Is there a way to copy array.array (not list) in Python, Instead of creating a new list and copying the values, do you use .to_something and .from_something? I can’t seem to find anything in the docs. If not, is there a similar built-in data type that can do that?

I’m working on a high-performance module, so the sooner the answer, the better.

My current solution is just using .to_bytes and .from_bytes, which is about 1.8x faster than my tests.

Not sure what your array.array contains, but use example:

>>> import array
>>> a = array.array('i', [1, 2, 3] * 1000)
array('i', [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,
2, 3, 1, 2, 3, 1, 2, 3, 1, 2, ... ])

Some settings:

>>> from copy import deepcopy
>>> import numpy as np

Various methods of timing

(Use %timeit magic in Jupyter Notebook

):

Slice

In [1]: %timeit cp = a[:]

418 ns ± 4.89 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Deep copy

In [2]: %timeit cp = deepcopy(a)

1.83 µs ± 34 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

numpy copy … Note: This generates a numpy array instead of array.array

In [3]: %timeit cp = np.copy(a)

1.87 µs ± 62.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

List understanding and array.array conversion

In [4]: %timeit cp = array.array('i', [item for item in a])

147 µs ± 5.39 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

numpy replication and array.array conversion

In [5]: %timeit cp = array.array('i', np.copy(a))

310 µs ± 2.25 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Copy to an existing array

In[6]: pre = array.array('i', [0, 0, 0] * 1000)
In[7]: %timeit for i, element in enumerate(a): pre[i] = a[i]

344 µs ± 7.83 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)