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src\venv\w310\lib\site-packages\pandas\core\base file ~/pandas-dev/pandas/core/base pandas/pandas/core/base integer-type pandas data 'natype' issue description nullable extension arrays personal information bug comment metadata assignees import pandas type projects fourth 'float64' case latest version represent missing values marcogorelli mentioned nullable type lib pd import numpy baloe edits open projects milestone bug exists pandas triage issue phofl mentioned objecttypeseries = floattypeseries result = np ' behaves differently main branch recent call raised exception exception raises related converting milestone relationships dtype=object real number print statements github dtype='float64' dtype=dtype bug issue floattypeseries 'natype' print respecting na_value x2 = pd dtype=float na_value=np na fails expected behavior

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      context:https://schema.org
      headline:BUG: 'Series.to_numpy(dtype=, na_value=)' behaves differently with 'pd.NA' and 'np.nan'
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python 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). ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 87cfe4e38bafe7300a6003a1d18bd80f3f77c763 python : 3.10.6.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19043 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.5.0 numpy : 1.23.3 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.3.0 pip : 22.2.2 Cython : 0.29.32 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.1 html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.6.0 numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : NoneReplace this line with the output of pd.show_versions() </details>
      author:
         url:https://github.com/bbassett-tibco
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      datePublished:2022-10-05T03:27:00.000Z
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      url:https://github.com/48951/pandas/issues/48951
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      headline:BUG: 'Series.to_numpy(dtype=, na_value=)' behaves differently with 'pd.NA' and 'np.nan'
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python 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). ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 87cfe4e38bafe7300a6003a1d18bd80f3f77c763 python : 3.10.6.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19043 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.5.0 numpy : 1.23.3 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.3.0 pip : 22.2.2 Cython : 0.29.32 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.1 html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.6.0 numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : NoneReplace this line with the output of pd.show_versions() </details>
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