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BUG: MultiIndex.from_tuples() fails to infer dtype from nan-only values from an index #36375

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Description

@ssche
  • I have checked that this issue has not already been reported.

  • 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.


Code Sample, a copy-pastable example

In [23]: df2 = pd.DataFrame({'a': [np.nan, 1, 2, 1], 'b': [1, 1, 2, 1], 'd': [np.nan, np.nan, np.nan, np.nan], 'c': [1, 1, 1, 1]}) 

In [24]: df2.dtypes

Out[24]: 
a    float64
b      int64
d    float64
c      int64
dtype: object

In [29]: s2 = df2.groupby(['a', 'b', 'd'], dropna=False)['c'].sum()                                                                                                                                
In [30]: s2.index                                                                                                                                                                                  
Out[30]: 
MultiIndex([(1.0, 1, nan),
            (2.0, 2, nan),
            (nan, 1, nan)],
           names=['a', 'b', 'd'])

In [31]: s2.index.get_level_values(2).dtype                                                                                                                                                        
Out[31]: dtype('float64')

In [32]: pd.MultiIndex.from_tuples(s2.index)                                                                                                                                                       
Out[32]: 
MultiIndex([(1.0, 1, nan),
            (2.0, 2, nan),
            (nan, 1, nan)],
           )

In [34]: pd.MultiIndex.from_tuples(s2.index).get_level_values(2).dtype                                                                                                                             
Out[34]: dtype('O')

Problem description

When a MultiIndex is re-created from another MultiIndex which contains a nan-only column by using .from_tuples(), the dtype of the nan-only level is object when it should be float64.

As a workaround, the dtype can be inferred correctly when the original index is wrapped in a tuple:

In [35]: pd.MultiIndex.from_tuples(tuple(s2.index)).get_level_values(2).dtype                                                                                                                      
Out[35]: dtype('float64')

This problem also doesn't occur when there is at least one non-nan value in the index column:

In [36]: df3 = pd.DataFrame({'a': [np.nan, 1, 2, 1], 'b': [1, 1, 2, 1], 'd': [np.nan, np.nan, np.nan, 1], 'c': [1, 1, 1, 1]})
In [37]: pd.MultiIndex.from_tuples(df3.groupby(['a', 'b', 'd'], dropna=False)['c'].sum().index).get_level_values(2).dtype
Out[37]: dtype('float64')

Expected Output

The dtype is inferred correctly even without wrapping the source index into a tuple.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 2a7d332
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8

pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

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