A small, complete example of the issue

In [37]: df = pd.DataFrame(dict(a=[0,1,2,3],b=[0.0,1.0,2.0,3.0]))

In [38]: df
Out[38]:
   a    b
0  0  0.0
1  1  1.0
2  2  2.0
3  3  3.0

In [39]: df.dtypes
Out[39]:
a      int64
b    float64
dtype: object

In [40]: df.iloc[0]
Out[40]:
a    0.0
b    0.0
Name: 0, dtype: float64

In [41]: df.loc[1,'a']
Out[41]: 1.0

Expected Output

Any values from column 'a' should still be ints.

Output of pd.show_versions()

## INSTALLED VERSIONS commit: None python: 2.7.12.final.0 python-bits: 64 OS: Darwin OS-release: 15.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.18.1 nose: 1.3.7 pip: 8.1.2 setuptools: 23.0.0 Cython: 0.24 numpy: 1.11.1 scipy: 0.17.1 statsmodels: 0.6.1 xarray: None IPython: 4.2.0 sphinx: 1.4.1 patsy: 0.4.1 dateutil: 2.5.3 pytz: 2016.4 blosc: None bottleneck: 1.1.0 tables: 3.2.2 numexpr: 2.6.0 matplotlib: 1.5.1 openpyxl: 2.3.2 xlrd: 1.0.0 xlwt: 1.1.2 xlsxwriter: 0.9.2 lxml: 3.6.0 bs4: 4.4.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.0.13 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.40.0 pandas_datareader: None

Comment From: TomAugspurger

Your Out[40] is the correct behavior, since a Series (which is what that slice returns) can only have a single dtype, the int will be cast to a float.

I thought we had an open issue for your Out[41], but wasn't able to find it; thanks for reporting it.

Comment From: sinhrks

Out[41] is a dupe with #11617. Thx for the report.

Comment From: nileracecrew

Your Out[40] is the correct behavior, since a Series (which is what that slice returns) can only have a single dtype, the int will be cast to a float.

It could be returned as object, which what happens when some columns are non-numeric. But I guess this is done for understandable performance reasons.

Thanks.