Axis in a multidimensional NumPy array [duplicate]

The easiest way is with an example:

In [8]: x = np.array([[1, 2, 3], [4,5,6],[7,8,9]], np.int32)In [9]: xOut[9]: array([[1, 2, 3],       [4, 5, 6],       [7, 8, 9]], dtype=int32)In [10]: x.sum(axis=0)  # sum the columns [1,4,7] = 12, [2,5,8] = 15 [3,6,9] = 18  Out[10]: array([12, 15, 18])In [11]: x.sum(axis=1)    # sum the rows [1,2,3] = 6, [4,5,6] = 15 [7,8,9] = 24Out[11]: array([ 6, 15, 24])

axis 0 are the columns and axis 1 are the rows.

In a three dimensional array:

In [26]: x = np.array((((1,2), (3,4) ), ((5,6),(7,8))))In [27]: xOut[27]:    array([[[1, 2],           [3, 4]],          [[5, 6],           [7, 8]]])In [28]: x.shape # dimensions of the arrayOut[28]: (2, 2, 2)In [29]: x.sum(axis=0)Out[29]: array([[ 6,  8],   #  [1,5] = 6 [2,6] = 8 [3,7] = 10 [4, 8] = 12      [10, 12]])In [31]: x.sum(axis=1)Out[31]:     array([[ 4,  6],   # [1,3] = 4 [2,4] = 6 [5, 7] = 12 [6, 8] = 14           [12, 14]])In [33]: x.sum(axis=2) # [1, 2] = 3 [3, 4] = 7 [5, 6] = 11 [7, 8] = 15Out[33]: array([[ 3,  7],       [11, 15]])In [77]: x.ndim # number of dimensions of the arrayOut[77]: 3

Link for a good tutorial on using multidimensional data arrays

The axes can be named by traversing through the n-dimensional array, right from the outside of the array to the inside till we reach the actual scalar elements.The outermost dimension will always be axis 0 and the the innermost dimension(scalar elements) will be axis n-1.The below link will be more useful in imagining and realising NumPy axes -How does NumPy's transpose() method permute the axes of an array?

Cheat Code 1: When you use the NumPy sum function with the axis parameter, the axis that you specify is the axis that gets collapsed.

Cheat Code 2: When we use the axis parameter with the np.concatenate() function, the axis parameter defines the axis along which we stack the arrays.