Flattening a list of NumPy arrays?

You could use numpy.concatenate, which as the name suggests, basically concatenates all the elements of such an input list into a single NumPy array, like so -

import numpy as npout = np.concatenate(input_list).ravel()

If you wish the final output to be a list, you can extend the solution, like so -

out = np.concatenate(input_list).ravel().tolist()

Sample run -

In [24]: input_listOut[24]: [array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]])]In [25]: np.concatenate(input_list).ravel()Out[25]: array([ 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])

Convert to list -

In [26]: np.concatenate(input_list).ravel().tolist()Out[26]: [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]

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 perfplotperfplot.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.

Another simple approach would be to use numpy.hstack() followed by removing the singleton dimension using squeeze() as in:

In [61]: np.hstack(list_of_arrs).squeeze()Out[61]: array([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])