code: def nanpercentile(
      a,
      q,
      axis=None,
      out=None,
      overwrite_input=False,
      interpolation="linear",
      keepdims=np._NoValue,
  ):
      """
      Compute the qth percentile of the data along the specified axis,
      while ignoring nan values.
  
      Returns the qth percentile(s) of the array elements.
  
      .. versionadded:: 1.9.0
  
      Parameters
      ----------
      a : array_like
          Input array or object that can be converted to an array, containing
          nan values to be ignored.
      q : array_like of float
          Percentile or sequence of percentiles to compute, which must be between
          0 and 100 inclusive.
      axis : {int, tuple of int, None}, optional
          Axis or axes along which the percentiles are computed. The
          default is to compute the percentile(s) along a flattened
          version of the array.
      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 the input array `a` to be modified by intermediate
          calculations, to save memory. In this case, the contents of the input
          `a` after this function completes is undefined.
      interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
          This optional parameter specifies the interpolation method to
          use when the desired percentile lies between two data points
          ``i < 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``.
      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 array `a`.
  
          If this is anything but the default value it will be passed
          through (in the special case of an empty array) to the
          `mean` function of the underlying array.  If the array is
          a sub-class and `mean` does not have the kwarg `keepdims` this
          will raise a RuntimeError.
  
      Returns
      -------
      percentile : scalar or ndarray
          If `q` is a single percentile and `axis=None`, then the result
          is a scalar. If multiple percentiles are given, first axis of
          the result corresponds to the percentiles. The other axes are
          the axes that remain after the reduction of `a`. If the input
          contains integers or floats smaller than ``float64``, the output
          data-type is ``float64``. Otherwise, the output data-type is the
          same as that of the input. If `out` is specified, that array is
          returned instead.
  
      See Also
      --------
      nanmean
      nanmedian : equivalent to ``nanpercentile(..., 50)``
      percentile, median, mean
      nanquantile : equivalent to nanpercentile, but with q in the range [0, 1].
  
      Notes
      -----
      Given a vector ``V`` of length ``N``, the ``q``-th percentile of
      ``V`` is the value ``q/100`` of the way from the minimum to the
      maximum 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 the location of ``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[0][1] = np.nan
      >>> a
      array([[ 10.,  nan,   4.],
            [  3.,   2.,   1.]])
      >>> np.percentile(a, 50)
      nan
      >>> np.nanpercentile(a, 50)
      3.5
      >>> np.nanpercentile(a, 50, axis=0)
      array([ 6.5,  2.,   2.5])
      >>> np.nanpercentile(a, 50, axis=1, keepdims=True)
      array([[ 7.],
             [ 2.]])
      >>> m = np.nanpercentile(a, 50, axis=0)
      >>> out = np.zeros_like(m)
      >>> np.nanpercentile(a, 50, axis=0, out=out)
      array([ 6.5,  2.,   2.5])
      >>> m
      array([ 6.5,  2. ,  2.5])
  
      >>> b = a.copy()
      >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True)
      array([  7.,  2.])
      >>> assert not np.all(a==b)
  
      """
      a = np.asanyarray(a)
      q = np.true_divide(q, 100.0)  # handles the asarray for us too
      if not function_base._quantile_is_valid(q):
          raise ValueError("Percentiles must be in the range [0, 100]")
      return _nanquantile_unchecked(
          a, q, axis, out, overwrite_input, interpolation, keepdims
      )
  
imports: from __future__ import division, absolute_import, print_function
  import functools
  import warnings
  import numpy
  from numpy.lib import function_base
  from numpy.core import overrides
label_desc: Use image node_id 29 for calling a global function with example usage: nanpercentile(a, q, axis, out, overwrite_input, interpolation, keepdims) and returns: _nanquantile_unchecked