Assigned conversion via loc to Int64 fails under peculiar conditions

Code Sample, a copy-pastable example if possible

test = pd.DataFrame({'a': ['test'], 'b': [np.nan]})
test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
# *** AttributeError: 'IntegerArray' object has no attribute 'size'

Problem description

Assignment of .astype('Int64') sometimes fails with AttributeError: 'IntegerArray' object has no attribute 'size'.

Initially, I thought this was a regression from #25584, but it only manifests under very specific conditions:

1. There is exactly one null value in the converted column

   # works
   test = pd.DataFrame({'a': ['test', 'test'], 'b': [np.nan, 1.0]})
   test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
   
   # works
   test = pd.DataFrame({'a': ['test', 'test'], 'b': [np.nan, np.nan]})
   test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
   
   # fails
   test = pd.DataFrame({'a': ['test'], 'b': [np.nan]})
   test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
   # *** AttributeError: 'IntegerArray' object has no attribute 'size'
   

2. There is another column in the DataFrame

   # works
   test = pd.DataFrame({'b': [np.nan]})
   test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
   

3. Assignment happens via df.loc[:, colname] - without assignment, or with assignment to df[colname], things work.

   test = pd.DataFrame({'a': ['test'], 'b': [np.nan]})
   
   # fails
   test.loc[:, 'b'] = test['b'].astype('Int64')
   # *** AttributeError: 'IntegerArray' object has no attribute 'size'
   
   # fails
   test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
   # *** AttributeError: 'IntegerArray' object has no attribute 'size'
   
   # works
   test.loc[:, 'b'].astype('Int64')
   
   # works
   test['b'] = test['b'].astype('Int64')
   

Expected Output

test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64') shouldn’t raise an AttributeError.

print(test) after assignment should read along the lines of

0    <NA>
dtype: Int64

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None python : 3.6.9.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-46-generic machine : x86_64 processor : byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.0.1 numpy : 1.16.1 pytz : 2018.9 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.4.0 Cython : None pytest : 3.10.1 hypothesis : None sphinx : 1.8.5 blosc : None feather : None xlsxwriter : None lxml.etree : 4.3.0 html5lib : None pymysql : 0.9.3 psycopg2 : None jinja2 : 2.10 IPython : 7.2.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : 4.3.0 matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : 3.10.1 pyxlsb : None s3fs : None scipy : 1.2.0 sqlalchemy : 1.2.17 tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None

[TAJD]

Please contribute! I’m no longer working on this.

[hardikpnsp]

So far I have added a test for functionality similar to the one provided in the issue. Basically, test astype via loc on np.nan on Int64, Int32 and Int16. should I add tests for pd.NA too? Was adding a test for Int32 and Int16 unnecessary? Also, do we need similar tests for Series? As I am quite new to this, I would appreciate some guidance.