[python] python numpy static , sum, mean, std (axis = dimension)

a = np.array([[1, 2], [3, 4],[5, 6]])

print('-------------------')
print(np.sum(a))
print(a.sum())
print(a.sum(0))
print(np.sum(a,axis=0))
print(np.sum(a,axis=1))
print('-------------------')
print(np.mean(a))
print(a.mean())
print(a.mean(0))
print(np.mean(a, axis=0))
print(np.mean(a, axis=1))

print('-------------------')
print(np.std(a))
print(a.std())
print(a.std(1))
print(np.std(a, axis=0))
print(np.std(a, axis=1))

output:

-------------------
21
21
[ 9 12]
[ 9 12]
[ 3  7 11]
-------------------
3.5
3.5
[ 3.  4.]
[ 3.  4.]
[ 1.5  3.5  5.5]
-------------------
1.70782512766
1.70782512766
[ 0.5  0.5  0.5]
[ 1.63299316  1.63299316]
[ 0.5  0.5  0.5]
[Finished in 0.1s]

ps: axis 0 means do operator in each column individual. (first dimension.) 

print(np.sum(a,axis=0))

[1,2]

[3,4]

[5,6] 

=[1+3+5 , 2+4+6]= [9,12]

axis 1 means to operator second dimension.  

print(np.sum(a,axis=1)) 

=[1+2,3+4,5+6]=[3,7,11]

---------------------

a = np.array([[1, 3,3, 4, 5, 6],[1, 3,3, 4, 5, 6]])
b = np.array([[1, 2,3, 4, 5, 6],[1, 1,1, 1, 1, 1]])
print(a)
print(np.mean(b, axis=0))
print(a-np.mean(b, axis=0))

[[1 3 3 4 5 6]
 [1 3 3 4 5 6]]

[ 1.   1.5  2.   2.5  3.   3.5]

[[ 0.   1.5  1.   1.5  2.   2.5]
 [ 0.   1.5  1.   1.5  2.   2.5]]
[Finished in 0.1s