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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A comprehensive guide to Python programming for web development using the most popular Python web framework - Django
Key Features
- Learn the fundamentals of programming with Python and building web apps
- Build web applications from scratch with Django
- Create real-world RESTful web services with the latest Django framework
Book Description
If you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform.
Learn Web Development with Python will get you started with Python programming techniques, show you how to enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. Last but not least, you’ll learn the best practices for creating real-world applications.
By the end of this Learning Path, you will have a full understanding of how Django works and how to use it to build web applications from scratch.
This Learning Path includes content from the following Packt products:
•Learn Python Programming by Fabrizio Romano
•Django RESTful Web Services by Gastón C. Hillar
•Django Design Patterns and Best Practices by Arun Ravindran
What you will learn
- Explore the fundamentals of Python programming with interactive projects
- Grasp essential coding concepts along with the basics of data structures and control flow
- Develop RESTful APIs from scratch with Django and the Django REST Framework
- Create automated tests for RESTful web services
- Debug, test, and profile RESTful web services with Django and the Django REST Framework
- Use Django with other technologies such as Redis and Celery
Who this book is for
If you have little experience in coding or Python and want to learn how to build full-fledged web apps, this Learning Path is for you. No prior experience with RESTful web services, Python, or Django is required, but basic Python programming experience is needed to understand the concepts covered.
Fabrizio Romano was born in Italy in 1975. He holds a master's degree in computer science engineering from the University of Padova. He is also a certified scrum master, Reiki master and teacher, and a member of CNHC. He moved to London in 2011 to work for companies such as Glasses Direct, and TBG/Sprinklr. He now works at Sohonet as a Principal Engineer/Team Lead. He has given talks on Teaching Python and TDD at two editions of EuroPython, and at Skillsmatter and ProgSCon, in London. Gaston C. Hillar is Italian and has been working with computers since he was eight years old. Gaston has a bachelor's degree in computer science (graduated with honors) and an MBA. He is an independent consultant, a freelance author, and a speaker. He has been a senior contributing editor at Dr. Dobb's and has written more than a hundred articles on software development topics. He has received the prestigious Intel® Black Belt Software Developer award eight times. He lives with his wife, Vanesa, and his two sons, Kevin and Brandon. Arun Ravindran is an avid speaker and blogger who has been tinkering with Django since 2007 for projects ranging from intranet applications to social networks. He is a long-time open source enthusiast and Python developer. His articles and screencasts have been invaluable to the rapidly growing Django community. He is currently a developer member of the Django Software Foundation. Arun is also a movie buff and loves graphic novels and comics.Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 1024
Veröffentlichungsjahr: 2018
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Learn Web Development with Python
Copyright © 2018 Packt Publishing
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: December 2018
Production reference: 1201218
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78995-329-9
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Contributors
About the authors
Fabrizio Romano was born in Italy in 1975. He holds a master's degree in computer science engineering from the University of Padova. He is also a certified scrum master, Reiki master and teacher, and a member of CNHC.
He moved to London in 2011 to work for companies such as Glasses Direct, and TBG/Sprinklr. He now works at Sohonet as a Principal Engineer/Team Lead.
He has given talks on Teaching Python and TDD at two editions of EuroPython, and at Skillsmatter and ProgSCon, in London.
Gaston C. Hillar is Italian and has been working with computers since he was eight years old. Gaston has a bachelor's degree in computer science (graduated with honors) and an MBA. He is an independent consultant, a freelance author, and a speaker.
He has been a senior contributing editor at Dr. Dobb's and has written more than a hundred articles on software development topics. He has received the prestigious Intel® Black Belt Software Developer award eight times.
He lives with his wife, Vanesa, and his two sons, Kevin and Brandon.
Arun Ravindran is an avid speaker and blogger who has been tinkering with Django since 2007 for projects ranging from intranet applications to social networks. He is a long-time open source enthusiast and Python developer. His articles and screencasts have been invaluable to the rapidly growing Django community. He is currently a developer member of the Django Software Foundation. Arun is also a movie buff and loves graphic novels and comics.
