Converting multidimensional array into arrays in list for every row

Question

I have a data set like this,

data = np.array([[ 5, 31, 61],
                 [ 10, 31, 67],
                 [ 15, 31, 69],
                 [ 4, 31, 72],
                 [ 14, 31, 73],
                 [ 21, 31, 77],
                 [ 19, 31, 78]])

I want to convert it into arrays in list for every single row. I tried,

np.split(data,len(data))

#[array([[ 5, 31, 61]]),
# array([[10, 31, 67]]),
# array([[15, 31, 69]]),
# array([[ 4, 31, 72]]),
# array([[14, 31, 73]]),
# array([[21, 31, 77]]),
# array([[19, 31, 78]])]

But as you can see it gives double [ to me. What I simply want is;

[np.array([5, 31, 61]),
np.array([10, 31, 67]),
np.array([15, 31, 69]),
np.array([4, 31, 72]),
np.array([14, 31, 73]),
np.array([21, 31, 77]),
np.array([19, 31, 78])]

Wonder why you would need a format like this ? You can simply use a loop to get what you need [data[i] for i in range(len(data))] — akuiper
– akuiper, Commented Feb 10, 2022 at 6:57
The format is necessary for a script I use in somewhere else. I always stop myself from using loops in python. That is why I tried np.split. But I think sometimes it is good to use it since it does the job simply in here. Thanks for the answer. — datatech
– datatech, Commented Feb 10, 2022 at 7:04
Split does a loop. It's making a list. Have you tried list(data)? — hpaulj
– hpaulj, Commented Feb 10, 2022 at 7:31
np.split works too but it seems not to be optimized for OP's case. [*data] is optimized the best. — mathfux
– mathfux, Commented Feb 10, 2022 at 13:56

mozway · Accepted Answer · 2022-02-10 07:33:15Z

1

What about taking advantage of unpacking?

lst = [*data]

or:

lst = list(data)

output:

[array([ 5, 31, 61]),
 array([10, 31, 67]),
 array([15, 31, 69]),
 array([ 4, 31, 72]),
 array([14, 31, 73]),
 array([21, 31, 77]),
 array([19, 31, 78])]

answered Feb 10, 2022 at 7:33

mozway

267k13 gold badges56 silver badges106 bronze badges

Sign up to request clarification or add additional context in comments.

Comments

mathfux · Accepted Answer · 2022-02-10 13:57:10Z

np.split could be applied too but you are required to do it one-dimensionally. So you might like to create a one-dimensional view of your data first:

%%timeit
data_ravel = data.ravel()
out = np.split(data_ravel, len(data))
>>> 14.5 µs ± 337 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

Note that creating a view is costless (it took 0.13 µs on my computer)

Internally, it's being done like so:

out = []
div_points = range(0, data.size+1, data.shape[1])
start = div_points[:-1]
end = div_points[1:]
out = list(data_ravel[i:j] for i,j in zip(start, end))
>>> 2.31 µs ± 44.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

Note that it's faster because I'd doing a couple of optimisations here:

using range instead of np.array
using lazy list comprehension instead of list.append

However, it can't compete with classical methods like in @mozway 's answer. They are optimal:

%%timeit
out = [*data]
>>> 902 ns ± 8.09 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%%timeit
out = list(data)
>>> 979 ns ± 12.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%%timeit
out = [n for n in data]
>>> 1.04 µs ± 14.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%%timeit
out = list(n for n in data)
>>> 1.37 µs ± 80.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Collectives™ on Stack Overflow

Converting multidimensional array into arrays in list for every row

2 Answers 2

Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related