Speed up interpolation in Python

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Python3.6

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. I have been using another program where the same plot happens in 6 - 7 seconds. Also, I know that the plot function of that program uses Python in the background

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

In future I will be handling even bigger data (8 to 10 times the size of current one), so I am in need to finding something robust, which handles all the data, without going into memory overflow

What I have tried:

import numpy as np
import matplotlib.pyplot as plt
import time
from scipy.interpolate import griddata
import os

arr_len = 932826
x = np.random.uniform(low=0, high=4496, size=arr_len)
y = np.random.uniform(low=-74, high=492, size=arr_len)
z = np.random.uniform(low=-30, high=97, size=arr_len)

x_linspace = 10
y_linspace = 10
x_lab = 'x_label'
y_lab = 'y_label'
title = 'some title'
plot_name = 'example plot'

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)