Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)

Object-Oriented Approach Using Subplots

Using the functional approach of plotting in Matplotlib does not allow the user to save the plot as an object in our environment. In the object-oriented approach, we create a figure object that acts as an empty canvas and then we add a set of axes, or subplots, to it. The figure object is callable and, if called, will return the figure to the console. We will demonstrate how this works by plotting the same x and y objects as we did in Exercise 13.

Exercise 19: Single Line Plot using Subplots

When we learned about the functional approach of plotting in Matplotlib, we began by creating and customizing a line plot. In this exercise, we will create and style a line plot using the functional plotting approach:

  1. Save x as an array ranging from 0 to 10 in 20 linearly spaced steps as follows:

    import numpy as np

    x = np.linspace(0, 10, 20)

    Save y as x cubed using the following:

    y = x**3

  2. Create a figure and a set of axes as follows:

    import matplotlib.pyplot as...