Python Numpy array creation from multiple lists

Question

I am learning more about numpy and need help creating an numpy array from multiple lists. Say I have 3 lists,

a = [1, 1, 1] 
b = [2, 2, 2] 
c = [3, 3, 3]

How can I create a new numpy array with each list as a column? Meaning that the new array would be [[1, 2, 3], [1, 2, 3], [1, 2, 3]]. I know how to do this by looping through the lists but I am not sure if there is an easier way to accomplish this. The numpy concatenate function seems to be close but I couldn't figure out how to get it to do what I'm after. Thanks

Longwen Ou · Accepted Answer · 2018-12-07 22:11:26Z

9

Try with np.column_stack:

d = np.column_stack([a, b, c])

answered Dec 7, 2018 at 22:11

Longwen Ou

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hpaulj · Accepted Answer · 2018-12-08 00:06:25Z

3

No need to use numpy. Python zip does a nice job:

In [606]: a = [1, 1, 1] 
     ...: b = [2, 2, 2] 
     ...: c = [3, 3, 3] 
In [607]: abc = list(zip(a,b,c))
In [608]: abc
Out[608]: [(1, 2, 3), (1, 2, 3), (1, 2, 3)]

But if your heart is set on using numpy, a good way is to make a 2d array, and transpose it:

In [609]: np.array((a,b,c))
Out[609]: 
array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])
In [610]: np.array((a,b,c)).T
Out[610]: 
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

Others show how to do this with stack and column_stack, but underlying these is a concatenate. In one way or other they turn the lists into 2d arrays that can be joined on axis=1, e.g.

In [616]: np.concatenate([np.array(x)[:,None] for x in [a,b,c]], axis=1)
Out[616]: 
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

answered Dec 8, 2018 at 0:06

hpaulj

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2 Comments

gargoylebident Over a year ago

Why double parenth?

hpaulj Over a year ago

@stackexchange_account1111, it could also be np.array([a,b,c])

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