About the reviewers
Heinrich Kruger was born in South Africa in 1981. He obtained a bachelor's degree with honors from the University of the Witwatersrand in South Africa in 2005 and a master's degree in computer science from Utrecht University in the Netherlands in 2008. He worked as a research assistant at Utrecht University from 2009 until 2013 and has been working as a professional software developer developer since 2014. He has been using Python for personal and projects and in his studies since 2004, and professionally since 2014.
Julio Vicente Trigo Guijarro is a computer science engineer with over a decade of experience in software development. He completed his studies at the University of Alicante, Spain, in 2007 and moved to London in 2010. He has been using Python since 2012 and currently works as a senior software developer and team lead at Sohonet, developing real-time collaboration applications for the media industry. He is also a certified ScrumMaster and was one of the technical reviewers of the first edition of this book.
Norbert Mate is a web developer who started his career back in 2008. His first programming language as a professional web development was PHP, and then he moved on to JavaScript/node.js and Python/Django/Django REST framework. He is passionate about software architecture, design patterns, and clean code.
Antoni Aloy is a computer engineer graduated from the Universitat Oberta de Catalunya (UOC). He has been working with Python since 1999 and with Django since its early releases. In 2009, he founded APSL (apsl.net), a development and IT company based in Mallorca (Spain), in which Python and Django are the backbone of the software development department. He is also a founding member of the Python España Association and promotes the use of Python and Django through workshops and articles.
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Table of Contents
Title Page
Copyright and Credits
Learn Web Development with Python
About Packt
Why subscribe?
Packt.com
Contributors
About the authors
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Reviews
A Gentle Introduction to Python
A proper introduction
Enter the Python
About Python
Portability
Coherence
Developer productivity
An extensive library
Software quality
Software integration
Satisfaction and enjoyment
What are the drawbacks?
Who is using Python today?
Setting up the environment
Python 2 versus Python 3
Installing Python
Setting up the Python interpreter
About virtualenv
Your first virtual environment
Your friend, the console
How you can run a Python program
Running Python scripts
Running the Python interactive shell
Running Python as a service
Running Python as a GUI application
How is Python code organized?
How do we use modules and packages?
Python's execution model
Names and namespaces
Scopes
Objects and classes
Guidelines on how to write good code
The Python culture
A note on IDEs
Summary
Built-in Data Types
Everything is an object
Mutable or immutable? That is the question
Numbers
Integers
Booleans
Real numbers
Complex numbers
Fractions and decimals
Immutable sequences
Strings and bytes
Encoding and decoding strings
Indexing and slicing strings
String formatting
Tuples
Mutable sequences
Lists
Byte arrays
Set types
Mapping types – dictionaries
The collections module
namedtuple
defaultdict
ChainMap
Enums
Final considerations
Small values caching
How to choose data structures
About indexing and slicing
About the names
Summary
Iterating and Making Decisions
Conditional programming
A specialized else – elif
The ternary operator
Looping
The for loop
Iterating over a range
Iterating over a sequence
Iterators and iterables
Iterating over multiple sequences
The while loop
The break and continue statements
A special else clause
Putting all this together
A prime generator
Applying discounts
A quick peek at the itertools module
Infinite iterators
Iterators terminating on the shortest input sequence
Combinatoric generators
Summary
Functions, the Building Blocks of Code
Why use functions?
