Revision c28a24e1-6e40-4d36-a6c1-1b568e063c18 - Stack Overflow

## First:

By convention, in Python world, the shortcut for `numpy` is `np`, so:

 In [1]: import numpy as np

 In [2]: a = np.array([[1,2],[3,4]])

## Second：

In Numpy, **dimension**, **axis/axes**, **shape** are related and sometimes similar concepts:

### dimension

In *Mathematics/Physics*, dimension or dimensionality is informally defined as the minimum number of coordinates needed to specify any point within a space. But in *Numpy*, it's the same as axis/axes.


 In [3]: a.ndim # num of dimensions/axes, *Mathematics definition of dimension*
 Out[3]: 2


### axis/axes

the *nth* coordinate to index an `array` in Numpy. And multidimensional arrays can have one index per axis.

 In [4]: a[1,0] # to index `a`, we specific 1 at the first axis and 0 at the second axis.
 Out[4]: 3 # which results in 3 (locate at the row 1 and column 0, 0-based index)

### shape

describes how many data along each available axis.

 In [5]: a.shape
 Out[5]: (2, 2) # both the first and second axis have 2 (columns/rows/pages/blocks/...) data