NumPy — Programming at Multi Dimensions.

NumPy is a Python library that helps to deal with multi-dimensional arrays, matrices, vectors along high computational mathematical formulas and concepts. Wiki

Installing Numpy

Numpy is pre-installed in Google Colab. Here the code snippet of installing NumPy in Colaboratory.

!pip install numpy

Python List vs NumPy Array

A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension. A list is the Python equivalent of an array, but is resizeable and can contain elements of different types.[1]

import numpy as np
a = np.array([[1,2,3],[1,2,3],[4,5,6]])

Why Numpy Arrays over Python Lists?

• Size — Numpy data structures take up less space
• Performance — they have a need for speed and are faster than lists
• Functionality — SciPy and NumPy have optimized functions such as linear algebra operations built in.

Numpy Basic Operations

• Creating an array with zeros

array_zeros = np.zeros(4)

• Creating an array with ones

array_ones = np.ones(4)

• Creating an Empty Array

array_empty = np.empty(4)

• Creating Identity Matrices

ip: i = np.identity(3)
    i
op:array([[1., 0., 0.],        [0., 1., 0.],        [0., 0., 1.]])

• Multiplying Matrices

ip:array_a = np.array([[1,2],[3,4]])
   array_b = np.array([[1,2],[3,4]])
   dot_product = np.dot(array_a,array_b)
   cross_product = np.cross(array_a, array_b)
   dot_product
   cross_product
op: array([[ 7, 10],        [15, 22]])
    array([0, 0])

• Getting the mean, variance and Standard Deviation

ip: numpy_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
    mean = numpy_array.mean()
    variance = numpy_array.var()
    standard_deviation = numpy_array.std()
    print('Mean :',mean)
    print('Variance:',variance)
    print('Standard Deviation:', standard_deviation)
op: Mean : 5.0 
    Variance: 6.666666666666667 
    Standard Deviation: 2.581988897471611