- numpy.percentile(a, q, axis=None, out=None, overwrite_input=False)¶
Compute the qth percentile of the data along the specified axis.
Returns the qth percentile of the array elements.
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, optional
Axis along which the percentiles are computed. The default (None) is to compute the median 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 use of memory of input array a for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if overwrite_input is True and the input is not already an array, an error will be raised.
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.
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) 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) 3.5