scipy.interpolate.gridata speed issues

I am trying to plot heatmap with three variables x, y and z, each with vector length of 932826. The time taken with the function below is 28 seconds, with a bin size of 10 on each x and y

When debugging I see that the maximum time consumed is while using the griddata function. So, I was wondering if there are any alternatives that I can use instead of griddata to make the interpolation quicker

 import numpy as np
 import matplotlib.pyplot as plt
 import time
 from scipy.interpolate import griddata
 import os
 
 x = np.loadtxt(savepath + '\\' + 'x.csv', delimiter=',')
 y = np.loadtxt(savepath + '\\' + 'y.csv', delimiter=',')
 z = np.loadtxt(savepath + '\\' + 'z.csv', delimiter=',')
 
 x_linspace = 10
 y_linspace = 10
 
 xi = np.linspace(min(x), max(x), x_linspace)
 yi = np.linspace(min(y), max(y), y_linspace)
 start = time.time()
 zi = griddata((x, y), z, (xi[None, :], yi[:, None]), method='linear')
 print('Time elapsed = ', time.time()-start)

Inputs.zip