Type casting after merge

[albertvillanova]

Code Sample, a copy-pastable example if possible

In [2]: df1 = pd.DataFrame({'id':[1.,2.], 'c1':[10,20]})

In [3]: df2 = pd.DataFrame({'id':[2], 'c2':[200]})

In [4]: df = df1.merge(df2, on='id', how='left')

In [5]: df['id'].dtype
Out[5]: dtype('O')

Problem description

When I merge 2 DataFrames on a column (id) which is of type float in one of the DataFrames and of type int in the other DataFrame, the resulting DataFrame column is of type Object. Why? I guess the resulting DataFrame should keep the float type.

Expected Output

In [5]: df['id'].dtype
Out[5]: dtype('float64')

Indeed, this was the behavior for pandas 0.19.2.

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-431.29.2.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: C LOCALE: None.None

pandas: 0.20.2
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[jreback]

This is as expected. We don't cast keys on divergent types for the keys. I suppose you could try to cast this upfront and make them both floats. So if you wanted to do a PR would be ok.

[mroeschke]

Looks to be fixed in master. Could use a test.

In [39]: In [2]: df1 = pd.DataFrame({'id':[1.,2.], 'c1':[10,20]})
    ...:
    ...: In [3]: df2 = pd.DataFrame({'id':[2], 'c2':[200]})
    ...:
    ...: In [4]: df = df1.merge(df2, on='id', how='left')
    ...:
    ...: In [5]: df['id'].dtype
Out[39]: dtype('float64')

In [40]: pd.__version__
Out[40]: '0.26.0.dev0+565.g8c5941cd5'