Reducing code duplication
Splitting a complex task
Hiding implementation details
Improving readability
Improving traceability
Scopes and name resolution
The global and nonlocal statements
Input parameters
Argument-passing
Assignment to argument names doesn't affect the caller
Changing a mutable affects the caller
How to specify input parameters
Positional arguments
Keyword arguments and default values
Variable positional arguments
Variable keyword arguments
Keyword-only arguments
Combining input parameters
Additional unpacking generalizations
Avoid the trap! Mutable defaults
Return values
Returning multiple values
A few useful tips
Recursive functions
Anonymous functions
Function attributes
Built-in functions
One final example
Documenting your code
Importing objects
Relative imports
Summary
Saving Time and Memory
The map, zip, and filter functions
map
zip
filter
Comprehensions
Nested comprehensions
Filtering a comprehension
dict comprehensions
set comprehensions
Generators
Generator functions
Going beyond next
The yield from expression
Generator expressions
Some performance considerations
Don't overdo comprehensions and generators
Name localization
Generation behavior in built-ins
One last example
Summary
OOP, Decorators, and Iterators
Decorators
A decorator factory
Object-oriented programming (OOP)
The simplest Python class
Class and object namespaces
Attribute shadowing
Me, myself, and I – using the self variable
Initializing an instance
OOP is about code reuse
Inheritance and composition
Accessing a base class
Multiple inheritance
Method resolution order
Class and static methods
Static methods
Class methods
Private methods and name mangling
The property decorator
Operator overloading
Polymorphism – a brief overview
Data classes
Writing a custom iterator
Summary
Files and Data Persistence
Working with files and directories
Opening files
Using a context manager to open a file
Reading and writing to a file
Reading and writing in binary mode
Protecting against overriding an existing file
Checking for file and directory existence
Manipulating files and directories
Manipulating pathnames
Temporary files and directories
Directory content
File and directory compression
Data interchange formats
Working with JSON
Custom encoding/decoding with JSON
IO, streams, and requests
Using an in-memory stream
Making HTTP requests
Persisting data on disk
Serializing data with pickle
Saving data with shelve
Saving data to a database
Summary
Testing, Profiling, and Dealing with Exceptions
Testing your application
The anatomy of a test
Testing guidelines
Unit testing 
Writing a unit test
Mock objects and patching
Assertions
Testing a CSV generator
Boundaries and granularity
Testing the export function
Final considerations
Test-driven development
Exceptions
Profiling Python
When to profile?
Summary
Concurrent Execution
Concurrency versus parallelism
Threads and processes – an overview
Quick anatomy of a thread
Killing threads
Context-switching
The Global Interpreter Lock
Race conditions and deadlocks
Race conditions
Scenario A – race condition not happening
Scenario B – race condition happening
Locks to the rescue
Scenario C – using a lock
Deadlocks
Quick anatomy of a process
Properties of a process
Multithreading or multiprocessing?
Concurrent execution in Python
Starting a thread
Starting a process
Stopping threads and processes
Stopping a process
Spawning multiple threads
Dealing with race conditions
A thread's local data
Thread and process communication
Thread communication
Sending events
Inter-process communication with queues
Thread and process pools
Using a process to add a timeout to a function
Case examples
Example one – concurrent mergesort
Single-thread mergesort
Single-thread multipart mergesort
Multithreaded mergesort
Multiprocess mergesort
Example two – batch sudoku-solver
What is Sudoku?
Implementing a sudoku-solver in Python
Solving sudoku with multiprocessing
Example three – downloading random pictures
Downloading random pictures with asyncio
Summary
Debugging and Troubleshooting
Debugging techniques
Debugging with print
Debugging with a custom function
Inspecting the traceback
Using the Python debugger
Inspecting log files
Other techniques
Profiling
Assertions
Where to find information
Troubleshooting guidelines
Using console editors
Where to inspect
Using tests to debug
Monitoring
Summary
Installing the Required Software and Tools
Creating a virtual environment with Python 3.x and PEP 405
Understanding the directory structure for a virtual environment
Activating the virtual environment
Deactivating the virtual environment
Installing Django and Django REST frameworks in an isolated environment
Creating an app with Django
Understanding Django folders, files, and configurations
Installing tools
Installing Curl
Installing HTTPie
Installing the Postman REST client
Installing Stoplight
Installing iCurlHTTP
Test your knowledge
Summary
Working with Models, Migrations, Serialization, and Deserialization
Defining the requirements for our first RESTful Web Service
Creating our first model
Running our initial migration
Understanding migrations
Analyzing the database
Understanding the table generated by Django
Controlling, serialization, and deserialization
