I have a list of numpy arrays, that I want to convert into a single int numpy array.
For example if I have 46 4 x 4 numpy arrays in a list of dimension 2 x 23, I want to convert it into a single integer numpy array of 2 x 23 x 4 x 4 dimension. I have found a way to do this by going through every single element and using numpy.stack(). Is there any better way?
2 Answers
You can simply use np.asarray like so
import numpy as np
list_of_lists = [[np.random.normal(0, 1, (4, 4)) for _ in range(23)]
for _ in range(2)]
a = np.asarray(list_of_lists)
a.shape
The function will infer the shape of the list of lists for you and create an appropriate array.
Comments
Stack works for me:
In [191]: A,B,C = np.zeros((2,2),int),np.ones((2,2),int),np.arange(4).reshape(2,
...: 2)
In [192]: x = [[A,B,C],[C,B,A]]
In [193]:
In [193]: x
Out[193]:
[[array([[0, 0],
[0, 0]]), array([[1, 1],
[1, 1]]), array([[0, 1],
[2, 3]])], [array([[0, 1],
[2, 3]]), array([[1, 1],
[1, 1]]), array([[0, 0],
[0, 0]])]]
In [194]: np.stack(x)
Out[194]:
array([[[[0, 0],
[0, 0]],
[[1, 1],
[1, 1]],
[[0, 1],
[2, 3]]],
[[[0, 1],
[2, 3]],
[[1, 1],
[1, 1]],
[[0, 0],
[0, 0]]]])
In [195]: _.shape
Out[195]: (2, 3, 2, 2)
stack views x as a list of 2 items, and applies np.asarray to each.
In [198]: np.array(x[0]).shape
Out[198]: (3, 2, 2)
Then adds a dimension, (1,3,2,2), and concatenates on the first axis.
In this case np.array(x) works just as well
In [201]: np.array(x).shape
Out[201]: (2, 3, 2, 2)
2x23list of4x4numpy arrays and want to make it one single 4 dimensional numpy array?