Inconsistent dtype changes between multi-column assignment and single-column assignment

[howsiwei]

Code Sample, a copy-pastable example if possible

import pandas as pd

df = pd.DataFrame({'a': [0.0], 'b': 0.0})

df[['a', 'b']] = 0
print(df)
print()

df['b'] = 0
print(df)

Output:

     a    b
0  0.0  0.0

     a  b
0  0.0  0

Problem description

As you can see above, the column type doesn't change in multi-column assignment but changes from float64 to int64 in single-column assignment. This behavior seems quite inconsistent.

Expected Output

   a  b
0  0  0

   a  b
0  0  0

Both multi-column assignment and single-column assignment should result in the correct dtype.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 3b96ada
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-52-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8

pandas : 0.25.0rc0+131.g3b96ada3a
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.7
pytest : 4.4.2
hypothesis : 4.17.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None

[WillAyd]

Hmm yea looks strange. If you have a PR to fix I think would welcome

[mroeschke]

This looks okay on master. Could use a test

In [4]: import pandas as pd
   ...:
   ...: df = pd.DataFrame({'a': [0.0], 'b': 0.0})
   ...:
   ...: df[['a', 'b']] = 0
   ...: print(df)
   ...: print()
   ...:
   ...: df['b'] = 0
   ...: print(df)
   a  b
0  0  0

   a  b
0  0  0

[jackgoldsmith4]

take