Working with the Django shell and diving deeply into serialization and deserialization
Test your knowledge
Summary
Creating API Views
Creating Django views combined with serializer classes
Understanding CRUD operations with Django views and the request methods
Routing URLs to Django views and functions
Launching Django's development server
Making HTTP GET requests that target a collection of instances
Making HTTP GET requests that target a single instance
Making HTTP POST requests
Making HTTP PUT requests
Making HTTP DELETE requests
Making HTTP GET requests with Postman
Making HTTP POST requests with Postman
Test your knowledge
Summary
Using Generalized Behavior from the APIView Class
Taking advantage of model serializers
Understanding accepted and returned content types
Making unsupported HTTP OPTIONS requests with command-line tools
Understanding decorators that work as wrappers
Using decorators to enable different parsers and renderers
Taking advantage of content negotiation classes
Making supported HTTP OPTIONS requests with command-line tools
Working with different content types
Sending HTTP requests with unsupported HTTP verbs
Test your knowledge
Summary
Understanding and Customizing the Browsable API Feature
Understanding the possibility of rendering text/HTML content
Using a web browser to work with our web service
Making HTTP GET requests with the browsable API
Making HTTP POST requests with the browsable API
Making HTTP PUT requests with the browsable API
Making HTTP OPTIONS requests with the browsable API
Making HTTP DELETE requests with the browsable API
Test your knowledge
Summary
Using Constraints, Filtering, Searching, Ordering, and Pagination
Browsing the API with resources and relationships
Defining unique constraints
Working with unique constraints
Understanding pagination
Configuring pagination classes
Making requests that paginate results
Working with customized pagination classes
Making requests that use customized paginated results
Configuring filter backend classes
Adding filtering, searching, and ordering
Working with different types of Django filters
Making requests that filter results
Composing requests that filter and order results
Making requests that perform starts with searches
Using the browsable API to test pagination, filtering, searching, and ordering
Test your knowledge
Summary
Securing the API with Authentication and Permissions
Understanding authentication and permissions in Django, the Django REST framework, and RESTful Web Services
Learning about the authentication classes
Including security and permissions-related data to models
Working with object-level permissions via customized permission classes
Saving information about users that make requests
Setting permission policies
Creating the superuser for Django
Creating a user for Django
Making authenticated requests
Making authenticated HTTP PATCH requests with Postman
Browsing the secured API with the required authentication
Working with token-based authentication
Generating and using tokens
Test your knowledge
Summary
Applying Throttling Rules and Versioning Management
Understanding the importance of throttling rules
Learning the purpose of the different throttling classes in the Django REST framework
Configuring throttling policies in the Django REST framework
Running tests to check that throttling policies work as expected
Understanding versioning classes
Configuring a versioning scheme
Running tests to check that versioning works as expected
Test your knowledge
Summary
Automating Tests
Getting ready for unit testing with pytest
Writing unit tests for a RESTful Web Service
Discovering and running unit tests with pytest
Writing new unit tests to improve the tests' code coverage
Running unit tests again with pytest
Test your knowledge
Summary
Solutions
Chapter 11: Installing the Required Software and Tools
Chapter 12: Working with Models, Migrations, Serialization, and Deserialization
Chapter 13: Creating API Views
Chapter 14: Using Generalized Behavior from the APIView Class
Chapter 15: Understanding and Customizing the Browsable API Feature
Chapter 16: Using Constraints, Filtering, Searching, Ordering, and Pagination
Chapter 17: Securing the API with Authentication and Permissions
Chapter 18: Applying Throttling Rules and Versioning Management
Chapter 19: Automating Tests
Templates
Understanding Django's template language features
Variables
Attributes
Filters
Tags
Philosophy – don't invent a programming language
Jinja2
Organizing templates
How templates work
Using Bootstrap
But they all look the same!
Lightweight alternatives
Template patterns
Pattern — template inheritance tree
Problem details
Solution details
Pattern — the active link
Problem details
Solution details
A template-only solution
Custom tags
Summary
Admin Interface
Using the admin interface
Enhancing models for the admin
Not everyone should be an admin
Admin interface customizations
Changing the heading
Changing the base and stylesheets
Adding a rich-text editor for WYSIWYG editing
Bootstrap-themed admin
Complete overhauls
Protecting the admin
Pattern – feature flags
Problem details
Solution details
Summary
Forms
How forms work
Forms in Django
Why does data need cleaning?
