Machine Learning Prerequisites (Numpy)

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NumPy - Arrays - Attributes of a NumPy Array

NumPy array (ndarray class) is the most used construct of NumPy in Machine Learning and Deep Learning. Let us look into some important attributes of this NumPy array.

Let us create a Numpy array first, say, array_A.

Pass the above list to array() function of NumPy

array_A = np.array([ [3,4,6], [0,8,1] ])

Now, let us understand some important attributes of ndarray object using the above-created array array_A.

(1) ndarray.ndim

ndim represents the number of dimensions (axes) of the ndarray.

e.g. for this 2-dimensional array [ [3,4,6], [0,8,1]], value of ndim will be 2. This ndarray has two dimensions (axes) - rows (axis=0) and columns (axis=1)

(2) ndarray.shape

shape is a tuple of integers representing the size of the ndarray in each dimension.

e.g. for this 2-dimensional array [ [3,4,6], [0,8,1]], value of shape will be (2,3) because this ndarray has two dimensions - rows and columns - and the number of rows is 2 and the number of columns is 3

(3) ndarray.size

size is the total number of elements in the ndarray. It is equal to the product of elements of the shape. e.g. for this 2-dimensional array [ [3,4,6], [0,8,1]], shape is (2,3), size will be product (multiplication) of 2 and 3 i.e. (2*3) = 6. Hence, the size is 6.

(4) ndarray.dtype

dtype tells the data type of the elements of a NumPy array. In NumPy array, all the elements have the same data type.

e.g. for this NumPy array [ [3,4,6], [0,8,1]], dtype will be int64

(5) ndarray.itemsize

itemsize returns the size (in bytes) of each element of a NumPy array.

e.g. for this NumPy array [ [3,4,6], [0,8,1]], itemsize will be 8, because this array consists of integers and size of integer (in bytes) is 8 bytes.


Please follow the below steps:

(1) Please import numpy as np

(2) Create a NumPy array with name my_array with the below elements

[ [1, 4, 5, 6], [7, 8, 9, 10], [11, 12, 14, 16] ]

Now, based on the above-created array (my_array), please answer the questions in the next few slides.

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