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)

Functional Approach

The functional approach to plotting in Matplotlib is a way of quickly generating a single-axis plot. Often, this is the approach taught to beginners. The functional approach allows the user to customize and save plots as image files in a chosen directory. In the following exercises and activities, you will learn how to build line plots, bar plots, histograms, box-and-whisker plots, and scatterplots using the functional approach.

Exercise 13: Functional Approach – Line Plot

To get started with Matplotlib, we will begin by creating a line plot and go on to customize it:

  1. Generate an array of numbers for the horizontal axis ranging from 0 to 10 in 20 evenly spaced values using the following code:

    import numpy as np

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

  2. Create an array and save it as object y. The snippet of the following code cubes the values of x and saves it to the array, y:

    y = x**3

  3. Create the plot as follows:

    import matplotlib.pyplot as plt

    plt.plot(x, y)