Speed up random weighted choice without replacement in python

I want to sample ~10⁷ times from a population of ~10⁷ integers without replacements and with weights, each time picking 10 elements. After each sampling I change the weights. I have timed two approaches (python3 and numpy) in the following script. Both approaches seem painfully slow to me, do you see a way of speeding it up?

import numpy as np
import random

@profile
def test_choices():
    population = list(range(10**7))
    weights = np.random.uniform(size=10**7)
    np_weights = np.array(weights)

    def numpy_choice():
        np_w = np_weights / sum(np_weights)
        c = np.random.choice(population, size=10, replace=False, p=np_w)

    def python_choice():
        c = []
        while len(c) < 10:
            c += random.choices(population=population, weights=weights, k=10 - len(c))
            c = list(set(c))

    for i in range(10**1):

        numpy_choice()
        python_choice()

        add_weight = np.random.uniform()
        random_element = random.randint(0, 10**7)
        weights[random_element] += add_weight
        np_weights[random_element] += add_weight


test_choices()

With the timer result:

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    24        10   20720062.0 2072006.2     56.6          numpy_choice()
    25        10   15593925.0 1559392.5     42.6          python_choice()