[Python] What is the most efficient way to convert numpy arrays to Shapely Points?

I have a function that outputs a grid of points as x and y numpy arrays for interpolation, but before I interpolate, I want to use Geopandas to perform an intersection with my research boundary (otherwise half of my interpolation points fall in the ocean).

I'm generating points like this:

import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry import Point

x = np.linspace(0,100,100)
y = np.linspace(0,100,100)
x, y = np.meshgrid(x, y)
x, y = x.flatten(), y.flatten()


f, ax = plt.subplots()

plt.scatter(x, y)
plt.axis('equal')
plt.show()


Is there an efficient way to convert these numpy arrays to shapely.Point([x, y]) so they can be placed in a geopandas geodataframe?

This is my current approach:

interp_points = []
index = 0
y_list = yi.tolist()
for x in xi.tolist():
interp_points.append(Point(x,y_list[index]))
index += 1


But it seems like converting to lists and then iterating is likely not a good approach for performance, and I have approximately 160,000 points.

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