The Python Workshop - Second Edition

By : Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee

4.7 (3)

Buy this Book

The Python Workshop - Second Edition

4.7 (3)

By: Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee

Buy this Book

Overview of this book

Python is among the most popular programming languages in the world. It’s ideal for beginners because it’s easy to read and write, and for developers, because it’s widely available with a strong support community, extensive documentation, and phenomenal libraries – both built-in and user-contributed. This project-based course has been designed by a team of expert authors to get you up and running with Python. You’ll work though engaging projects that’ll enable you to leverage your newfound Python skills efficiently in technical jobs, personal projects, and job interviews. The book will help you gain an edge in data science, web development, and software development, preparing you to tackle real-world challenges in Python and pursue advanced topics on your own. Throughout the chapters, each component has been explicitly designed to engage and stimulate different parts of the brain so that you can retain and apply what you learn in the practical context with maximum impact. By completing the course from start to finish, you’ll walk away feeling capable of tackling any real-world Python development problem.

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

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Chapter 1: Python Fundamentals – Math, Strings, Conditionals, and Loops

Overview

Introduction

Technical requirements

Python as a calculator

Strings – concatenation, methods, and input()

String interpolation

String indexing and slicing

Slicing

Booleans and conditionals

Loops

Summary

Free Chapter

Chapter 2: Python Data Structures

Overview

Introduction

Technical requirements

The power of lists

List methods

Matrix operations

Dictionary keys and values

Dictionary methods

Tuples

A survey of sets

Choosing types

Summary

Chapter 3: Executing Python – Programs, Algorithms, and Functions

Overview

Introduction

Technical requirements

Python scripts and modules

Summary

Chapter 4: Extending Python, Files, Errors, and Graphs

Overview

Introduction

Technical requirements

Reading files

Writing files

Preparing for debugging (defensive code)

Plotting techniques

The don’ts of plotting graphs

Summary

Chapter 5: Constructing Python – Classes and Methods

Overview

Introduction

Technical requirements

Methods

Summary

Chapter 6: The Standard Library

Overview

Introduction

Technical requirements

The importance of the Standard Library

Working with dates and times

Interacting with the OS

Using the subprocess module

Logging in Python

Using collections in Python

Using functools

Summary

Chapter 7: Becoming Pythonic

Overview

Introduction

Technical requirements

Using list comprehensions

Set and dictionary comprehensions

Using defaultdict to get default values

Creating custom iterators

Leveraging itertools

Lazy evaluations with generators

Using regular expressions

Summary

Chapter 8: Software Development

Overview

Introduction

Technical requirements

How to debug

Automated testing

Creating a pip package

Creating documentation the easy way

Source code management

Summary

Chapter 9: Practical Python – Advanced Topics

Overview

Introduction

Technical requirements

Developing collaboratively

Dependency management

Deploying code into production

Running code in parallel with multiprocessing

Parsing command-line arguments in scripts

Performance and profiling

Profiling code

Summary

Chapter 10: Data Analytics with pandas and NumPy

Overview

Introduction

Technical requirements

NumPy and basic stats

Matrices

The pandas library

Working with big data

Null values

Creating statistical graphs

Summary

Chapter 11: Machine Learning

Overview

Introduction

Technical requirements

Introduction to linear regression

Testing data with cross-validation

Regularization – Ridge and Lasso

K-nearest neighbors, decision trees, and random forests

Classification models

Boosting algorithms

Summary

Chapter 12: Deep Learning with Python

Overview

Introduction

Technical requirements

Introduction to deep learning

Your first deep learning model

Additional regularization technique – Dropout

Building neural networks for classification

Convolutional neural networks

Summary

Chapter 13: The Evolution of Python – Discovering New Python Features

Overview

Introduction

Python Enhancement Proposals

Python 3.7

Python 3.8

Python 3.9