Displaying forms
Time to be crisp
Understanding CSRF
Form processing with class-based views
Form patterns
Pattern – dynamic form generation
Problem details
Solution details
Pattern – user-based forms
Problem details
Solution details
Pattern – multiple form actions per view
Problem details
Solution details
Separate views for separate actions
Same view for separate actions
Pattern – CRUD views
Problem details
Solution details
Summary
Security
Cross-site scripting
Why are your cookies valuable?
How Django helps
Where Django might not help
Cross-site request forgery
How Django helps
Where Django might not help
SQL injection
How Django helps
Where Django might not help
Clickjacking
How Django helps
Shell injection
How Django helps
And the web attacks are unending
A handy security checklist
Summary
Working Asynchronously
Why asynchronous?
Pitfalls of asynchronous code
Asynchronous patterns
Endpoint callback pattern
Publish-subscribe pattern
Polling pattern
Asynchronous solutions for Django
Working with Celery
How Celery works
Celery best practices
Handling failure
Idempotent tasks
Avoid writing to shared or global state
Database updates without race conditions
Avoid passing complex objects to tasks
Understanding asyncio
asyncio versus threads
The classic web-scraper example
Synchronous web-scraping
Asynchronous web-scraping
Concurrency is not parallelism
Entering Channels
Listening to notifications with WebSockets
Differences from Celery
Summary
Creating APIs
RESTful API
API design
Versioning
Django Rest framework
Improving the Public Posts API
Hiding the IDs
API patterns
Pattern – human browsable interface
Problem details
Solution details
Pattern – Infinite Scrolling
Problem details
Solution details
Summary
Production-Ready
The production environment
Choosing a web stack
Components of a stack
Virtual machines or Docker
Microservices
Hosting
Platform as a service
Virtual private servers
Serverless
Other hosting approaches
Deployment tools
Fabric
Typical deployment steps
Configuration management
Monitoring
Improving Performance
Frontend performance
Backend performance
Templates
Database
Caching
Cached session backend
Caching frameworks
Caching patterns
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
If you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform.
Learn Web Development with Python will get you started with Python programming techniques, show you how to enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. Last but not least, you’ll learn the best practices for creating of real-world applications.
By the end of this Learning Path, you will have a full understanding of how Django works and how use it to build web applications from scratch.
This Learning Path includes content from the following Packt products:
Learn Python Programming by Fabrizio Romano
Django RESTful Web Services by Gastón C. Hillar
Django Design Patterns and Best Practices by Arun Ravindran
Who this book is for
If you have little experience in coding or Python and want to learn how to build full-fledged web apps, this Learning Path is for you. No prior experience with RESTful web services, Python, or Django is required, but basic Python programming experience is needed to understand the concepts covered.
What this book covers
Chapter 1, A Gentle Introduction to Python, introduces you to fundamental programming concepts. It guides you through getting Python up and running on your computer and introduces you to some of its constructs.
Chapter 2, Built-in Data Types, introduces you to Python built-in data types. Python has a very rich set of native data types, and this chapter will give you a description and a short example for each of them.
Chapter 3, Iterating and Making Decisions, teaches you how to control the flow of your code by inspecting conditions, applying logic, and performing loops.
Chapter 4, Functions, the Building Blocks of Code, teaches you how to write functions. Functions are the keys to reusing code, to reducing debugging time, and, in general, to writing better code.
Chapter 5, Saving Time and Memory, introduces you to the functional aspects of Python programming. This chapter teaches you how to write comprehensions and generators, which are powerful tools that you can use to speed up your code and save memory.
Chapter 6, OOP, Decorators, and Iterators, teaches you the basics of object-oriented programming with Python. It shows you the key concepts and all the potentials of this paradigm. It also shows you one of the most beloved characteristics of Python: decorators. Finally, it also covers the concept of iterators.
Chapter 7, Files and Data Persistence, teaches you how to deal with files, streams, data interchange formats, and databases, among other things.
