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DiscussionForumPosting:
      context:https://schema.org
      headline:BUG: Dividing Int64 and float64 results in a mix of null-types
      articleBody:- [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas as pd import numpy as np data = { "A": [15, np.nan, 5, 4], "B": [15, 5, np.nan, 4], } df = pd.DataFrame(data) df = df.astype({"A": "Int64", "B": "float64"}) df["C"] = df["A"] / df["B"] print(df) print(df.dtypes) print(df.dropna(subset=["C"])) ``` This example outputs: ``` A B C 0 15 15.0 1.0 1 <NA> 5.0 <NA> 2 5 NaN NaN 3 4 4.0 1.0 A Int64 B float64 C Float64 dtype: object A B C 0 15 15.0 1.0 2 5 NaN NaN 3 4 4.0 1.0 ``` #### Problem description Dividing an ``Int64`` column by a ``float64`` column results in a ``Float64`` column that can contain both ``<NA>`` and ``NaN`` values. However, only ``<NA>`` values will be removed by the ``dropna`` function. I would expect only ``<NA>`` values in the new column and I would also expect the ``dropna`` function to remove all NA variants (both ``<NA>`` and ``NaN``). #### Expected Output The row with index 2 should be dropped. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : f00ed8f47020034e752baf0250483053340971b0 python : 3.9.4.final.0 python-bits : 64 OS : Darwin OS-release : 20.5.0 Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.3.0 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.3 setuptools : 49.2.1 Cython : 0.29.23 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 7.23.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : 2021.06.0 fastparquet : 0.6.3 gcsfs : None matplotlib : 3.4.1 numexpr : None odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 4.0.1 pyxlsb : None s3fs : None scipy : 1.6.3 sqlalchemy : 1.4.17 tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : None </details>
      author:
         url:https://github.com/tobiasbrodd
         type:Person
         name:tobiasbrodd
      datePublished:2021-07-20T16:16:18.000Z
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      url:https://github.com/42630/pandas/issues/42630
      context:https://schema.org
      headline:BUG: Dividing Int64 and float64 results in a mix of null-types
      articleBody:- [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas as pd import numpy as np data = { "A": [15, np.nan, 5, 4], "B": [15, 5, np.nan, 4], } df = pd.DataFrame(data) df = df.astype({"A": "Int64", "B": "float64"}) df["C"] = df["A"] / df["B"] print(df) print(df.dtypes) print(df.dropna(subset=["C"])) ``` This example outputs: ``` A B C 0 15 15.0 1.0 1 <NA> 5.0 <NA> 2 5 NaN NaN 3 4 4.0 1.0 A Int64 B float64 C Float64 dtype: object A B C 0 15 15.0 1.0 2 5 NaN NaN 3 4 4.0 1.0 ``` #### Problem description Dividing an ``Int64`` column by a ``float64`` column results in a ``Float64`` column that can contain both ``<NA>`` and ``NaN`` values. However, only ``<NA>`` values will be removed by the ``dropna`` function. I would expect only ``<NA>`` values in the new column and I would also expect the ``dropna`` function to remove all NA variants (both ``<NA>`` and ``NaN``). #### Expected Output The row with index 2 should be dropped. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : f00ed8f47020034e752baf0250483053340971b0 python : 3.9.4.final.0 python-bits : 64 OS : Darwin OS-release : 20.5.0 Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.3.0 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.3 setuptools : 49.2.1 Cython : 0.29.23 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 7.23.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : 2021.06.0 fastparquet : 0.6.3 gcsfs : None matplotlib : 3.4.1 numexpr : None odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 4.0.1 pyxlsb : None s3fs : None scipy : 1.6.3 sqlalchemy : 1.4.17 tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : None </details>
      author:
         url:https://github.com/tobiasbrodd
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         name:tobiasbrodd
      datePublished:2021-07-20T16:16:18.000Z
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      url:https://github.com/42630/pandas/issues/42630
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      name:tobiasbrodd
      url:https://github.com/tobiasbrodd
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