How can I iterate over an 1D array and build a 2D array in Numpy?

Question

If I have an 1D numpy.ndarray b and a Python function f that I want to vectorize, this is very easy using the numpy.vectorize function:

c = numpy.vectorize(f)(a).

But if f returns a 1D numpy.ndarray instead of a scalar, how can I build a 2D numpy.ndarray instead? (That is, I want every 1D numpy.ndarray returned from f to become a row in the new 2D numpy.ndarray.)

Example:

def f(x):
    return x * x

a = numpy.array([1,2,3])
c = numpy.vectorize(f)(a)

def f_1d(x):
    return numpy.array([x, x])

a = numpy.ndarray([1,2,3])
d = ???(f_1d)(a)

In the above example c would become array([1, 4, 9]). What should ??? be replaced with if d should become array([[1, 1], [2, 2], [3, 3]])?

You could also use the Kronecker product to do this without using a user defined function at all: d=np.kron(np.ones((1,2),dtype=np.int), a.reshape((-1,1)) — talonmies
– talonmies, Commented Nov 18, 2012 at 7:49

diliop · Accepted Answer · 2012-11-18 06:16:40Z

1

Could do this instead:

def f_1d(x):
    return (x,x)
d = numpy.column_stack(numpy.vectorize(f_1d)(a))

will output:

array([[1, 1],
       [2, 2],
       [3, 3]])

answered Nov 18, 2012 at 6:16

diliop

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2 Comments

c00kiemonster Over a year ago

I tried that, but the numpy.vectorize(f_1d)(a) part throws ValueError: setting an array element with a sequence..

diliop Over a year ago

Replace your definition of f_1d with mine.

loopbackbee · Accepted Answer · 2012-11-18 19:44:24Z

0

I think you're looking for reshape and repeat

def f(x):
    return x * x
a = numpy.array([1,2,3])
b= numpy.vectorize(f)(a)
c = numpy.repeat(b.reshape( (-1,1) ),2, axis=1)
print c

output:

[[1 1]
 [4 4]
 [9 9]]

You can also just set the array.shape tuple directly. It may be worthwhile to know that you can accomplish the same as vectorize using map, if you ever need to write pure python. b= numpy.vectorize(f)(a) would become b=map(f,a)

Using this kind of approach, it becomes unnecessary to have your f_1d at all, since all it seems to do is duplicate information, which is done best by numpy.repeat.

Also, this version is a bit faster, but this only matters if you're dealing with large arrays.

edited Nov 18, 2012 at 19:44

answered Nov 18, 2012 at 5:33

loopbackbee

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2 Comments

c00kiemonster Over a year ago

Hmmm either my question wasn't clear enough, or there is something I don't understand. I just added an example to my question, please update your answer if possible.

loopbackbee Over a year ago

@c00kiemonster Indeed, I misunderstood your question slighly, your example makes it clearer. I've edited my answer

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