CODEWITHSUNDEEP
pythonarraysnumpydimensions
HM14
HM14

Reputation: 709

How to add a dimension to a numpy array in Python

I have an array that is size (214, 144). I need it to be (214,144,1) is there a way to do this easily in Python? Basically the dimensions are supposed to be (Days, Times, Stations). Since I only have 1 station's data that dimension would be a 1. However if I could also make the code flexible enough work for say 2 stations that would be great (e.g. changing the dimension size from (428,288) to (214,144,2)) that would be great!

Upvotes: 0

Views: 6410

Answers (2)

Tasos Papastylianou
Tasos Papastylianou

Reputation: 22215

1) To add a dimension to an array a of arbitrary dimensionality:

b = numpy.reshape (a, list (numpy.shape (a)) + [1])

Explanation:

You get the shape of a, turn it into a list, concatenate 1 to that list, and use that list as the new shape in a reshape operation.

2) To specify subdivisions of the dimensions, and have the size of the last dimension calculated automatically, use -1 for the size of the last dimension. e.g.:

b = numpy.reshape(a, [numpy.size(a,0)/2, numpy.size(a,1)/2, -1])

The shape of b in this case will be [214,144,4].

(obviously you could combine the two approaches if necessary):

b = numpy.reshape (a, numpy.append (numpy.array (numpy.shape (a))/2, -1))

Upvotes: 1

kennytm
kennytm

Reputation: 523174

You could use reshape:

>>> a = numpy.array([[1,2,3,4,5,6],[7,8,9,10,11,12]])
>>> a.shape
(2, 6)
>>> a.reshape((2, 6, 1))
array([[[ 1],
        [ 2],
        [ 3],
        [ 4],
        [ 5],
        [ 6]],

       [[ 7],
        [ 8],
        [ 9],
        [10],
        [11],
        [12]]])
>>> _.shape
(2, 6, 1)

Besides changing the shape from (x, y) to (x, y, 1), you could use (x, y/n, n) as well, but you may want to specify the column order depending on the input:

>>> a.reshape((2, 3, 2))
array([[[ 1,  2],
        [ 3,  4],
        [ 5,  6]],

       [[ 7,  8],
        [ 9, 10],
        [11, 12]]])
>>> a.reshape((2, 3, 2), order='F')
array([[[ 1,  4],
        [ 2,  5],
        [ 3,  6]],

       [[ 7, 10],
        [ 8, 11],
        [ 9, 12]]])

Upvotes: 5

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