def percentile(a, q, axis=None, out=None,<EOL>overwrite_input=False, interpolation='<STR_LIT>', keepdims=False):
q = array(q, dtype=np.float64, copy=True)<EOL>r, k = _ureduce(a, func=_percentile, q=q, axis=axis, out=out,<EOL>overwrite_input=overwrite_input,<EOL>interpolation=interpolation)<EOL>if keepdims:<EOL><INDENT>if q.ndim == <NUM_LIT:0>:<EOL><INDENT>return r.reshape(k)<EOL><DEDENT>else:<EOL><INDENT>return r.reshape([len(q)] + k)<EOL><DEDENT><DEDENT>else:<EOL><INDENT>return r<EOL><DEDENT>
Compute the qth percentile of the data along the specified axis.

Returns the qth percentile of the array elements.

Parameters
----------
a : array_like
    Input array or object that can be converted to an array.
q : float in range of [0,100] (or sequence of floats)
    Percentile to compute which must be between 0 and 100 inclusive.
axis : int or sequence of int, optional
    Axis along which the percentiles are computed. The default (None)
    is to compute the percentiles along a flattened version of the array.
    A sequence of axes is supported since version 1.9.0.
out : ndarray, optional
    Alternative output array in which to place the result. It must
    have the same shape and buffer length as the expected output,
    but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
    If True, then allow use of memory of input array `a` for
    calculations. The input array will be modified by the call to
    percentile. This will save memory when you do not need to preserve
    the contents of the input array. In this case you should not make
    any assumptions about the content of the passed in array `a` after
    this function completes -- treat it as undefined. Default is False.
    Note that, if the `a` input is not already an array this parameter
    will have no effect, `a` will be converted to an array internally
    regardless of the value of this parameter.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
    This optional parameter specifies the interpolation method to use,
    when the desired quantile lies between two data points `i` and `j`:
        * linear: `i + (j - i) * fraction`, where `fraction` is the
          fractional part of the index surrounded by `i` and `j`.
        * lower: `i`.
        * higher: `j`.
        * nearest: `i` or `j` whichever is nearest.
        * midpoint: (`i` + `j`) / 2.

    .. versionadded:: 1.9.0
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the original `arr`.

    .. versionadded:: 1.9.0

Returns
-------
percentile : scalar or ndarray
    If a single percentile `q` is given and axis=None a scalar is
    returned.  If multiple percentiles `q` are given an array holding
    the result is returned. The results are listed in the first axis.
    (If `out` is specified, in which case that array is returned
    instead).  If the input contains integers, or floats of smaller
    precision than 64, then the output data-type is float64. Otherwise,
    the output data-type is the same as that of the input.

See Also
--------
mean, median

Notes
-----
Given a vector V of length N, the q-th percentile of V is the q-th ranked
value in a sorted copy of V.  The values and distances of the two
nearest neighbors as well as the `interpolation` parameter will
determine the percentile if the normalized ranking does not match q
exactly. This function is the same as the median if ``q=50``, the same
as the minimum if ``q=0`` and the same as the maximum if ``q=100``.

Examples
--------
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> np.percentile(a, 50)
array([ 3.5])
>>> np.percentile(a, 50, axis=0)
array([[ 6.5,  4.5,  2.5]])
>>> np.percentile(a, 50, axis=1)
array([[ 7.],
       [ 2.]])

>>> m = np.percentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.percentile(a, 50, axis=0, out=m)
array([[ 6.5,  4.5,  2.5]])
>>> m
array([[ 6.5,  4.5,  2.5]])

>>> b = a.copy()
>>> np.percentile(b, 50, axis=1, overwrite_input=True)
array([[ 7.],
       [ 2.]])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.percentile(b, 50, axis=None, overwrite_input=True)
array([ 3.5])