combine_first changes datatype

[mohitanand001]

df_1 = pd.DataFrame(data = {'A' : [1, 2, 3], 'B' : [4, 5, 6]})
df_2 = pd.DataFrame(data = {'A' : [1, 20, 30], 'B' : [40, 50, 60], 'C' : [12, 34, 65]})
df_1.combine_first(df_2).dtypes
A      int64
B      int64
C    float64
dtype: object

Problem description

The combine_first changes the datatype of the the column C to float64 from int64. Is this
an expected behavior

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.7.1.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None

pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.7
dateutil : 2.7.5
pip : 18.1
setuptools : 40.6.3
Cython : 0.29.2
pytest : 4.0.2
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.15
tables : 3.4.4
xarray : None
xlrd : 1.0.0
xlwt : 1.3.0
xlsxwriter : 1.1.2

@mroeschke I would like to work on this issue. Is this issue suitable for 1st time contributors and if so, could I get more more details on the problem and suggestions on how to fix the issue?

[mroeschke]

We have a good first issue label that indicates issues that are good for first time contributors

https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22

[mroeschke]

This looks fixed on master. Could use a test

In [6]: df_1 = pd.DataFrame(data = {'A' : [1, 2, 3], 'B' : [4, 5, 6]})
   ...: df_2 = pd.DataFrame(data = {'A' : [1, 20, 30], 'B' : [40, 50, 60], 'C' : [12, 34, 65]})
   ...: df_1.combine_first(df_2).dtypes
Out[6]:
A    int64
B    int64
C    int64
dtype: object

[goyal-aman]

@mroeschke I would like to work on it. Though this would be my first contribution. Can you suggest how to go about doing this?

[mroeschke]

Sure, see https://pandas.pydata.org/pandas-docs/stable/development/contributing_codebase.html#writing-tests

[goyal-aman]

@mroeschke Hi, does this make any sense? I have little understanding on what is acceptable test. If this is correct, then I can
write for float and other types as well.

  def test_combine_first_dtype_stay_same_int(self):
      df_1 = pd.DataFrame(data = {'A' : [1, 2, 3], 'B' : [4, 5, 6]})
      df_2 = pd.DataFrame(data = {'A' : [1, 20, 30], 'B' : [40, 50, 60], 'C' : [12, 34, 65]})
      result = df_1.combine_first(df_2)
  
      exp = pd.DataFrame(data = {
          'A':[1, 2, 3],
          'B':[4, 5, 6], 
          'C':[12, 34, 65]
      })        
      tm.assert_equal(result.dtypes, exp.dtypes)