Element-wise broadcasting for comparing two NumPy arrays?

Question

Let\'s say I have an array like this:

import numpy as np

base_array = np.array([-13, -9, -11, -3, -3, -4,   2,  2,
                         2,  5,   7,  7,          


        

Answer 1

Here's one with np.searchsorted with focus on memory efficiency and hence performance -

def get_comparative_sum(base_array, comparison_array):
    n = len(base_array)
    base_array_sorted = np.sort(base_array)
    idx = np.searchsorted(base_array_sorted, comparison_array, 'right')
    idx[idx==n] = n-1
    return n - idx - (base_array_sorted[idx] == comparison_array)

Timings -

In [40]: np.random.seed(0)
    ...: base_array = np.random.randint(-1000,1000,(10000))
    ...: comparison_array = np.random.randint(-1000,1000,(20000))

# @miradulo's soln
In [41]: %timeit np.sum(comparison_array[:, None] < base_array, axis=1)
1 loop, best of 3: 386 ms per loop

In [42]: %timeit get_comparative_sum(base_array, comparison_array)
100 loops, best of 3: 2.36 ms per loop