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dtype, bug, object, pandas, index, issue, dtypefloat, multiindex, pdseriesnpnan, allnan, ssche, pddataframea, npnan, droptrue, combinefirst, columns, sign, coerces, dfsetindexa, conversions, phofl, projects, closed, expected, type, member, navigation, pull, requests, actions, security, description, version, confirmed, exists, import, dfcombinefirstdfindexdtypes, int, dimensions, dfsetindexx, float, added, triage, reviewed, team, unexpected, buggy, commented, multiindexjoin, mentioned,

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personal information bug single-dimension index comment metadata assignees index dimensions remains index columns latest version type projects phofl mentioned dtype dtype issue persists triage issue projects milestone 2 bug exists dtype=float original dtype 0 closed type float object dtype dtype object pandas pandas 1 issue main branch drop=true dataframes involved expected behavior 100% complete relationships nan values df1 = pd bug github index dtype join dimensions expected 'float64' df1 nan nan] nan multiindex `multiindex` pd df2 object `object` sign [np np

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DiscussionForumPosting:
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      headline:BUG: `combine_first()` coerces to `object` for `MultiIndex` and all-nan index columns
      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. - [x] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python >>> import numpy as np >>> import pandas as pd >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> >>> df1 = df1.set_index(['A', 'X'], drop=True) >>> >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df2 = df2.set_index(['A', 'X'], drop=True) >>> >>> df1.combine_first(df2).index.dtypes A int64 X object dtype: object ``` ### Issue Description `df1.combine_first(df2)` produces a new index with dtype object when both `df1` and `df2` use a `MultiIndex` where one of the dimensions contains all-nan values (in the example column "X"). It's expected that after `combine_first()` the original dtype of the index dimensions remains the same (in particular when both dataframes involved had the same dtype to begin with). Note: this doesn't happen for a single-dimension index: ```python >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df1 = df1.set_index(['X'], drop=True) >>> >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df2 = df2.set_index(['X'], drop=True) >>> >>> df1.combine_first(df2).index.dtype dtype('float64') ``` or if one of the (all-nan) index columns contains a value that is different to nan: ```python >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4], 'X': pd.Series([np.nan, 1.0], dtype=float)}) >>> >>> df1 = df1.set_index(['A', 'X'], drop=True) >>> >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df2 = df2.set_index(['A', 'X'], drop=True) >>> >>> df1.combine_first(df2).index.dtypes A int64 X float64 <---- expected result even for df1 = pd.DataFrame({... 'X': pd.Series([np.nan, np.nan], dtype=float)}) dtype: object ``` This issue persists for Pandas 1.5.0, 1.5.1 and 2.0.0.dev0+724.ga6d3e13b3. ### Expected Behavior No change in dtype ("X" is of type float, not object) ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 87cfe4e38bafe7300a6003a1d18bd80f3f77c763 python : 3.8.15.final.0 python-bits : 64 OS : Linux OS-release : 6.0.8-300.fc37.x86_64 Version : #1 SMP PREEMPT_DYNAMIC Fri Nov 11 15:09:04 UTC 2022 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_AU.UTF-8 LOCALE : en_AU.UTF-8 pandas : 1.5.0 numpy : 1.23.2 pytz : 2020.4 dateutil : 2.8.1 setuptools : 59.6.0 pip : 22.3.1 Cython : 0.29.32 pytest : 7.1.2 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 0.9.6 lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.8.6 jinja2 : 2.11.2 IPython : None pandas_datareader: None bs4 : None bottleneck : 1.3.5 brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : 2.8.1 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 1.0.1 pyreadstat : None pyxlsb : None s3fs : None scipy : 1.4.1 snappy : None sqlalchemy : 1.3.23 tables : 3.7.0 tabulate : None xarray : None xlrd : 2.0.1 xlwt : None zstandard : None tzdata : None </details>
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      url:https://github.com/49830/pandas/issues/49830
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      headline:BUG: `combine_first()` coerces to `object` for `MultiIndex` and all-nan index columns
      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. - [x] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python >>> import numpy as np >>> import pandas as pd >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> >>> df1 = df1.set_index(['A', 'X'], drop=True) >>> >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df2 = df2.set_index(['A', 'X'], drop=True) >>> >>> df1.combine_first(df2).index.dtypes A int64 X object dtype: object ``` ### Issue Description `df1.combine_first(df2)` produces a new index with dtype object when both `df1` and `df2` use a `MultiIndex` where one of the dimensions contains all-nan values (in the example column "X"). It's expected that after `combine_first()` the original dtype of the index dimensions remains the same (in particular when both dataframes involved had the same dtype to begin with). Note: this doesn't happen for a single-dimension index: ```python >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df1 = df1.set_index(['X'], drop=True) >>> >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df2 = df2.set_index(['X'], drop=True) >>> >>> df1.combine_first(df2).index.dtype dtype('float64') ``` or if one of the (all-nan) index columns contains a value that is different to nan: ```python >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4], 'X': pd.Series([np.nan, 1.0], dtype=float)}) >>> >>> df1 = df1.set_index(['A', 'X'], drop=True) >>> >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3], 'X': pd.Series([np.nan, np.nan], dtype=float)}) >>> df2 = df2.set_index(['A', 'X'], drop=True) >>> >>> df1.combine_first(df2).index.dtypes A int64 X float64 <---- expected result even for df1 = pd.DataFrame({... 'X': pd.Series([np.nan, np.nan], dtype=float)}) dtype: object ``` This issue persists for Pandas 1.5.0, 1.5.1 and 2.0.0.dev0+724.ga6d3e13b3. ### Expected Behavior No change in dtype ("X" is of type float, not object) ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 87cfe4e38bafe7300a6003a1d18bd80f3f77c763 python : 3.8.15.final.0 python-bits : 64 OS : Linux OS-release : 6.0.8-300.fc37.x86_64 Version : #1 SMP PREEMPT_DYNAMIC Fri Nov 11 15:09:04 UTC 2022 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_AU.UTF-8 LOCALE : en_AU.UTF-8 pandas : 1.5.0 numpy : 1.23.2 pytz : 2020.4 dateutil : 2.8.1 setuptools : 59.6.0 pip : 22.3.1 Cython : 0.29.32 pytest : 7.1.2 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 0.9.6 lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.8.6 jinja2 : 2.11.2 IPython : None pandas_datareader: None bs4 : None bottleneck : 1.3.5 brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : 2.8.1 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 1.0.1 pyreadstat : None pyxlsb : None s3fs : None scipy : 1.4.1 snappy : None sqlalchemy : 1.3.23 tables : 3.7.0 tabulate : None xarray : None xlrd : 2.0.1 xlwt : None zstandard : None tzdata : None </details>
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