How better perform Pearson R from 2 arrays of dimensions (m, n) and (n), returning an array of (m) size? [Python, NumPy, SciPy]

Question

I'm trying to improve a simple algorithm to obtaining the Pearson correlation coefficient from two arrays, X(m, n) and Y(n), returning me another array R of dimension (m).
In the case, I want to know the behavior each row of X regarding the values of Y. A sample (working) code is presented below:

import numpy as np
from scipy.stats import pearsonr

np.random.seed(1)
m, n = 10, 5

x = 100*np.random.rand(m, n)
y = 2 + 2*x.mean(0)
r = np.empty(m)

for i in range(m):
    r[i] = pearsonr(x[i], y)[0]

For this particular case, I get: r = array([0.95272843, -0.69134753, 0.36419159, 0.27467137, 0.76887201, 0.08823868, -0.72608421, -0.01224453, 0.58375626, 0.87442889])

For small values of m (near 10k) this runs pretty fast, but I'm starting to work with m ~ 30k, and so this is taking much longer than I expected. I'm aware I could implement multiprocessing/multi-threading but I believe there's a (better) pythonic way of doing this.

I tried to use use pearsonr(x, np.ones((m, n))*y), but it returns only (nan, nan).