pd.read_csv generating different pd.DataFrames with identical dataset
#### Code Sample, a copy-pastable example if possible

```python


For loading the csvfile:

loadedFile = pd.read_csv(fileLocation)


For getting the "description" of the pd.DataFrame

loadedFile.describe()


For outputting the pd.DataFrames

         #  open DEBUG File
         file_debug_setup = open('/'XXX'/'XXX'/DEBUG_selectionPreparation_2.txt', "w")

          ##  outputs entire DataFrame ...
         with pd.option_context('display.max_rows', None, 'display.max_columns', None):
              file_debug_setup.write("" + str(original_dataset) + "\n\n")

        file_debug_setup.close()


*where 'original_dataset' contains the present pd.DataFrame from 'loadedFile'
This code is run twice, to compare the pd.DataFrames generated from each separate individual run



```
#### Problem description


The code above is run twice (or any number of times, greater than one) each run generates
a pd.DataFrame using the same programming code.  The problem is that a *slightly* different pd.DataFrame is being generated, EVEN THOUGH THE DATASET LOADED IS IDENTICAL EACH TIME - THE SAME CODE IS BEING USED TO GENERATE THE PD.DATAFRAME


When loading the identical dataset, the program returns a pd.DataFrame that contains slightly different values.   When testing the two pd.DataFrames,  the ".describe()" function returns slightly different values for the quartiles of each attribute.  Further affirming the idea that the datasets generated are different is that a comparison of the datasets (outputted using code above) using the linux/unix 'diff' command results in many contrasts.

The difference is minimal - usually a hundredth of a decimal point or smaller.  However ever small this may be, IS WITHIN A STATISTICAL CONTEXT large.  For example, if selecting a model using selections via MSE, the rounding of these attributes significantly alter the selection process - leading to different selection of attributes when analyzing an IDENTICAL dataset using IDENTICAL code.
 

#### Expected Output

The pd.DataFrame values should always be identical when loading IDENTICAL DATASETS with IDENTICAL CODE


#### Output of ``pd.show_versions()``

<details>

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

In [17]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 17.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
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

</details>
