def cut(x, bins, right=True, labels=None, retbins=False, precision=<NUM_LIT:3>,<EOL>include_lowest=False, duplicates='<STR_LIT>'):
<EOL>x_is_series, series_index, name, x = _preprocess_for_cut(x)<EOL>x, dtype = _coerce_to_type(x)<EOL>if not np.iterable(bins):<EOL><INDENT>if is_scalar(bins) and bins < <NUM_LIT:1>:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>try:  <EOL><INDENT>sz = x.size<EOL><DEDENT>except AttributeError:<EOL><INDENT>x = np.asarray(x)<EOL>sz = x.size<EOL><DEDENT>if sz == <NUM_LIT:0>:<EOL><INDENT>raise ValueError('<STR_LIT>')<EOL><DEDENT>rng = (nanops.nanmin(x), nanops.nanmax(x))<EOL>mn, mx = [mi + <NUM_LIT:0.0> for mi in rng]<EOL>if np.isinf(mn) or np.isinf(mx):<EOL><INDENT>raise ValueError('<STR_LIT>'<EOL>'<STR_LIT>')<EOL><DEDENT>elif mn == mx:  <EOL><INDENT>mn -= <NUM_LIT> * abs(mn) if mn != <NUM_LIT:0> else <NUM_LIT><EOL>mx += <NUM_LIT> * abs(mx) if mx != <NUM_LIT:0> else <NUM_LIT><EOL>bins = np.linspace(mn, mx, bins + <NUM_LIT:1>, endpoint=True)<EOL><DEDENT>else:  <EOL><INDENT>bins = np.linspace(mn, mx, bins + <NUM_LIT:1>, endpoint=True)<EOL>adj = (mx - mn) * <NUM_LIT>  <EOL>if right:<EOL><INDENT>bins[<NUM_LIT:0>] -= adj<EOL><DEDENT>else:<EOL><INDENT>bins[-<NUM_LIT:1>] += adj<EOL><DEDENT><DEDENT><DEDENT>elif isinstance(bins, IntervalIndex):<EOL><INDENT>if bins.is_overlapping:<EOL><INDENT>raise ValueError('<STR_LIT>')<EOL><DEDENT><DEDENT>else:<EOL><INDENT>if is_datetime64tz_dtype(bins):<EOL><INDENT>bins = np.asarray(bins, dtype=_NS_DTYPE)<EOL><DEDENT>else:<EOL><INDENT>bins = np.asarray(bins)<EOL><DEDENT>bins = _convert_bin_to_numeric_type(bins, dtype)<EOL>if (np.diff(bins.astype('<STR_LIT>')) < <NUM_LIT:0>).any():<EOL><INDENT>raise ValueError('<STR_LIT>')<EOL><DEDENT><DEDENT>fac, bins = _bins_to_cuts(x, bins, right=right, labels=labels,<EOL>precision=precision,<EOL>include_lowest=include_lowest,<EOL>dtype=dtype,<EOL>duplicates=duplicates)<EOL>return _postprocess_for_cut(fac, bins, retbins, x_is_series,<EOL>series_index, name, dtype)<EOL>
Bin values into discrete intervals.

Use `cut` when you need to segment and sort data values into bins. 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. Supports binning into an equal number of bins, or a
pre-specified array of bins.

Parameters
----------
x : array-like
    The input array to be binned. Must be 1-dimensional.
bins : int, sequence of scalars, or IntervalIndex
    The criteria to bin by.

    * 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 minimum
      and maximum values of `x`.
    * sequence of scalars : Defines the bin edges allowing for non-uniform
      width. No extension of the range of `x` is done.
    * IntervalIndex : Defines the exact bins to be used. Note that
      IntervalIndex for `bins` must be non-overlapping.

right : bool, default True
    Indicates whether `bins` includes the rightmost edge or not. If
    ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
    indicate (1,2], (2,3], (3,4]. This argument is ignored when
    `bins` is an IntervalIndex.
labels : array or bool, optional
    Specifies the labels for the returned bins. Must be the same length as
    the resulting bins. If False, returns only integer indicators of the
    bins. This affects the type of the output container (see below).
    This argument is ignored when `bins` is an IntervalIndex.
retbins : bool, default False
    Whether to return the bins or not. Useful when bins is provided
    as a scalar.
precision : int, default 3
    The precision at which to store and display the bins labels.
include_lowest : bool, default False
    Whether the first interval should be left-inclusive or not.
duplicates : {default 'raise', 'drop'}, optional
    If bin edges are not unique, raise ValueError or drop non-uniques.

