Fill array combining segmented limits and number of elements stored in a separated array

I need to improve as much as possible the performance of the function defined below. It is called millions of times, and the for loop is currently the bottleneck of my code.

The function takes ranges defined in x and a number of elements inside each range defined in y. If the number of elements N in a range is < 1, combine it with adjacent upper segments until N>1. When this condition is fulfilled, generate a random number of N elements within that range, and store the result in the final array d.

# Sample data. Changes with every call to func().
x = np.arange(0.11, 1000., 0.1)
y = [1881.44972, 719.75073, 372.08464, 241.89332, 175.18145, 121.59943, 83.14836, 60.02836, 45.13496, 35.02594, 27.87664, 22.6501, 18.72337, 15.70481, 13.33868, 11.45254, 9.92687, 8.67683, 7.6409, 6.77366, 6.04099, 5.41692, 4.88137, 4.41865, 4.01638, 3.66464, 3.35548, 3.08242, 2.84015, 2.6243, 2.43123, 2.2579, 2.10178, 1.96069, 1.8328, 1.71655, 1.6106, 1.51378, 1.42511, 1.3437, 1.2688, 1.19975, 1.13596, 1.07693, 1.02221, 0.97139, 0.92411, 0.88007, 0.83898, 0.80059, 0.76467, 0.73102, 0.69945, 0.6698, 0.64193, 0.61568, 0.59095, 0.56762, 0.54559, 0.52477, 0.50507, 0.48641, 0.46873, 0.45195, 0.43602, 0.42089, 0.40649, 0.3928, 0.37975, 0.36732, 0.35547, 0.34415, 0.33335, 0.32302, 0.31315, 0.30371, 0.29467, 0.28601, 0.27771, 0.26975, 0.26211, 0.25479, 0.24775, 0.24099, 0.23448, 0.22823, 0.22222, 0.21643, 0.21085, 0.20548, 0.2003, 0.1953, 0.19049, 0.18584, 0.18135, 0.17702, 0.17283, 0.16879, 0.16488, 0.16109, 0.15744, 0.15389, 0.15047, 0.14715, 0.14393, 0.14082, 0.1378, 0.13487, 0.13203, 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def func(x, y):
    d, x_low, N_add = [], 0.01, 0.
    # For each upper x limit
    for x_up, N in zip(*[x, y]):
        # Number of elements in this range
        N += N_add
        # If the number of elements is < 1., combine with the next segment
        if N < 1.:
            N_add = N
        else:
            # Generate N random elements in this range
            d.extend(np.random.uniform(x_low, x_up, int(round(N))))
            # Reset the number of elements and move the lower x limit.
            N_add, x_low = 0., x_up

    return np.asarray(d)