Chapter 8, Testing, Profiling, and Dealing with Exceptions, teaches you how to make your code more robust, fast, and stable using techniques such as testing and profiling. It also formally defines the concept of exceptions.
Chapter 9, Concurrent Execution, is a challenging chapter that describes how to do many things at the same time. It provides an introduction to the theoretical aspects of this subject and then presents three nice exercises that are developed with different techniques, thereby enabling the reader to understand the differences between the paradigms presented.
Chapter 10, Debugging and Troubleshooting, shows you the main methods for debugging your code and some examples on how to apply them.
Chapter 11, Installing the Required Software and Tools, shows how to get started in our journey toward creating RESTful Web Services with Python and its most popular web framework—Django. We will install and configure the environments, the software, and the tools required to create RESTful Web Services with Django and Django REST framework. We will learn the necessary steps in Linux, macOS, and Windows. We will create our first app with Django, we will take a first look at the Django folders, files, and configurations, and we will make the necessary changes to activate Django REST framework. In addition, we will introduce and install command-line and GUI tools that we will use to interact with the RESTful Web Services that we will design, code, and test in the forthcoming chapters.
Chapter 12, Working with Models, Migrations, Serialization, and Deserialization, describes how to design a RESTful Web Service to interact with a simple SQLite database and perform CRUD operations with toys. We will define the requirements for our web service, and we will understand the tasks performed by each HTTP method and the different scopes. We will create a model to represent and persist toys and execute migrations in Django to create the required tables in the database. We will analyze the tables and learn how to manage the serialization of toy instances into JSON representations with Django REST framework and the reverse process.
Chapter 13, Creating API Views, is about executing the first version of a simple Django RESTful Web Service that interacts with a SQLite database. We will write API views to process diverse HTTP requests on a collection of toys and on a specific toy. We will work with the following HTTP verbs: GET, POST, and PUT. We will configure the URL patterns list to route URLs to views. We will start the Django development server and use command-line tools (curl and HTTPie) to compose and send diverse HTTP requests to our RESTful Web Service. We will learn how HTTP requests are processed in Django and our code. In addition, we will work with Postman, a GUI tool, to compose and send other HTTP requests to our RESTful Web Service.
Chapter 14, Using Generalized Behavior from the APIView Class, presents different ways to improve our simple Django RESTful Web Service. We will take advantage of many features included in the Django REST framework to remove duplicate code and add many features for the web service. We will use model serializers, understand the different accepted and returned content types, and the importance of providing accurate responses to the HTTP OPTIONS requests. We will make the necessary changes to the existing code to enable diverse parsers and renderers. We will learn how things work under the hoods in Django REST framework. We will work with different content types and note how the RESTful Web Service improves compared to its previous versions.
Chapter 15, Understanding and Customizing the Browsable API Feature, explains how to use one of the additional features that Django REST framework adds to our RESTful Web Service—the browsable API. We will use a web browser to work with our first web service built with Django. We will learn to make HTTP GET, POST, PUT, OPTIONS, and DELETE requests with the browsable API. We will be able to easily test CRUD operations with a web browser. The browsable API will allow us to easily interact with our RESTful Web Service.
Chapter 16, Using Constraints, Filtering, Searching, Ordering, and Pagination, describes the usage of the browsable API feature to navigate through the API with resources and relationships. We will add unique constraints to improve the consistency of the models in our RESTful Web Service. We will understand the importance of paginating results and configure and test a global limit/offset pagination scheme with Django REST framework. Then, we will create our own customized pagination class to ensure that requests won't be able to require a huge number of elements on a single page. We will configure filter backend classes and incorporate code into the models to add filtering, searching, and ordering capabilities to the class-based views. We will create a customized filter and make requests to filter, search, and order results. Finally, we will use the browsable API to test pagination, filtering, and ordering.
Chapter 17, Securing the API with Authentication and Permissions, presents the differences between authentication and permissions in Django, Django REST framework, and RESTful Web Services. We will analyze the authentication classes included in Django REST framework out of the box. We will follow the steps needed to provide security- and permissions-related data to models.
