parameters: (a, q, axis=None, out=None, overwrite_input=None, interpolation='linear', keepdims=False)
return_statement: return _api_internal.percentile(a, q * 100, axis, interpolation, keepdims, out)
docstring: Compute the q-th quantile of the data along the specified axis.
      New in version 1.15.0.
      Parameters
      ----------
      a : ndarray
          Input array or object that can be converted to an array.
      q : ndarray
          Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.
      axis : {int, tuple of int, None}, optional
          Axis or axes along which the quantiles are computed.
          The default is to compute the quantile(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.
      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 < 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.
      Returns
      -------
      quantile : ndarray
          If q is a single quantile and axis=None, then the result is a scalar.
          If multiple quantiles are given, first axis of the result corresponds to the quantiles.
          The other axes are the axes that remain after the reduction of a.
          If out is specified, that array is returned instead.
      See also
      --------
      mean
      Notes
      -----
      Given a vector V of length N, the q-th quantile of V is the value q 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 quantile if the normalized ranking
      does not match the location of q exactly. This function is the same as the median if q=0.5,
      the same as the minimum if q=0.0 and the same as the maximum if q=1.0.
      This function differs from the original `numpy.quantile
      <https://numpy.org/devdocs/reference/generated/numpy.quantile.html>`_ in
      the following aspects:
      - q must be ndarray type even if it is a scalar
      - do not support overwrite_input
      Examples
      --------
      >>> a = np.array([[10, 7, 4], [3, 2, 1]])
      >>> a
      array([[10., 7., 4.],
             [3., 2., 1.]])
      >>> q = np.array(0.5)
      >>> q
      array(0.5)
      >>> np.quantile(a, q)
      array(3.5)
      >>> np.quantile(a, q, axis=0)
      array([6.5, 4.5, 2.5])
      >>> np.quantile(a, q, axis=1)
      array([7., 2.])
      >>> np.quantile(a, q, axis=1, keepdims=True)
      array([[7.],
             [2.]])
      >>> m = np.quantile(a, q, axis=0)
      >>> out = np.zeros_like(m)
      >>> np.quantile(a, q, axis=0, out=out)
      array([6.5, 4.5, 2.5])
      >>> out
      array([6.5, 4.5, 2.5])
docstring_summary: Compute the q-th quantile of the data along the specified axis.
      New in version 1.15.0.
      Parameters
      ----------
      a : ndarray
          Input array or object that can be converted to an array.
      q : ndarray
          Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.
      axis : {int, tuple of int, None}, optional
          Axis or axes along which the quantiles are computed.
          The default is to compute the quantile(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.
      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 < 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.
      Returns
      -------
      quantile : ndarray
          If q is a single quantile and axis=None, then the result is a scalar.
          If multiple quantiles are given, first axis of the result corresponds to the quantiles.
          The other axes are the axes that remain after the reduction of a.
          If out is specified, that array is returned instead.
      See also
      --------
      mean
      Notes
      -----
      Given a vector V of length N, the q-th quantile of V is the value q 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 quantile if the normalized ranking
      does not match the location of q exactly. This function is the same as the median if q=0.5,
      the same as the minimum if q=0.0 and the same as the maximum if q=1.0.
      This function differs from the original `numpy.quantile
      <https://numpy.org/devdocs/reference/generated/numpy.quantile.html>`_ in
      the following aspects:
      - q must be ndarray type even if it is a scalar
      - do not support overwrite_input
      Examples
      --------
      >>> a = np.array([[10, 7, 4], [3, 2, 1]])
      >>> a
      array([[10., 7., 4.],
             [3., 2., 1.]])
      >>> q = np.array(0.5)
      >>> q
      array(0.5)
      >>> np.quantile(a, q)
      array(3.5)
      >>> np.quantile(a, q, axis=0)
      array([6.5, 4.5, 2.5])
      >>> np.quantile(a, q, axis=1)
      array([7., 2.])
      >>> np.quantile(a, q, axis=1, keepdims=True)
      array([[7.],
             [2.]])
