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Data Science  with Python
Data Science  with Python

Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data

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Profile Icon Rohan Chopra Profile Icon England Profile Icon Mohamed Noordeen Alaudeen
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback Jul 2019 426 pages 1st Edition
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Arrow left icon
Profile Icon Rohan Chopra Profile Icon England Profile Icon Mohamed Noordeen Alaudeen
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Paperback Jul 2019 426 pages 1st Edition
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Data Science with Python

Data Visualization

Learning Objectives

By the end of this chapter, you will be able to:

  • Create and customize line plots, bar plots, histograms, scatterplots, and box-and-whisker plots using a functional approach
  • Develop a programmatic, descriptive plot title
  • Describe the advantages of using an object-oriented approach to create Matplotlib plots
  • Create a callable figure object containing a single axis or multiple axes
  • Resize and save figure objects with numerous subplots
  • Create and customize common plot types using Matplotlib

This chapter will cover various concepts that fall under data visualization.

Introduction

Data visualization is a powerful tool that allows users to digest large amounts of data very quickly. There are different types of plots that serve various purposes. In business, line plots and bar graphs are very common to display trends over time and compare metrics across groups, respectively. Statisticians, on the other hand, may be more interested in checking correlations between variables using a scatterplot or correlation matrix. They may also use histograms to check the distribution of a variable or boxplots to check for outliers. In politics, pie charts are widely used for comparing the total data between or among categories. Data visualizations can be very intricate and creative, being limited only by one's imagination.

The Python library Matplotlib is a well-documented, two-dimensional plotting library that can be used to create a variety of powerful data visualizations and aims to "...make easy things easy and hard things possible" (https://matplotlib...

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)

    plt.show()

    See...

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...

Summary

In this chapter, we used the Python plotting library Matplotlib to create, customize, and save plots using the functional approach. We then covered the importance of a descriptive title and created our own descriptive, programmatic titles. However, the functional approach does not create a callable figure object and it does not return subplots. Thus, to create a callable figure object with the potential of numerous subplots, we created, customized, and saved our plots using the object-oriented approach. Plotting needs can vary analysis to analysis, so covering every possible plot in this chapter is not practical. To create powerful plots that meet the needs of each individual analysis, it is imperative to become familiar with the documentation and examples found on the Matplotlib documentation page.

In the subsequent chapter, we will apply some of these plotting techniques as we dive into machine learning using scikit-learn.

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Key benefits

  • Gain useful insights into data science, from data collection through to visualization
  • Get up to speed with pandas, scikit-learn, and Matplotlib
  • Study a variety of data science algorithms using real-world datasets

Description

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.

Who is this book for?

Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.

What you will learn

  • Pre-process data to make it ready to use for machine learning
  • Create data visualizations with Matplotlib
  • Use scikit-learn to perform dimension reduction using principal component analysis (PCA)
  • Solve classification and regression problems
  • Get predictions using the XGBoost library
  • Process images and create machine learning models to decode them
  • Process human language for prediction and classification
  • Use TensorBoard to monitor training metrics in real time
  • Find the best hyperparameters for your model with AutoML

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Length: 426 pages
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Table of Contents

8 Chapters
Introduction to Data Science and Data Pre-Processing Chevron down icon Chevron up icon
Data Visualization Chevron down icon Chevron up icon
Introduction to Machine Learning via Scikit-Learn Chevron down icon Chevron up icon
Dimensionality Reduction and Unsupervised Learning Chevron down icon Chevron up icon
Mastering Structured Data Chevron down icon Chevron up icon
Decoding Images Chevron down icon Chevron up icon
Processing Human Language Chevron down icon Chevron up icon
Tips and Tricks of the Trade Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(1 Ratings)
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3 star 100%
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1 star 0%
C.T. Wong Apr 11, 2021
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
For a novice at data science, I found most of what I needed to get started in this book but if you are not a fan of constantly having to refer to another section in the middle of the chapter there are probably other books with better layouts. Personally I prefer all my information in one place and flowing smoothly - having to read about PCA and then flip forwards and backwards because the author has recycled information from elsewhere instead of printing it out twice got annoying. I bought the hard copy so can at least keep the pages open with paper weights - I'm not sure how well this works with the ebook.
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