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Keywords {🔍}

var, groupby, nan, bug, issue, categorical, pandas, ser, npnan, output, rhshadrach, sign, cat, member, commented, type, code, projects, result, dropnafalse, dropnafalsesum, added, fenderjazz, columns, noncat, mentioned, nas, timstaley, dropna, milestone, navigation, issues, pull, requests, actions, security, wrong, category, column, zxymath, pdcategoricala, dtype, float, expected, triage, reviewed, team, phofl, edited, edits,

Topics {✒️}

dask groupby type projects personal information bug groupby category column comment metadata assignees interpolate type rhshadrach mentioned dask timstaley edits categorical projects milestone output var1 var2 bug exists dropna=false fenderjazz edits np triage issue pandas float64 output milestone relationships bug groupby wrong result master branch observed=true distinct result na group test nas grouping columns handle nas issue dropna github ser = pd df = pd cat = pd 'var1' 'var2' ['var1' 'var2'] na code noncat noncat} nas columns pd ser {'ser' nan]

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• Already have an account?
• It looks different from #35646 to me -- is specifically about categoricals, and happens with >1 groupby columns?
• Have you checked #35646?

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DiscussionForumPosting:
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      headline:BUG: Get wrong result when groupby category column with dropna=False
      articleBody:I have confirmed this bug exists on the master branch of pandas. ```python # Your code here ser = pd.Series([1., 1., 1., 1.]) cat = pd.Categorical(['a', 'b', 'c', np.nan]) print(ser.groupby(cat, dropna=False).sum()) ``` ``` # output: a 1.0 b 1.0 c 1.0 dtype: float64 ``` #### Expected Output ``` a 1.0 b 1.0 c 1.0 NaN 1.0 dtype: float64 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6 python : 3.7.6.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.1.2 numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 45.2.0.post20200210 Cython : 0.29.15 pytest : 5.3.5 hypothesis : 5.5.4 sphinx : 2.4.0 blosc : None feather : None xlsxwriter : 1.2.7 lxml.etree : 4.5.0 html5lib : 1.0.1 pymysql : 0.9.3 psycopg2 : None jinja2 : 2.11.1 IPython : 7.12.0 pandas_datareader: None bs4 : 4.8.2 bottleneck : 1.3.2 fsspec : 0.6.2 fastparquet : None gcsfs : None matplotlib : 3.1.3 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.3 pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.3.13 tables : 3.6.1 tabulate : None xarray : None xlrd : 1.2.0 xlwt : 1.3.0 numba : 0.48.0 </details>
      author:
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         name:zxymath
      datePublished:2020-09-13T09:48:31.000Z
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      url:https://github.com/36327/pandas/issues/36327
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      headline:BUG: Get wrong result when groupby category column with dropna=False
      articleBody:I have confirmed this bug exists on the master branch of pandas. ```python # Your code here ser = pd.Series([1., 1., 1., 1.]) cat = pd.Categorical(['a', 'b', 'c', np.nan]) print(ser.groupby(cat, dropna=False).sum()) ``` ``` # output: a 1.0 b 1.0 c 1.0 dtype: float64 ``` #### Expected Output ``` a 1.0 b 1.0 c 1.0 NaN 1.0 dtype: float64 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6 python : 3.7.6.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.1.2 numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 45.2.0.post20200210 Cython : 0.29.15 pytest : 5.3.5 hypothesis : 5.5.4 sphinx : 2.4.0 blosc : None feather : None xlsxwriter : 1.2.7 lxml.etree : 4.5.0 html5lib : 1.0.1 pymysql : 0.9.3 psycopg2 : None jinja2 : 2.11.1 IPython : 7.12.0 pandas_datareader: None bs4 : 4.8.2 bottleneck : 1.3.2 fsspec : 0.6.2 fastparquet : None gcsfs : None matplotlib : 3.1.3 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.3 pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.3.13 tables : 3.6.1 tabulate : None xarray : None xlrd : 1.2.0 xlwt : 1.3.0 numba : 0.48.0 </details>
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      url:https://github.com/36327/pandas/issues/36327
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