Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Python Workshop

You're reading from   The Python Workshop Learn to code in Python and kickstart your career in software development or data science

Arrow left icon
Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781839218859
Length 608 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (6):
Arrow left icon
Andrew Bird Andrew Bird
Author Profile Icon Andrew Bird
Andrew Bird
Graham Lee Graham Lee
Author Profile Icon Graham Lee
Graham Lee
Corey Wade Corey Wade
Author Profile Icon Corey Wade
Corey Wade
Dr. Lau Cher Han Dr. Lau Cher Han
Author Profile Icon Dr. Lau Cher Han
Dr. Lau Cher Han
Olivier Pons Olivier Pons
Author Profile Icon Olivier Pons
Olivier Pons
Mario Corchero Jiménez Mario Corchero Jiménez
Author Profile Icon Mario Corchero Jiménez
Mario Corchero Jiménez
+2 more Show less
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Vital Python – Math, Strings, Conditionals, and Loops FREE CHAPTER 2. Python Structures 3. Executing Python – Programs, Algorithms, and Functions 4. Extending Python, Files, Errors, and Graphs 5. Constructing Python – Classes and Methods 6. The Standard Library 7. Becoming Pythonic 8. Software Development 9. Practical Python – Advanced Topics 10. Data Analytics with pandas and NumPy 11. Machine Learning Appendix

About the Course

Have you always wanted to learn Python, but never quite known how to start?

More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial.

The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code.

As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior.

You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms.

By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.

About the Chapters

Chapter 1, Vital Python: Math, Strings, Conditionals, Loops, explains how to write basic Python programs, and outlines the fundamentals of the Python language.

Chapter 2, Python Structures, covers the essential elements that are used to store and retrieve data in all programming languages.

Chapter 3, Executing Python: Programs, Algorithms, Functions, explains how to write more powerful and concise code through an increased appreciation of well-written algorithms, and an understanding of functions

Chapter 4, Extending Python, Files, Errors, Graphs, covers the basic I/O (input-output) operations for Python and covers using the matplotlib and seaborn libraries to create visualizations.

Chapter 5, Constructing Python: Classes and Methods, introduces one of the most central concepts in object-oriented programming classes, and it will help you write code using classes, which will make your life easier.

Chapter 6, The standard library, covers the importance of the Python standard library. It explains how to navigate in the standard Python libraries and overviews some of the most commonly used modules.

Chapter 7, Becoming Pythonic, covers the Python programming language, with which you will enjoy writing succinct, meaningful code. It also demonstrates some techniques for expressing yourself in ways that are familiar to other Python programmers.

Chapter 8, Software Development, covers how to debug and troubleshoot our applications, how to write tests to validate our code, and the documentation for other developers and users.

Chapter 9, Practical Python: Advanced Topics, explains how to take advantage of parallel programming, how to parse command-line arguments, how to encode and decode Unicode, and how to profile Python to discover and fix performance problems.

Chapter 10, Data Analytics with pandas and NumPy, covers data science, which is the core application of Python. We will be covering NumPy and pandas in this chapter.

Chapter 11, Machine Learning, covers the concept of machine learning and the steps involved in building a machine learning algorithm

Conventions

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Python provides the collections.defaultdict type."

A block of code is set as follows:

cubes = [x**3 for x in range(1,6)]
print(cubes)

New important words are shown like this: "Typically, standalone .py files are either called scripts or modules".

Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "You can also use Jupyter (New -> Text File)."

Long code snippets are truncated and the corresponding names of the code files on GitHub are placed at the top of the truncated code. The permalinks to the entire code are placed below the code snippet. It should look as follows:

Exercise66.ipynb

def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
textcolors=["black", "white"],
threshold=None, **textkw):
import matplotlib
if not isinstance(data, (list, np.ndarray)):

Before You Begin

Each great journey begins with a humble step. Our upcoming adventure in the land of Python is no exception. Before you can begin, you need to be prepared with the most productive environment. In this section, you will see how to do that.

Installing Jupyter on your system

We will be using Python 3.7 (from https://python.org):

To install Jupyter on windows, MacOS and Linux follow these steps:

  1. Head to https://www.anaconda.com/distribution/ to install the Anaconda Navigator, which is an interface through which you can access your local Jupyter notebook.
  2. Now, based on your Operating system (Windows, macOS or Linux) you need to download the Anaconda Installer.

    Have a look at the following figure where we have downloaded the Anaconda files for Windows:

Figure 0.1: The Anaconda homescreen

Figure 0.1: The Anaconda homescreen

Launching the Jupyter Notebook

To Launch the Jupyter Notebook frrom the Anaconda Navigator you need to follow the mentioned steps below:

  1. Once you install the Anaconda Navigator you will have the following screen at your end as shown in Figure 0.2.
    Figure 0.2: Anaconda installation screen

    Figure 0.2: Anaconda installation screen

  2. Now, click on Launch under the Jupyter Notebook option and launch the notebook on your local system:
    Figure 0.3: Jupyter notebook launch option

Figure 0.3: Jupyter notebook launch option

Congratulations! You have successfully installed Jupyter Notebook onto your system.

To Install the Python Terminal on your system

To install the Python terminal on your system, follow these steps:

  1. Open the following link, which is the Python community website URL: https://www.python.org/downloads/.
  2. Select the Operating System (Windows, macOS or Linux) you would be working on as highligthed in the following screenshot:
    Figure 0.4: The Python homescreen

    Figure 0.4: The Python homescreen

  3. Once you have downloaded the software, you need to install it.
  4. Have a look at the following screenshot in which we have installed the Python terminal on a Windows system. We load it through the Start menu and search for Python and click on the software.

    The Python terminal will look like this:

Figure 0.5: Python terminal interface

Figure 0.5: Python terminal interface

Congratulations! You have successfully installed the Python terminal onto your system.

A Few Important Packages

Some of the exercises in this chapter require the following packages:

  • Matplotlib
  • Seaborn
  • NumPy

Install them by following this guide. On Windows, open up the command prompt. On macOS or Linux, open up the terminal. Type the following commands:

pip install matplotlib seaborn numpy

If you prefer to use Anaconda to manage your packages, type in the following:

conda install matplotlib seaborn numpy

To install Docker

  1. Head to https://docs.docker.com/docker-for-windows/install/ to install Docker for Windows.
  2. Head to https://docs.docker.com/docker-for-mac/install/ to install Docker for macOS.
  3. Head to https://docs.docker.com/v17.12/install/linux/docker-ce/ubuntu/ to install Docker on Linux.

If you have any issues or questions about installation please email us at [email protected].

Installing the Code Bundle 

Download the code files from GitHub at https://packt.live/2PfducF and place them in a new folder called C:\Code on your local system. Refer to these code files for the complete code bundle. 

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image