过滤 numpy 数组以仅保留给定值的一行 [英] Filter numpy array to retain only one row for a given value

问题描述

我有一个很大的 n x 2 numpy 数组，它的格式为 (x, y) 坐标.我想过滤这个数组以便:

I have a large n x 2 numpy array that is formatted as (x, y) coordinates. I would like to filter this array so as to:

1. 识别具有重复 x 值的坐标对.
2. 仅保留具有最高 y 值的那些重复项的坐标对.

例如，在以下数组中:

arr = [[1, 4]
       [1, 8]
       [2, 3]
       [4, 6]
       [4, 2]
       [5, 1]
       [5, 2]
       [5, 6]]

我希望结果是:

arr = [[1, 8]
       [2, 3]
       [4, 6]
       [5, 6]]

我已经探索了 np.unique 和 np.where，但不知道如何利用它们来解决这个问题.非常感谢！

Ive explored np.unique and np.where but cannot figure out how to leverage them to solve this problem. Thanks so much!

推荐答案

这里有一种方法基于 np.maximum.reduceat -

Here's one way based on np.maximum.reduceat -

def grouby_maxY(a):
    b = a[a[:,0].argsort()] # if first col is already sorted, skip this
    grp_idx = np.flatnonzero(np.r_[True,(b[:-1,0] != b[1:,0])])
    grp_maxY = np.maximum.reduceat(b[:,1], grp_idx)
    return np.c_[b[grp_idx,0], grp_maxY]

或者，如果你想带np.unique，我们可以用np.unique(b[:,0],return_index=1)[1].

Alternatively, if you want to bring np.unique, we can use it to find grp_idx with np.unique(b[:,0], return_index=1)[1].

样品运行 -

In [453]: np.random.seed(0)

In [454]: arr = np.random.randint(0,5,(10,2))

In [455]: arr
Out[455]: 
array([[4, 0],
       [3, 3],
       [3, 1],
       [3, 2],
       [4, 0],
       [0, 4],
       [2, 1],
       [0, 1],
       [1, 0],
       [1, 4]])

In [456]: grouby_maxY(arr)
Out[456]: 
array([[0, 4],
       [1, 4],
       [2, 1],
       [3, 3],
       [4, 0]])

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