numpy:数组中唯一值的最有效频率计数 [英] numpy: most efficient frequency counts for unique values in an array

问题描述

在numpy/scipy中，是否有一种有效的方法来获得数组中唯一值的频率计数?

In numpy / scipy, is there an efficient way to get frequency counts for unique values in an array?

大致如下:

x = array( [1,1,1,2,2,2,5,25,1,1] )
y = freq_count( x )
print y

>> [[1, 5], [2,3], [5,1], [25,1]]

(对于你，R 用户，我基本上是在寻找 table() 函数)

( For you, R users out there, I'm basically looking for the table() function )

推荐答案

看一看np.bincount:

http://docs.scipy.org/doc/numpy/reference/generated/numpy.bincount.html

import numpy as np
x = np.array([1,1,1,2,2,2,5,25,1,1])
y = np.bincount(x)
ii = np.nonzero(y)[0]

然后:

zip(ii,y[ii]) 
# [(1, 5), (2, 3), (5, 1), (25, 1)]

或:

np.vstack((ii,y[ii])).T
# array([[ 1,  5],
         [ 2,  3],
         [ 5,  1],
         [25,  1]])

或者您想结合计数和唯一值.

or however you want to combine the counts and the unique values.

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