The shape and dimension of NumPy arrays

1. Start by importing NumPy:

import numpy as np

1. Produce a NumPy array of 10 digits, similar to Python's range(10) method:

np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

1. The array looks like a Python list with only one pair of brackets. This means it is of one dimension. Store the array and find out the shape:

array_1 = np.arange(10)
array_1.shape
(10L,)

1. The array has a data attribute, shape. The type of array_1.shape is a tuple (10L,), which has length 1, in this case. The number of dimensions is the same as the length of the tuple—a dimension of 1, in this case:

array_1.ndim      #Find number of dimensions of array_1
1

1. The array has 10 elements. Reshape the array by calling the reshape method:

array_1.reshape((5,2))
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7],
       [8, 9]])

1. This reshapes the array into 5 x 2 data object that resembles a list of lists (a three dimensional NumPy array looks like a list of lists of lists). You did not save the changes. Save the reshaped array as follows::

array_1 = array_1.reshape((5,2))

1. Note that array_1 is now two-dimensional. This is expected, as its shape has two numbers and it looks like a Python list of lists:

array_1.ndim
2