numpy.quantile¶

numpy.
quantile
(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)[source]¶ Compute the qth quantile of the data along the specified axis.
New in version 1.15.0.
Parameters:  a : array_like
Input array or object that can be converted to an array.
 q : array_like of float
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.
 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 quantile lies between two data points
i < j
: linear:
i + (j  i) * fraction
, wherefraction
is the fractional part of the index surrounded byi
andj
.  lower:
i
.  higher:
j
.  nearest:
i
orj
, whichever is nearest.  midpoint:
(i + j) / 2
.
 linear:
 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 : scalar or 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 the input contains integers or floats smaller than
float64
, the output datatype isfloat64
. Otherwise, the output datatype is the same as that of the input. If out is specified, that array is returned instead.
See also
percentile
 equivalent to quantile, but with q in the range [0, 100].
median
 equivalent to
quantile(..., 0.5)
Notes
Given a vector
V
of lengthN
, the qth quantile ofV
is the valueq
of the way from the minimum to the maximum in a sorted copy ofV
. 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 ofq
exactly. This function is the same as the median ifq=0.5
, the same as the minimum ifq=0.0
and the same as the maximum ifq=1.0
.Examples
>>> a = np.array([[10, 7, 4], [3, 2, 1]]) >>> a array([[10, 7, 4], [ 3, 2, 1]]) >>> np.quantile(a, 0.5) 3.5 >>> np.quantile(a, 0.5, axis=0) array([6.5, 4.5, 2.5]) >>> np.quantile(a, 0.5, axis=1) array([7., 2.]) >>> np.quantile(a, 0.5, axis=1, keepdims=True) array([[7.], [2.]]) >>> m = np.quantile(a, 0.5, axis=0) >>> out = np.zeros_like(m) >>> np.quantile(a, 0.5, axis=0, out=out) array([6.5, 4.5, 2.5]) >>> m array([6.5, 4.5, 2.5]) >>> b = a.copy() >>> np.quantile(b, 0.5, axis=1, overwrite_input=True) array([7., 2.]) >>> assert not np.all(a == b)