]

Thanks for the report. I'd guess that this is the intention as integers with NA values cannot be stored in a NumPy array (they would be coerced to float or object). If that is the case, the docstring could be improved to include this (or a more generic description of it).

This was originally added in #50505; @phofl - was this case considered?

[luke396]

take

[luke396]

It is observed that the resulting data type is not solely dependent on the presence of NaN values.

ser2 = pd.Series(['150.0', '2'])
print(pd.to_numeric(ser2, dtype_backend='numpy_nullable'))
print(pd.to_numeric(ser2, dtype_backend='pyarrow'))
print(pd.to_numeric(ser2))

# 0    150.0
# 1      2.0
# dtype: Float64
# 0    150.0
# 1      2.0
# dtype: double[pyarrow]
# 0    150.0
# 1      2.0
# dtype: float64

The default dtype_backend value of NO_DEFAULT does not yield results similar to those obtained with numpy_nullable or pyarrow.

pandas/pandas/core/tools/numeric.py

Line 46 in 566e592

dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,

pandas/pandas/core/tools/numeric.py

Lines 213 to 220 in 566e592

	values, new_mask = lib.maybe_convert_numeric( # type: ignore[call-overload]
	values,
	set(),
	coerce_numeric=coerce_numeric,
	convert_to_masked_nullable=dtype_backend is not lib.no_default
	or isinstance(values_dtype, StringDtype)
	and not values_dtype.storage == "pyarrow_numpy",
	)

[rhshadrach]

Good find @luke396 - this documentation became incorrect in #54104. We should fix that as well!