numpy.percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)[source]
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 interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’} This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points
New in version 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 array New in version 1.9.0. |
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Returns: |
percentile : scalar or ndarray If a single percentile |
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
.
>>> 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])
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https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.percentile.html