read_csv returns different float values for same number

Code Sample, a copy-pastable example if possible

test.csv

-15.361
-15.361000

>>> import pandas as pd
>>> x = pd.read_csv('test.csv', header=None)
>>> x.loc[0, 0] == x.loc[1, 0]
False

Problem description / Expected output

The expected output of the code above is

>>> x.loc[0, 0] == x.loc[1, 0]
True

We should expect both -15.361 and -15.361000 to be converted to the same np.float64 representation. However, they are converted to different float values, differing in exactly the last bit of their floating point representation. For some reason, -15.361 gets converted incorrectly to 0xC02EB8D4FDF3B645 whereas -15.361000 is correctly to 0xC02EB8D4FDF3B646.

For completeness, here are some more comparisons x.loc[1, 0] is equal (==) to np.float64('-15.361'), np.float64('-15.361000'), and float('-15.361000'). x.loc[0, 0] is not equal to any of those.

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]

INSTALLED VERSIONS

commit: None python: 3.5.3.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-73-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8

pandas: 0.20.3 pytest: None pip: 9.0.1 setuptools: 36.2.4 Cython: None numpy: 1.13.1 scipy: 0.19.1 xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.1 pytz: 2017.2 blosc: None bottleneck: None tables: None numexpr: None feather: None matplotlib: 2.0.2 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 sqlalchemy: None pymysql: None psycopg2: None jinja2: None s3fs: None pandas_gbq: None pandas_datareader: None

[chris-b1]

Trying to get exact equality out of floating points is generally a losing battle, doubly so with a lossy format like csv - do use one of float_precision options if it’s important.

[chrisyeh96]

Thanks @jreback! Glad this finally is resolved 👍