How does NumPy Sum (with axis) work?

Question

I\'ve taken it upon myself to learn how NumPy works for my own curiosity.

It seems that the simplest function is the hardest to translate to code (I un

Answer 1

I use a nested loop operation to explain it.

import numpy as np

n = np.array(
[[[1, 2, 3],
 [4, 5, 6],
 [7, 8, 9]],

 [[2, 4, 6],
 [8, 10, 12],
 [14, 16, 18]],

 [[1, 3, 5],
 [7, 9, 11],
 [13, 15, 17]]])

print(n)

print("============ sum axis=None=============")

sum = 0
for i in range(3):
  for j in range(3): 
    for k in range(3):
      sum += n[k][i][j]
print(sum) # 216

print('------------------')
print(np.sum(n))  # 216
print("============ sum axis=0 =============") 
for i in range(3):
  for j in range(3):
    sum = 0
    for axis in range(3):
      sum += n[axis][i][j]
    print(sum,end=' ')
  print()

print('------------------')
print("sum[0][0] = %d" % (n[0][0][0] + n[1][0][0] + n[2][0][0]))
print("sum[1][1] = %d" % (n[0][1][1] + n[1][1][1] + n[2][1][1]))
print("sum[2][2] = %d" % (n[0][2][2] + n[1][2][2] + n[2][2][2]))
print('------------------')
print(np.sum(n, axis=0)) 
print("============ sum axis=1 =============") 
for i in range(3):
  for j in range(3):
    sum = 0
    for axis in range(3):
      sum += n[i][axis][j]
    print(sum,end=' ')
  print()
print('------------------')
print("sum[0][0] = %d" % (n[0][0][0] + n[0][1][0] + n[0][2][0]))
print("sum[1][1] = %d" % (n[1][0][1] + n[1][1][1] + n[1][2][1]))
print("sum[2][2] = %d" % (n[2][0][2] + n[2][1][2] + n[2][2][2]))
print('------------------')
print(np.sum(n, axis=1))  
print("============ sum axis=2 =============") 
for i in range(3):
  for j in range(3):
    sum = 0
    for axis in range(3):
      sum += n[i][j][axis]
    print(sum,end=' ')
  print()
print('------------------')
print("sum[0][0] = %d" % (n[0][0][0] + n[0][0][1] + n[0][0][2]))
print("sum[1][1] = %d" % (n[1][1][0] + n[1][1][1] + n[1][1][2]))
print("sum[2][2] = %d" % (n[2][2][0] + n[2][2][1] + n[2][2][2]))
print('------------------')
print(np.sum(n, axis=2))
print("============ sum axis=(0,1)) =============") 
for i in range(3):
  sum = 0
  for axis1 in range(3):   
    for axis2 in range(3):
      sum += n[axis1][axis2][i]
  print(sum,end=' ')

print()
print('------------------')
print("sum[1] = %d" % (n[0][0][1] + n[0][1][1] + n[0][2][1] +
              n[1][0][1] + n[1][1][1] + n[1][2][1] +
              n[2][0][1] + n[2][1][1] + n[2][2][1] ))
print('------------------')
print(np.sum(n, axis=(0,1)))

result：

[[[ 1  2  3]
  [ 4  5  6]
  [ 7  8  9]]

 [[ 2  4  6]
  [ 8 10 12]
  [14 16 18]]

 [[ 1  3  5]
  [ 7  9 11]
  [13 15 17]]]
============ sum axis=None=============
216
------------------
216
============ sum axis=0 =============
4 9 14 
19 24 29 
34 39 44 
------------------
sum[0][0] = 4
sum[1][1] = 24
sum[2][2] = 44
------------------
[[ 4  9 14]
 [19 24 29]
 [34 39 44]]
============ sum axis=1 =============
12 15 18 
24 30 36 
21 27 33 
------------------
sum[0][0] = 12
sum[1][1] = 30
sum[2][2] = 33
------------------
[[12 15 18]
 [24 30 36]
 [21 27 33]]
============ sum axis=2 =============
6 15 24 
12 30 48 
9 27 45 
------------------
sum[0][0] = 6
sum[1][1] = 30
sum[2][2] = 45
------------------
[[ 6 15 24]
 [12 30 48]
 [ 9 27 45]]
============ sum axis=(0,1)) =============
57 72 87 
------------------
sum[1] = 72
------------------
[57 72 87]

Answer 2

Setup

consider the numpy array a

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

---

Where are the dimensions?

The dimensions and positions are highlighted by the following

            p  p  p  p  p
            o  o  o  o  o
            s  s  s  s  s

     dim 2  0  1  2  3  4

            |  |  |  |  |
  dim 0     ↓  ↓  ↓  ↓  ↓
  ----> [[[ 0  1  2  3  4]   <---- dim 1, pos 0
  pos 0   [ 5  6  7  8  9]   <---- dim 1, pos 1
          [10 11 12 13 14]]  <---- dim 1, pos 2
  dim 0
  ---->  [[15 16 17 18 19]   <---- dim 1, pos 0
  pos 1   [20 21 22 23 24]   <---- dim 1, pos 1
          [25 26 27 28 29]]] <---- dim 1, pos 2
            ↑  ↑  ↑  ↑  ↑
            |  |  |  |  |

     dim 2  p  p  p  p  p
            o  o  o  o  o
            s  s  s  s  s

            0  1  2  3  4

---

Dimension examples:

This becomes more clear with a few examples

a[0, :, :] # dim 0, pos 0

[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]

---

a[:, 1, :] # dim 1, pos 1

[[ 5  6  7  8  9]
 [20 21 22 23 24]]

---

a[:, :, 3] # dim 2, pos 3

[[ 3  8 13]
 [18 23 28]]

---

sum

explanation of sum and axis
a.sum(0) is the sum of all slices along dim 0

a.sum(0)

[[15 17 19 21 23]
 [25 27 29 31 33]
 [35 37 39 41 43]]

same as

a[0, :, :] + \
a[1, :, :]

[[15 17 19 21 23]
 [25 27 29 31 33]
 [35 37 39 41 43]]

---

a.sum(1) is the sum of all slices along dim 1

a.sum(1)

[[15 18 21 24 27]
 [60 63 66 69 72]]

same as

a[:, 0, :] + \
a[:, 1, :] + \
a[:, 2, :]

[[15 18 21 24 27]
 [60 63 66 69 72]]

---

a.sum(2) is the sum of all slices along dim 2

a.sum(2)

[[ 10  35  60]
 [ 85 110 135]]

same as

a[:, :, 0] + \
a[:, :, 1] + \
a[:, :, 2] + \
a[:, :, 3] + \
a[:, :, 4]

[[ 10  35  60]
 [ 85 110 135]]

---

default axis is -1
this means all axes. or sum all numbers.

a.sum()

435