ndarray_3darray

🛻 3darray

import numpy as np

a = np.arange(30).reshape(3,5,2)
print(a)

# [[[ 0  1]
#   [ 2  3]
#   [ 4  5]
#   [ 6  7]
#   [ 8  9]]
 
#  [[10 11]
#   [12 13]
#   [14 15]
#   [16 17]
#   [18 19]]

#  [[20 21]
#   [22 23]
#   [24 25]
#   [26 27]
#   [28 29]]]

⌨️ np.sum (axis=0, 1, 2)

b = np.sum(a, axis=0)
print(b, b.shape)
#  [[30 33]
#   [36 39]
#   [42 45]
#   [48 51]
#   [54 57]] (5, 2)


c = np.sum(a, axis=1)
print(c, c.shape)
# [[ 20  25]
#  [ 70  75]
#  [120 125]] (3, 2)


d = np.sum(a, axis=2)
print(d, d.shape)
# [[ 1  5  9 13 17]
#  [21 25 29 33 37]
#  [41 45 49 53 57]] (3, 5)

.
.

⌨️ np.moveaxis

print(a, a.shape)
#   [[[ 0  1]
#     [ 2  3]
#     [ 4  5]
#     [ 6  7]
#     [ 8  9]]
 
#    [[10 11]
#     [12 13]
#     [14 15]
#     [16 17]
#     [18 19]]

#    [[20 21]
#     [22 23]
#     [24 25]
#     [26 27]
#     [28 29]]] (3, 5, 2)

a.shape = (3, 5, 2) 에서 ( , 5, )를 2번째 자리로 옮기면(a2),
a2.shape = (3, 2, 5)

a2 = np.moveaxis(a,2,0)
print(a2, a2.shape)
# [[[ 0  2  4  6  8]
#   [ 1  3  5  7  9]]

#   [[10 12 14 16 18]
#    [11 13 15 17 19]]

#   [[20 22 24 26 28]
#    [21 23 25 27 29]]] (3, 2, 5)

.
.

⌨️ np.newaxis / np.expand_dims

차원을 추가하고 싶다면, np.newaxis 또는 np.expand_dims 를 사용하면 된다.

a10 = np.arange(10)
print(a10, a10.shape)
# [0 1 2 3 4 5 6 7 8 9] (10,) -> 1차원

a11 = a10[np.new_axis, : ]
print(a11, a11.shape)
# [[0 1 2 3 4 5 6 7 8 9]] (1, 10)  -> 2차원

a12 = a10[ :, np.newaxis]
# [[0]
#  [1]
#  [2]
#  [3]
#  [4]
#  [5]
#  [6]
#  [7]
#  [8]
#  [9]] (10, 1)    -> 2차원
 
a13 = np.expand_dims(a10, 0)
print(a13, a13.shape)
# [[0 1 2 3 4 5 6 7 8 9]] (1, 10)  -> 2차원