DOC: update the pandas.errors.DtypeWarning docstring
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:

```
################################################################################
#################### Docstring (pandas.errors.DtypeWarning) ####################
################################################################################

Warning raised when reading different dtypes in a column from a file.

Raised for a dtype incompatibility. This can happen whenever `read_csv`
or `read_table` encounter non-uniform dtypes in a column(s) of a given
CSV file.

See Also
--------
pandas.read_csv : Read CSV (comma-separated) file into a DataFrame.
pandas.read_table : Read general delimited file into a DataFrame.

Notes
-----
This warning is issued when dealing with larger files because the dtype
checking happens per chunk read.

Despite the warning, the CSV file is read with mixed types in a single
column which will be an object type. See the examples below to better
understand this issue.

Examples
--------
This example creates and reads a large CSV file with a column that contains
`int` and `str`.

>>> df = pd.DataFrame({'a':['1']*100000 + ['X']*100000 + ['1']*100000,
...                    'b':['b']*300000})
>>> df.to_csv('test.csv', index=False)
>>> df2 = pd.read_csv('test.csv')
>>> DtypeWarning: Columns (0) have mixed types... # doctest: +SKIP

Important to notice that df2 will contain both `str` and `int` for the
same input, '1'.

>>> df2.iloc[262140, 0]
'1'
>>> type(df2.iloc[262140, 0])
<class 'str'>
>>> df2.iloc[262150, 0]
1
>>> type(df2.iloc[262150, 0])
<class 'int'>

One way to solve this issue is using the parameter `converters` in the
`read_csv` and `read_table` functions to explicit the conversion:

>>> df2 = pd.read_csv('test', sep=' ', converters={'a': str})
scripts/validate_docstrings.py:268: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
  runner.run(test)

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

Errors found:
	No returns section found
```
