Question

Im working with two arrays, trying to work with them like a 2 dimensional array. I’m using a lot of vectorized calculations with NumPy. Any idea how I would populate an array like this:

X = [1, 2, 3, 1, 2, 3, 1, 2, 3]

or:

X = [0.2, 0.4, 0.6, 0.8, 0.2, 0.4, 0.6, 0.8, 0.2, 0.4, 0.6, 0.8, 0.2, 0.4, 0.6, 0.8]

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Ignore the first part of the message.

I had to populate two arrays in a form of a grid. But the grid dimensions varied from the users, thats why I needed a general form. I worked on it all this morning and finally got what I wanted.

I apologize if I caused any confusion earlier. English is not my tongue language, and sometimes it is hard for me to explain things.

This is the code that did the job for me:

    myIter = linspace(1, N, N)
    for x in myIter:
        for y in myIter:
            index = ((x - 1)*N + y) - 1
            X[index] = x / (N+1)
            Y[index] = y / (N+1)

The user inputs N.
And the length of X, Y is N*N.

Answer 1

You can use the function tile. From the examples:

>>> a = np.array([0, 1, 2])
>>> np.tile(a, 2)
array([0, 1, 2, 0, 1, 2])

With this function, you can also reshape your array at once like they do in the other answers with reshape (by defining the ‘repeats’ is more dimensions):

>>> np.tile(a, (2, 1))
array([[0, 1, 2],
       [0, 1, 2]])

Addition: and a little comparison of the difference in speed between the built in function tile and the multiplication:

In [3]: %timeit numpy.array([1, 2, 3]* 3)
100000 loops, best of 3: 16.3 us per loop
In [4]: %timeit numpy.tile(numpy.array([1, 2, 3]), 3)
10000 loops, best of 3: 37 us per loop

In [5]: %timeit numpy.array([1, 2, 3]* 1000)
1000 loops, best of 3: 1.85 ms per loop
In [6]: %timeit numpy.tile(numpy.array([1, 2, 3]), 1000)
10000 loops, best of 3: 122 us per loop 

EDIT

The output of the code you gave in your question can also be achieved as following:

arr = myIter / (N + 1)
X = numpy.repeat(arr, N)
Y = numpy.tile(arr, N)

This way you can avoid looping the arrays (which is one of the great advantages of using numpy). The resulting code is simpler (if you know the functions of course, see the documentation for repeat and tile) and faster.