BUG: groupby first()/last() change datatype of all NaN columns to float; nth() preserves datatype

[jdmarino]

• [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.
  Version 1.0.3 from the conda distro.

Code Sample, a copy-pastable example

import pandas as pd
print('pandas version is', pd.__version__, '\n')

df = pd.DataFrame({'id':['a','b','b','c'], 'sym':['ibm','msft','msft','goog'], 'idx':range(4), 'sectype':['E','E','E','E']})
df['osi'] = df.sym.where(df.sectype=='O')  # add a column of NaNs that are object/str
print(df)
print(df.info())

# .nth(-1) does the right thing
x = df.groupby('id').nth(-1)
print('\nnth(-1)')
print(x.info())

# .last() converts the osi col to floats
x = df.groupby('id').last()
print('\nlast()')
print(x.info())

# .nth(0) does the right thing
x = df.groupby('id').nth(-1)
print('\nnth(0)')
print(x.info())

# .first() converts the osi col to floats
x = df.groupby('id').first()
print('\nfirst()')
print(x.info())

Problem description

Given a dataframe with an all-NaN column of str/objects, performing a groupby().first() will convert the all-NaN column to float. This is true for .last() as well, but not for .nth(0) and .nth(-1), so these are workarounds.

This is a problem for me as the groupby().first() is in general code that is iteratively called and the results either pd.concat'd (resulting in a column with mixed types) or appended to an hdf file (causing failure on the write).

Expected Output

The resulting dataframe from a groupby().first()/.last() should have the same metadata (column structure and datatypes) as the input dataframe. The result of .first()/.last() should match that of .nth(0)/.nth(-1) .

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None

pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0

[dsaxton]

I think this is fixed on master:

[ins] In [1]: import pandas as pd
         ...:
         ...: df = pd.DataFrame(
         ...:     {
         ...:         "id": ["a", "b", "b", "c"],
         ...:         "sym": ["ibm", "msft", "msft", "goog"],
         ...:         "sectype": ["E", "E", "E", "E"],
         ...:     }
         ...: )
         ...: df["osi"] = df.sym.where(df.sectype == "O")
         ...: print(df.dtypes)
         ...: print(df.groupby("id")["osi"].first())
         ...: print(df.groupby("id")["osi"].last())
         ...: print(pd.__version__)
         ...:
id         object
sym        object
sectype    object
osi        object
dtype: object
id
a    NaN
b    NaN
c    NaN
Name: osi, dtype: object
id
a    NaN
b    NaN
c    NaN
Name: osi, dtype: object
1.1.0.dev0+1288.g3a5ae505b

[simonjayhawkins]

I think this is fixed on master:

The code sample is different from the OP. The issue in OP does not appear to be fixed.

>>> import numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1302.ge878fdc41'
>>>
>>> df = pd.DataFrame(
...     {
...         "id": ["a", "b", "b", "c"],
...         "sym": ["ibm", "msft", "msft", "goog"],
...         "idx": range(4),
...         "sectype": ["E", "E", "E", "E"],
...     }
... )
>>> df["osi"] = df.sym.where(df.sectype == "O")  # add a column of NaNs that are object/str
>>>
>>>
>>> x = df.groupby("id").first()
>>> x.osi.dtype
dtype('float64')
>>>
>>> x = df.groupby("id").osi.first()
>>> x.dtypes
dtype('O')
>>>

[dsaxton]

@simonjayhawkins Oops, yeah I think you're right