      >>> m = np.quantile(a, q, axis=0)
      >>> out = np.zeros_like(m)
      >>> np.quantile(a, q, axis=0, out=out)
      array([6.5, 4.5, 2.5])
      >>> out
      array([6.5, 4.5, 2.5])
docstring_tokens: Compute the q - th quantile of the data along the specified axis . New in version 1 . 15 . 0 . Parameters ---------- a : ndarray Input array or object that can be converted to an array . q : ndarray Quantile or sequence of quantiles to compute which must be between 0 and 1 inclusive . axis : { int tuple of int None } optional Axis or axes along which the quantiles are computed . The default is to compute the quantile ( 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 . 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 < 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 . Returns ------- quantile : ndarray If q is a single quantile and axis = None then the result is a scalar . If multiple quantiles are given first axis of the result corresponds to the quantiles . The other axes are the axes that remain after the reduction of a . If out is specified that array is returned instead . See also -------- mean Notes ----- Given a vector V of length N the q - th quantile of V is the value q 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 quantile if the normalized ranking does not match the location of q exactly . This function is the same as the median if q = 0 . 5 the same as the minimum if q = 0 . 0 and the same as the maximum if q = 1 . 0 . This function differs from the original numpy . quantile <https : // numpy . org / devdocs / reference / generated / numpy . quantile . html > _ in the following aspects : - q must be ndarray type even if it is a scalar - do not support overwrite_input Examples -------- >>> a = np . array ( [[ 10 7 4 ] [ 3 2 1 ]] ) >>> a array ( [[ 10 . 7 . 4 . ] [ 3 . 2 . 1 . ]] ) >>> q = np . array ( 0 . 5 ) >>> q array ( 0 . 5 ) >>> np . quantile ( a q ) array ( 3 . 5 ) >>> np . quantile ( a q axis = 0 ) array ( [ 6 . 5 4 . 5 2 . 5 ] ) >>> np . quantile ( a q axis = 1 ) array ( [ 7 . 2 . ] ) >>> np . quantile ( a q axis = 1 keepdims = True ) array ( [[ 7 . ] [ 2 . ]] ) >>> m = np . quantile ( a q axis = 0 ) >>> out = np . zeros_like ( m ) >>> np . quantile ( a q axis = 0 out = out ) array ( [ 6 . 5 4 . 5 2 . 5 ] ) >>> out array ( [ 6 . 5 4 . 5 2 . 5 ] )
function: def quantile(a, q, axis=None, out=None, overwrite_input=None, interpolation='linear', keepdims=False): # pylint: disable=too-many-arguments
      """
      Compute the q-th quantile of the data along the specified axis.
      New in version 1.15.0.
      Parameters
      ----------
      a : ndarray
          Input array or object that can be converted to an array.
      q : ndarray
          Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.
      axis : {int, tuple of int, None}, optional
          Axis or axes along which the quantiles are computed.
          The default is to compute the quantile(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.
      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 < 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.
      Returns
      -------
      quantile : ndarray
          If q is a single quantile and axis=None, then the result is a scalar.
          If multiple quantiles are given, first axis of the result corresponds to the quantiles.
          The other axes are the axes that remain after the reduction of a.
          If out is specified, that array is returned instead.
      See also
      --------
      mean
      Notes
      -----
      Given a vector V of length N, the q-th quantile of V is the value q 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 quantile if the normalized ranking
      does not match the location of q exactly. This function is the same as the median if q=0.5,
      the same as the minimum if q=0.0 and the same as the maximum if q=1.0.
      This function differs from the original `numpy.quantile
      <https://numpy.org/devdocs/reference/generated/numpy.quantile.html>`_ in
      the following aspects:
      - q must be ndarray type even if it is a scalar
      - do not support overwrite_input
      Examples
      --------
      >>> a = np.array([[10, 7, 4], [3, 2, 1]])
      >>> a
      array([[10., 7., 4.],
             [3., 2., 1.]])
      >>> q = np.array(0.5)
      >>> q
      array(0.5)
      >>> np.quantile(a, q)
      array(3.5)
      >>> np.quantile(a, q, axis=0)
      array([6.5, 4.5, 2.5])
      >>> np.quantile(a, q, axis=1)
      array([7., 2.])
      >>> np.quantile(a, q, axis=1, keepdims=True)
      array([[7.],
             [2.]])
      >>> m = np.quantile(a, q, axis=0)
      >>> out = np.zeros_like(m)
      >>> np.quantile(a, q, axis=0, out=out)
      array([6.5, 4.5, 2.5])
      >>> out
      array([6.5, 4.5, 2.5])
      """
      if overwrite_input is not None:
          raise NotImplementedError('overwrite_input is not supported yet')
      return _api_internal.percentile(a, q * 100, axis, interpolation, keepdims, out)
function_tokens: def quantile ( a , q , axis = None , out = None , overwrite_input = None , interpolation = 'linear' , keepdims = False ) : # pylint: disable=too-many-arguments if overwrite_input is not None : raise NotImplementedError ( 'overwrite_input is not supported yet' ) return _api_internal . percentile ( a , q * 100 , axis , interpolation , keepdims , out )