# Numpy array dimensions

## First:

By convention, in Python world, the shortcut for `numpy`

is `np`

, so:

`In [1]: import numpy as npIn [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*, according to the numpy doc, it's the same as axis/axes:

In Numpy dimensions are called axes. The number of axes is rank.

`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 (or the range) along each available axis.

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

`import numpy as np >>> np.shape(a)(2,2)`

Also works if the input is not a numpy array but a list of lists

`>>> a = [[1,2],[1,2]]>>> np.shape(a)(2,2)`

Or a tuple of tuples

`>>> a = ((1,2),(1,2))>>> np.shape(a)(2,2)`