DOC:Improve the docstring of DataFrame.iloc()
Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [X ] PR title is "DOC: update the <your-function-or-method> docstring"
- [ X] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
- [ X] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ X] The html version looks good: `python doc/make.py --single <your-function-or-method>`
- [X ] It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

```
(pandas_dev) tuhins-mbp:pandas tuhinm@us.ibm.com$ python scripts/validate_docstrings.py pandas.DataFrame.iloc

################################################################################
###################### Docstring (pandas.DataFrame.iloc)  ######################
################################################################################

Purely integer-location based indexing for selection by position.

``.iloc[]`` is primarily integer position based (from ``0`` to
``length-1`` of the axis), but may also be used with a boolean
array.

Allowed inputs are:

- An integer, e.g. ``5``.
- A list or array of integers, e.g. ``[4, 3, 0]``.
- A slice object with ints, e.g. ``1:7``.
- A boolean array.
- A ``callable`` function with one argument (the calling Series, DataFrame
  or Panel) and that returns valid output for indexing (one of the above)

``.iloc`` will raise ``IndexError`` if a requested indexer is
out-of-bounds, except *slice* indexers which allow out-of-bounds
indexing (this conforms with python/numpy *slice* semantics).

See Also
--------
DataFrame.ix : A primarily label-location based indexer, with integer position fallback.
DataFrame.loc : Fast integer location scalar accessor.

Examples
--------
>>> import pandas as pd
>>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
...           {'a': 100,  'b': 200, 'c': 300, 'd': 400},
...           {'a': 1000,  'b': 2000,  'c': 3000,  'd': 4000 }]
>>> df = pd.DataFrame(mydict)
>>> print(df.head())
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000
>>> print(df.iloc[0])
a    1
b    2
c    3
d    4
Name: 0, dtype: int64
>>> print(df.iloc[0:2])
     a    b    c    d
0        1    2    3    4
1  100  200  300  400

ref:`Selection by Position <indexing.integer>`

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	No returns section found



```

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

Error Returned because Class do not need return