计算numpy数组各列平均增长率的最快方法

Question

Given an array such as:

import numpy as np
a = np.array([[1,2,3,4,5],[6,7,8,9,10]])

What's the quickest way to calculate the growth rates of each row so that my results would be 0.52083333333333326 , and 0.13640873015873009 respectively.

I tried using:

>>> np.nanmean(np.rate(1,0,-a[:-1],a[1:]), axis=0)
array([ 5.        ,  2.5       ,  1.66666667,  1.25      ,  1.        ])

but of course it doesn't yield the right result and I don't know how to get the axis right for the numpy.rate function.

Answer 1

In [262]: a = np.array([[1,2,3,4,5],[6,7,8,9,10]]).astype(float)
In [263]: np.nanmean((a[:, 1:]/a[:, :-1]), axis=1) - 1
Out[263]: array([ 0.52083333,  0.13640873])

Answer 2

To take your approach using numpy.rate , you need to index into your a array properly (consider all rows separately) and use axis=1 :

In [6]: np.nanmean(np.rate(1,0,-a[:,:-1],a[:,1:]), axis=1)
Out[6]: array([ 0.52083333,  0.13640873])