We will work with object-level permissions via customized permission classes and save information about users who make requests. We will configure permission policies and compose and send authenticated requests to understand how the permission policies work. We will use command-line tools and GUI tools to compose and send authenticated requests. We will browse the secure RESTful Web Service with the browsable API feature and work with a simple token-based authentication provided by Django REST framework to understand another way of authenticating requests.
Chapter 18, Applying Throttling Rules and Versioning Management, focuses on the importance of throttling rules and how we can combine them with authentication and permissions in Django, Django REST framework, and RESTful Web Services. We will analyze the throttling classes included in Django REST framework out of the box. We will follow the necessary steps to configure many throttling policies in Django REST framework. We will work with global and scope-related settings. Then, we will use command-line tools to compose and send many requests to test how the throttling rules work. We will understand versioning classes and we will configure a URL path versioning scheme to allow us to work with two versions of our RESTful Web Service. We will use command-line tools and the Browsable API to understand the differences between the two versions.
Chapter 19, Automating Tests, shows how to automate tests for our RESTful Web Services developed with Django and Django REST framework. We will use different packages, tools, and configurations to perform tests. We will write the first round of unit tests for our RESTful Web Service, run them, and measure tests code coverage. Then, we will analyze tests code coverage reports and write new unit tests to improve the test code coverage. We will understand the new tests code coverage reports and learn the benefits of a good test code coverage.
Chapter 20, Solutions, the right answers for the Test Your Knowledge sections of each chapter are included in the appendix.
Chapter 21, Templates, walks us through Django template language constructs, explaining its design choices, suggests how to organize template files, introduces handy template patterns, and points to several ways Bootstrap can be integrated and customized.
Chapter 22, Admin Interface, focuses on how to use Django's brilliant out-of-the box admin interface more effectively and several ways to customize it, from enhancing the models to toggling feature flags.
Chapter 23, Forms, illustrates the often confusing form workflow, different ways of rendering forms, improving a form's appearance using crispy forms, and various applied form patterns.
Chapter 24, Working Asynchronously, tours various asynchronous solutions for the Django developer, from the feature-rich Celery task queues, Python 3's asyncio, to the brand new Channels, and compares them for you.
Chapter 25, Creating APIs, explains RESTful API design concepts with practical advice on topics such as versioning, error handling, and design patterns using the Django REST framework.
Chapter 26, Security, familiarizes you with various web security threats and their counter measures, specifically looking at how Django can protect you. Finally, a handy security checklist reminds you of the commonly overlooked areas.
Chapter 27, Production-Ready, is a crash course in deploying a public-facing application beginning with choosing your webstack, understanding hosting options, and walking through a typical deployment process. We go into the details of monitoring and performance at this stage.
To get the most out of this book
You will just need a computer (PC or Mac) and internet connectivity to start with. Then, ensure that the following are installed:
Python 3.4 or later
Django 2 or later (will be covered in installation instructions)
Text Editor (or a Python IDE)
Web browser (the latest version, please)
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A Gentle Introduction to Python
According to Wikipedia, computer programming is:
In a nutshell, coding is telling a computer to do something using a language it understands.
Computers are very powerful tools, but unfortunately, they can't think for themselves. They need to be told everything: how to perform a task, how to evaluate a condition to decide which path to follow, how to handle data that comes from a device, such as the network or a disk, and how to react when something unforeseen happens, say, something is broken or missing.
You can code in many different styles and languages. Is it hard? I would say yes and no. It's a bit like writing. Everybody can learn how to write, and you can too. But, what if you wanted to become a poet? Then writing alone is not enough. You have to acquire a whole other set of skills and this will take a longer and greater effort.
In the end, it all comes down to how far you want to go down the road. Coding is not just putting together some instructions that work. It is so much more!
Good code is short, fast, elegant, easy to read and understand, simple, easy to modify and extend, easy to scale and refactor, and easy to test. It takes time to be able to write code that has all these qualities at the same time, but the good news is that you're taking the first step towards it at this very moment by reading this book. And I have no doubt you can do it. Anyone can; in fact, we all program all the time, only we aren't aware of it.
