MATCH python 中的最小值等效代码

Question

I would like to seek the quickest equivalent Python code of the MATCH Excel function which returns the position of the smallest and nearest value of "VALUE" in a "RANGE", ie Match(VALUE, RANGE, -1). This function should be applied to multiple "VALUE", ie, a vector.

I have an initial solution below but it is very very slow with a high number of element (100k):

In the example below I want to match each value in "vector" to find the position of its smallest nearest value in the "vector_to_match":

import numpy as np
import perfplot


simulLength = 100000

vector = np.random.rand(simulLength)
vector_to_match = np.arange(100000)/100000


def Match_Smallest(x):
    orderCheck = np.array((vector_to_match < x) * 1)
    x_order = sum(orderCheck) - 1
    return x_order

def A_Finding(x):
    return np.array(list(map(Match_Smallest, x)))


# what I want to get :
vector_position_outout = A_Finding(vector)

# but Match_Smallest(x) is really too slow

It takes about more than a minute for me to get the output vector A_Finding(vector). It would like to see if there is any quicker way to do it because Excel was beating me on my way in speed.

perfplot.show(
    setup=lambda n: np.random.rand(n),  # or setup=np.random.rand
    kernels=[
        A_Finding
    ],
    labels=["c_"],
    n_range=[10 ** k for k in range(5)],
    xlabel="len(a)",
)

Answer 1

Use broadcasting:

out = np.sum(np.array((vector_to_match < vector[:, None]) * 1), axis=1) - 1

Or like mentioned @donkopotamus in comments - use searchsorted :

out = vector_to_match.searchsorted(vector) - 1