BUG: 'Series.to_numpy(dtype=, na_value=)' behaves differently with 'pd.NA' and 'np.nan' #48951
Description
Pandas version checks
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
import numpy as np
x1 = pd.Series([1, 2, None, 4])
x2 = pd.Series([1, 2, np.nan, 4])
x3 = pd.Series([1, 2, pd.NA, 4])
print(x1.to_numpy('int32', na_value=0))
# [1 2 0 4]
print(x2.to_numpy('int32', na_value=0))
# [1 2 0 4]
print(x3.to_numpy('int32', na_value=0))
# Traceback (most recent call last):
# File "<input>", line 14, in <module>
# print(x3.to_numpy('int32', na_value=0))
# File "C:\src\venv\w310\lib\site-packages\pandas\core\base.py", line 535, in to_numpy
# result = np.asarray(self._values, dtype=dtype)
# TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NAType'
print(x3.to_numpy('float64', na_value=0))
# Traceback (most recent call last):
# File "<input>", line 22, in <module>
# print(x3.to_numpy('float64', na_value=0))
# File "C:\src\venv\w310\lib\site-packages\pandas\core\base.py", line 535, in to_numpy
# result = np.asarray(self._values, dtype=dtype)
# TypeError: float() argument must be a string or a real number, not 'NAType'Issue Description
It appears that a Series that has a missing value that was created using either None or np.nan can be replaced by using Series.to_numpy(dtype=, na_value=), but one created with pd.NA fails with a raised exception (both arguments must be specified to trigger the behavior).
Expected Behavior
It is expected that since all three values (None, np.nan, and pd.NA) all represent missing values, that all three should behave the same. For the above reproducible example, the print statements should all report [1 2 0 4] (or [1. 2. 0. 4.] for the fourth 'float64' case).