Taaam Taaam - 1 year ago 707
Python Question

Numpy: get 1D array as 2D array without reshape

I have need for hstacking multple arrays with with the same number of rows (although the number of rows is variable between uses) but different number of columns. However some of the arrays only have one column, eg.

array = np.array([1,2,3,4,5])

which gives

#array.shape = (5,)

but I'd like to have the shape recognized as a 2d array, eg.

#array.shape = (5,1)

So that hstack can actually combine them.
My current solution is:

array = np.atleast_2d([1,2,3,4,5]).T
#array.shape = (5,1)

So I was wondering, is there a better way to do this? Would

array = np.array([1,2,3,4,5]).reshape(len([1,2,3,4,5]), 1)

be better?
Note that my use of [1,2,3,4,5] is just a toy list to make the example concrete. In practice it will be a much larger list passed into a function as an argument. Thanks!

Answer Source

Check the code of hstack and vstack. One, or both of those, pass the arguments through atleast_nd. That is a perfectly acceptable way of reshaping an array.

Some other ways:

arr = np.array([1,2,3,4,5]).reshape(-1,1)  # saves the use of len()
arr = np.array([1,2,3,4,5])[:,None]  # adds a new dim at end
np.array([1,2,3],ndmin=2).T  # used by column_stack

hstack and vstack transform their inputs with:

arrs = [atleast_1d(_m) for _m in tup]
[atleast_2d(_m) for _m in tup]

test data:




array([[0, 0, 1, 2, 3, 4, 0, 1, 2, 3],
       [1, 5, 6, 7, 8, 9, 4, 5, 6, 7]])

If you don't know ahead of time which arrays are 1d, then column_stack is easiest to use. The others require a little function that tests for dimensionality before applying the reshaping.

Numpy: use reshape or newaxis to add dimensions