    .. versionadded:: 0.23.0

Returns
-------
out : Categorical, Series, or ndarray
    An array-like object representing the respective bin for each value
    of `x`. The type depends on the value of `labels`.

    * True (default) : returns a Series for Series `x` or a
      Categorical for all other inputs. The values stored within
      are Interval dtype.

    * sequence of scalars : returns a Series for Series `x` or a
      Categorical for all other inputs. The values stored within
      are whatever the type in the sequence is.

    * False : returns an ndarray of integers.

bins : numpy.ndarray or IntervalIndex.
    The computed or specified bins. Only returned when `retbins=True`.
    For scalar or sequence `bins`, this is an ndarray with the computed
    bins. If set `duplicates=drop`, `bins` will drop non-unique bin. For
    an IntervalIndex `bins`, this is equal to `bins`.

See Also
--------
qcut : Discretize variable into equal-sized buckets based on rank
    or based on sample quantiles.
Categorical : Array type for storing data that come from a
    fixed set of values.
Series : One-dimensional array with axis labels (including time series).
IntervalIndex : Immutable Index implementing an ordered, sliceable set.

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

Examples
--------
Discretize into three equal-sized bins.

>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3)
... # doctest: +ELLIPSIS
[(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
Categories (3, interval[float64]): [(0.994, 3.0] < (3.0, 5.0] ...

>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3, retbins=True)
... # doctest: +ELLIPSIS
([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
Categories (3, interval[float64]): [(0.994, 3.0] < (3.0, 5.0] ...
array([0.994, 3.   , 5.   , 7.   ]))

Discovers the same bins, but assign them specific labels. Notice that
the returned Categorical's categories are `labels` and is ordered.

>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]),
...        3, labels=["bad", "medium", "good"])
[bad, good, medium, medium, good, bad]
Categories (3, object): [bad < medium < good]

``labels=False`` implies you just want the bins back.

>>> pd.cut([0, 1, 1, 2], bins=4, labels=False)
array([0, 1, 1, 3])

Passing a Series as an input returns a Series with categorical dtype:

>>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
...               index=['a', 'b', 'c', 'd', 'e'])
>>> pd.cut(s, 3)
... # doctest: +ELLIPSIS
a    (1.992, 4.667]
b    (1.992, 4.667]
c    (4.667, 7.333]
d     (7.333, 10.0]
e     (7.333, 10.0]
dtype: category
Categories (3, interval[float64]): [(1.992, 4.667] < (4.667, ...

Passing a Series as an input returns a Series with mapping value.
It is used to map numerically to intervals based on bins.

>>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
...               index=['a', 'b', 'c', 'd', 'e'])
>>> pd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)
... # doctest: +ELLIPSIS
(a    0.0
 b    1.0
 c    2.0
 d    3.0
 e    4.0
 dtype: float64, array([0, 2, 4, 6, 8]))

Use `drop` optional when bins is not unique

>>> pd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,
...        right=False, duplicates='drop')
... # doctest: +ELLIPSIS
(a    0.0
 b    1.0
 c    2.0
 d    3.0
 e    3.0
 dtype: float64, array([0, 2, 4, 6, 8]))

Passing an IntervalIndex for `bins` results in those categories exactly.
Notice that values not covered by the IntervalIndex are set to NaN. 0
is to the left of the first bin (which is closed on the right), and 1.5
falls between two bins.

>>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
>>> pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)
[NaN, (0, 1], NaN, (2, 3], (4, 5]]
Categories (3, interval[int64]): [(0, 1] < (2, 3] < (4, 5]]