Code Sample
import ibm_db_dbi
import ibm_db
import pandas as pd
conn_str = "DATABASE=<...>;HOSTNAME=<...>;PORT=<...>;PROTOCOL=TCPIP;UID=<...>;PWD=<...>;"
conn = ibm_db.connect(conn_str, "", "")
conni = ibm_db_dbi.Connection(conn)
df = pd.read_sql('SELECT <some rows> from <some table>, conni, coerce_float=False)
Problem description
If the SQL table contains integer columns with NULL values, the returned df has them converted to floats. I understand that it is currently not possible with pandas to have integer columns with NULL values. However, I want to be able to tell pandas NOT to convert the values to floats, in order not to loose precision. This is possible for other similar functions in pandas, such as read_csv, which has the argument dtype. Why not for read_sql??
Output of pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.20.2
pytest: 3.0.5
pip: 9.0.1
setuptools: 36.0.1
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
s3fs: None
pandas_gbq: None
pandas_datareader: None
Comment From: TomAugspurger
There's an open issue for this in https://github.com/pandas-dev/pandas/issues/13049