Book Image

Learn Python Programming, 3rd edition - Third Edition

By : Fabrizio Romano, Heinrich Kruger
5 (1)
Book Image

Learn Python Programming, 3rd edition - Third Edition

5 (1)
By: Fabrizio Romano, Heinrich Kruger

Overview of this book

Learn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries. This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter. The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book. Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
A Gentle Introduction to Python
A Gentle Introduction to Python
A proper introduction
Enter the Python
About Python
What are the drawbacks?
Who is using Python today?
Setting up the environment
Installing Python
How to run a Python program
How is Python code organized?
Python's execution model
Guidelines for writing good code
Python culture
A note on IDEs
Summary
Free Chapter
2
Built-In Data Types
Built-In Data Types
Everything is an object
Mutable or immutable? That is the question
Numbers
Immutable sequences
Mutable sequences
Set types
Mapping types: dictionaries
Data types
Final considerations
Summary
3
Conditionals and Iteration
Conditionals and Iteration
Conditional programming
Looping
Assignment expressions
Putting all this together
A quick peek at the itertools module
Summary
4
Functions, the Building Blocks of Code
Functions, the Building Blocks of Code
Why use functions?
Scopes and name resolution
Input parameters
Return values
A few useful tips
Recursive functions
Anonymous functions
Function attributes
Built-in functions
Documenting your code
Importing objects
One final example
Summary
5
Comprehensions and Generators
Comprehensions and Generators
The map, zip, and filter functions
Comprehensions
Generators
Some performance considerations
Don't overdo comprehensions and generators
Name localization
Generation behavior in built-ins
One last example
Summary
6
OOP, Decorators, and Iterators
OOP, Decorators, and Iterators
Decorators
Object-oriented programming (OOP)
Writing a custom iterator
Summary
7
Exceptions and Context Managers
Exceptions and Context Managers
Exceptions
Context managers
Summary
8
Files and Data Persistence
Files and Data Persistence
Working with files and directories
Data interchange formats
I/O, streams, and requests
Persisting data on disk
Summary
9
Cryptography and Tokens
Cryptography and Tokens
The need for cryptography
Hashlib
HMAC
Secrets
JSON Web Tokens
Useful references
Summary
10
Testing
Testing
Testing your application
Test-driven development
Summary
11
Debugging and Profiling
Debugging and Profiling
Debugging techniques
Troubleshooting guidelines
Profiling Python
Summary
12
GUIs and Scripting
GUIs and Scripting
First approach: scripting
Second approach: a GUI application
Where do we go from here?
Summary
13
Data Science in Brief
Data Science in Brief
IPython and Jupyter Notebook
Dealing with data
Where do we go from here?
Summary
14
Introduction to API Development
Introduction to API Development
What is the Web?
Type hinting: An overview
APIs: An introduction
The railway API
Consuming an API
Where do we go from here?
Summary
15
Packaging Python Applications
Packaging Python Applications
The Python Package Index
The train schedule project
Packaging with setuptools
Building and publishing packages
Advice for starting new projects
Alternative tools
Further reading
Summary
16
Other Books You May Enjoy
Other Books You May Enjoy
17
Index
Index

IPython and Jupyter Notebook

In 2001, Fernando Perez was a graduate student in physics at CU Boulder, and was trying to improve the Python shell so that he could have the niceties he was used to when working with tools such as Mathematica and Maple. The result of these efforts took the name IPython.

In a nutshell, that small script began as an enhanced version of the Python shell and, through the efforts of other coders and eventually with proper funding from several different companies, it became the wonderful and successful project it is today. Some 10 years after its birth, a Notebook environment was created, powered by technologies such as WebSockets, the Tornado web server, jQuery, CodeMirror, and MathJax. The ZeroMQ library was also used to handle the messages between the Notebook interface and the Python core that lies behind it.

The IPython Notebook became so popular and widely used that, over time, all sorts of goodies were added to it. It can handle widgets, parallel...

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