 DOC: Fixed example & description for pandas.cut
- [ ] closes #xxxx
- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

```
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############################ Docstring (pandas.cut) ############################
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Return indices of half-open `bins` to which each value of `x` belongs.

Use `cut` when you need to segment and sort data values into bins or
buckets of data. This function is also useful for going from a continuous
variable to a categorical variable. For example, `cut` could convert ages
to groups of age ranges.

Parameters
----------
x : array-like
    Input array to be binned. It has to be 1-dimensional.
bins : int, sequence of scalars, or pandas.IntervalIndex
    If `bins` is an int, defines the number of equal-width bins in the
    range of `x`. The range of `x` is extended by .1% on each side to
    include the min or max values of `x`.
    If `bins` is a sequence, defines the bin edges allowing for
    non-uniform bin width. No extension of the range of `x` is done.
right : bool, optional, default 'True'
    Indicates whether the `bins` include the rightmost edge or not. If
    `right == True` (the default), then the `bins` [1,2,3,4] indicate
    (1,2], (2,3], (3,4].
labels : array or bool, optional
    Used as labels for the resulting `bins`. Must be of the same length as
    the resulting `bins`. If False, returns only integer indicators of the
    `bins`.
retbins : bool, optional, default 'False'
    Whether to return the `bins` or not. Useful when `bins` is provided
    as a scalar.
precision : int, optional, default '3'
    The precision at which to store and display the `bins` labels.
include_lowest : bool, optional, default 'False'
    Whether the first interval should be left-inclusive or not.

Returns
-------
out : pandas.Categorical or Series, or array of int if `labels` is 'False'
    The return type depends on the input.
    If the input is a Series, a Series of type category is returned.
    Else - pandas.Categorical is returned. `Bins` are represented as
    categories when categorical data is returned.
bins : numpy.ndarray of floats
    Returned only if `retbins` is 'True'.

See Also
--------
qcut : Discretize variable into equal-sized buckets based on rank
    or based on sample quantiles.
pandas.Categorical : Represents a categorical variable in
    classic R / S-plus fashion.
Series : One-dimensional ndarray with axis labels (including time series).
pandas.IntervalIndex : Immutable Index implementing an ordered,
    sliceable set. IntervalIndex represents an Index of intervals that
    are all closed on the same side.

Notes
-----
Any NA values will be NA in the result. Out of bounds values will be NA in
the resulting pandas.Categorical object.

Examples
--------
>>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]), 3, retbins=True)
... # doctest: +ELLIPSIS
([(0.19, 3.367], (0.19, 3.367], (0.19, 3.367], (3.367, 6.533], ...
Categories (3, interval[float64]): [(0.19, 3.367] < (3.367, 6.533] ...

>>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]),
...        3, labels=["good", "medium", "bad"])
... # doctest: +SKIP
[good, good, good, medium, bad, good]
Categories (3, object): [good < medium < bad]

>>> pd.cut(np.ones(5), 4, labels=False)
array([1, 1, 1, 1, 1], dtype=int64)

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################################## Validation ##################################
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Docstring for "pandas.cut" correct. :)
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