# [SOLVED] Merging 1D arrays into a 2D array

## Issue

Is there a built-in function to join two 1D arrays into a 2D array?
Consider an example:

``````X=np.array([1,2])
y=np.array([3,4])
result=np.array([[1,3],[2,4]])
``````

I can think of 2 simple solutions.
The first one is pretty straightforward.

``````np.transpose([X,y])
``````

The other one employs a lambda function.

``````np.array(list(map(lambda i: [a[i],b[i]], range(len(X)))))
``````

While the second one looks more complex, it seems to be almost twice as fast as the first one.

Edit
A third solution involves the zip() function.

``````np.array(list(zip(X, y)))
``````

It’s faster than the lambda function but slower than column_stack solution suggested by @Divakar.

``````np.column_stack((X,y))
``````

## Solution

Take into consideration scalability. If we increase the size of the arrays, complete numpy solutions are quite faster than solutions involving python built-in operations:

``````np.random.seed(1234)
X = np.random.rand(10000)
y = np.random.rand(10000)

%timeit np.array(list(map(lambda i: [X[i],y[i]], range(len(X)))))
6.64 ms ± 32.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit np.array(list(zip(X, y)))
4.53 ms ± 33.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit np.column_stack((X,y))
19.2 µs ± 30.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit np.transpose([X,y])
16.2 µs ± 247 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit np.vstack((X, y)).T
14.2 µs ± 94.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
``````

Taking into account all proposed solutions, `np.vstack(X,y).T` is the fastest when working with greater array sizes.