DOC: Improved the docstring of pandas.plotting.radviz
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.plotting.radviz) ######################
################################################################################

Plot a multidimensional dataset in 2D.

Each `Series` in the `DataFrame` is represented as a evenly distributed
slice on a circle. Each data point is rendered in the circle according to
the value on each `Series`. Highly correlated `Series` in the `DataFrame`
are placed closer on the unit circle.

RadViz allow to project a N-dimensional data set into a 2D space where the
influence of each dimension can be interpreted as a balance between the
influence of all dimensions.

Parameters
----------
frame : :class:`DataFrame`
    Pandas object holding the data.
class_column : str
    Column name containing the name of the data point category.
ax : :class:`matplotlib.axes.Axes`, optional
    A plot instance to which to add the information.
color : list[str] or tuple[str], optional
    Assign a color to each category. Example: ['blue', 'green'].
colormap : str or :class:`matplotlib.colors.Colormap`, default None
    Colormap to select colors from. If string, load colormap with that
    name from matplotlib.
kwds : optional
    Options to pass to matplotlib scatter plotting method.

Returns
-------
axes: :class:`matplotlib.axes.Axes`

See Also
--------
:func:`pandas.plotting.andrews_curves` : Plot clustering visualization

Examples
--------
.. plot::
    :context: close-figs

    >>> df = pd.DataFrame({
    ...         'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6,
    ...                            6.7, 4.6],
    ...         'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2,
    ...                            3.3, 3.6],
    ...         'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4,
    ...                            5.7, 1.0],
    ...         'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2,
    ...                            2.1, 0.2],
    ...         'Category': ['virginica', 'virginica', 'setosa',
    ...                         'virginica', 'virginica', 'versicolor',
    ...                         'versicolor', 'setosa', 'virginica',
    ...                         'setosa']
    ...     })
    >>> rad_viz = pd.plotting.radviz(df, 'Category')

################################################################################
################################## Validation ##################################
################################################################################
```