Is there a multi-dimensional version of arange/linspace in numpy?

Question

I would like a list of 2d NumPy arrays (x,y) , where each x is in {-5, -4.5, -4, -3.5, ..., 3.5, 4, 4.5, 5} and the same for y.

I could do

x = np.arange(-5, 5.1, 0.5) y = np.arange(-5, 5.1, 0.5) 

and then iterate through all possible pairs, but I'm sure there's a nicer way...

I would like something back that looks like:

[[-5, -5],  [-5, -4.5],  [-5, -4],  ...  [5, 5]] 

but the order does not matter.

Answer 1

You can use np.mgrid for this, it's often more convenient than np.meshgrid because it creates the arrays in one step:

import numpy as np X,Y = np.mgrid[-5:5.1:0.5, -5:5.1:0.5] 

For linspace-like functionality, replace the step (i.e. 0.5) with a complex number whose magnitude specifies the number of points you want in the series. Using this syntax, the same arrays as above are specified as:

X, Y = np.mgrid[-5:5:21j, -5:5:21j] 

---

You can then create your pairs as:

xy = np.vstack((X.flatten(), Y.flatten())).T 

As @ali_m suggested, this can all be done in one line:

xy = np.mgrid[-5:5.1:0.5, -5:5.1:0.5].reshape(2,-1).T 

Best of luck!