Numpy array dimensions Numpy array dimensions python python

# Numpy array dimensions

It is `.shape`:

ndarray.shape
Tuple of array dimensions.

Thus:

``>>> a.shape(2, 2)``

## First:

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

``In : import numpy as npIn : 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 : a.ndim  # num of dimensions/axes, *Mathematics definition of dimension*Out: 2``

### axis/axes

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

``In : a[1,0]  # to index `a`, we specific 1 at the first axis and 0 at the second axis.Out: 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 : a.shapeOut: (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)`` 