Flattening a list of NumPy arrays?

Question

It appears that I have data in the format of a list of NumPy arrays (type() = np.ndarray):

[array([[ 0.00353654]]), array([[ 0.00353654]]), arra         


        

Answer 1

Can also be done by

np.array(list_of_arrays).flatten().tolist()

resulting in

[0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654]

---

Update

As @aydow points out in the comments, using numpy.ndarray.ravel can be faster if one doesn't care about getting a copy or a view

np.array(list_of_arrays).ravel()

Although, according to docs

When a view is desired in as many cases as possible, arr.reshape(-1) may be preferable.

In other words

np.array(list_of_arrays).reshape(-1)

The initial suggestion of mine was to use numpy.ndarray.flatten that returns a copy every time which affects performance.

Let's now see how the time complexity of the above-listed solutions compares using perfplot package for a setup similar to the one of the OP

import perfplot

perfplot.show(
    setup=lambda n: np.random.rand(n, 2),
    kernels=[lambda a: a.ravel(),
             lambda a: a.flatten(),
             lambda a: a.reshape(-1)],
    labels=['ravel', 'flatten', 'reshape'],
    n_range=[2**k for k in range(16)],
    xlabel='N')

Here flatten demonstrates piecewise linear complexity which can be reasonably explained by it making a copy of the initial array compare to constant complexities of ravel and reshape that return a view.

It's also worth noting that, quite predictably, converting the outputs .tolist() evens out the performance of all three to equally linear.