NumPy | meshgrid method
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Numpy's meshgrid(~) method returns a grid that is useful in plotting contour plots of 3D graphs. Since it is difficult to explain in words what this method does, consult the examples below.
Parameters
1. x1 | array-like
The input array to make a grid out of.
2. x2 | array-like | optional
If you want a 2D grid, then this is required.
* Note that you could add in more arrays (i.e. x3, x4,...) if desired.
3. sparse | boolean | optional
Whether to return a sparse grid. Set this to True if you're dealing with large volumes of data that cannot be fit into memory. By default, sparse=False.
4. copy | boolean | optional
If True, then a new Numpy array is created and returned. If False, then view is returned so as to save memory. By default, copy=True.
Return value
If only x1 is specified, then a Numpy array is returned. Otherwise, a tuple of Numpy arrays will be returned.
Examples
Visualising the mesh grid
Let us create a 2D mesh grid:
x = [1,2,3,4]y = [5,6,7,8]xx, yy = np.meshgrid(x, y)
Let's unveil the content of the returned arrays:
print(xx)
[[1 2 3 4] [1 2 3 4] [1 2 3 4] [1 2 3 4]]
Here's yy:
print(yy)
[[5 5 5 5] [6 6 6 6] [7 7 7 7] [8 8 8 8]]
Using Matplotlib, we can graph them like so:
plt.scatter(xx, yy)plt.show()
This gives us the following:
Drawing a contour plot
Let's now talk about the practical benefits of having these data-points. They come in handy when we draw contour plots of 3D functions.
Here's an example:
def f(x, y): return x**2 + y **2
We can draw the contour like so:
x = np.linspace(-5, 5, 100)y = np.linspace(-5, 5, 100)xx, yy = np.meshgrid(x, y)zz = f(xx, yy)plt.contour(xx, yy, zz)plt.show()
This gives us the following contour plot:
