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(s) of the array elements.
Parameters: 


Returns: 

See also
median
percentile(..., 50)
quantile
Given a vector V
of length N
, the qth 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
.
>>> 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.]) >>> np.percentile(a, 50, axis=1, keepdims=True) array([[7.], [2.]])
>>> m = np.percentile(a, 50, axis=0) >>> out = np.zeros_like(m) >>> np.percentile(a, 50, axis=0, out=out) 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)
The different types of interpolation can be visualized graphically:
import matplotlib.pyplot as plt a = np.arange(4) p = np.linspace(0, 100, 6001) ax = plt.gca() lines = [ ('linear', None), ('higher', ''), ('lower', ''), ('nearest', '.'), ('midpoint', '.'), ] for interpolation, style in lines: ax.plot( p, np.percentile(a, p, interpolation=interpolation), label=interpolation, linestyle=style) ax.set( title='Interpolation methods for list: ' + str(a), xlabel='Percentile', ylabel='List item returned', yticks=a) ax.legend() plt.show()
© 2005–2019 NumPy Developers
Licensed under the 3clause BSD License.
https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.percentile.html