Would you like an example?
Say you want to make instant coffee. You have to get a mug, the instant coffee jar, a teaspoon, water, and the kettle. Even if you're not aware of it, you're evaluating a lot of data. You're making sure that there is water in the kettle and that the kettle is plugged in, that the mug is clean, and that there is enough coffee in the jar. Then, you boil the water and maybe, in the meantime, you put some coffee in the mug. When the water is ready, you pour it into the cup, and stir.
So, how is this programming?
Well, we gathered resources (the kettle, coffee, water, teaspoon, and mug) and we verified some conditions concerning them (the kettle is plugged in, the mug is clean, and there is enough coffee). Then we started two actions (boiling the water and putting coffee in the mug), and when both of them were completed, we finally ended the procedure by pouring water in to the mug and stirring.
Can you see it? I have just described the high-level functionality of a coffee program. It wasn't that hard because this is what the brain does all day long: evaluate conditions, decide to take actions, carry out tasks, repeat some of them, and stop at some point. Clean objects, put them back, and so on.
All you need now is to learn how to deconstruct all those actions you do automatically in real life so that a computer can actually make some sense of them. And you need to learn a language as well, to instruct it.
So this is what this book is for. I'll tell you how to do it and I'll try to do that by means of many simple but focused examples (my favorite kind).
In this chapter, we are going to cover the following:
Python's characteristics and ecosystem
Guidelines on how to get up and running with Python and virtual environments
How to run Python programs
How to organize Python code and Python's execution model
A proper introduction
I love to make references to the real world when I teach coding; I believe they help people retain the concepts better. However, now is the time to be a bit more rigorous and see what coding is from a more technical perspective.
When we write code, we're instructing a computer about the things it has to do. Where does the action happen? In many places: the computer memory, hard drives, network cables, the CPU, and so on. It's a whole world, which most of the time is the representation of a subset of the real world.
If you write a piece of software that allows people to buy clothes online, you will have to represent real people, real clothes, real brands, sizes, and so on and so forth, within the boundaries of a program.
In order to do so, you will need to create and handle objects in the program you're writing. A person can be an object. A car is an object. A pair of socks is an object. Luckily, Python understands objects very well.
The two main features any object has are properties and methods. Let's take a person object as an example. Typically in a computer program, you'll represent people as customers or employees. The properties that you store against them are things like the name, the SSN, the age, if they have a driving license, their email, gender, and so on. In a computer program, you store all the data you need in order to use an object for the purpose you're serving. If you are coding a website to sell clothes, you probably want to store the heights and weights as well as other measures of your customers so that you can suggest the appropriate clothes for them. So, properties are characteristics of an object. We use them all the time: Could you pass me that pen?—Which one?—The black one. Here, we used the black property of a pen to identify it (most likely among a blue and a red one).
Methods are things that an object can do. As a person, I have methods such as speak, walk, sleep, wake up, eat, dream, write, read, and so on. All the things that I can do could be seen as methods of the objects that represent me.
So, now that you know what objects are and that they expose methods that you can run and properties that you can inspect, you're ready to start coding. Coding in fact is simply about managing those objects that live in the subset of the world that we're reproducing in our software. You can create, use, reuse, and delete objects as you please.
According to the Data Model chapter on the official Python documentation (https://docs.python.org/3/reference/datamodel.html):
We'll take a closer look at Python objects in Chapter6, OOP, Decorators, and Iterators. For now, all we need to know is that every object in Python has an ID (or identity), a type, and a value.
Once created, the ID of an object is never changed. It's a unique identifier for it, and it's used behind the scenes by Python to retrieve the object when we want to use it.
The type, as well, never changes. The type tells what operations are supported by the object and the possible values that can be assigned to it.
We'll see Python's most important data types in Chapter 2, Built-in Data Types.
The value can either change or not. If it can, the object is said to be mutable, while when it cannot, the object is said to be immutable.
How do we use an object? We give it a name, of course! When you give an object a name, then you can use the name to retrieve the object and use it.
In a more generic sense, objects such as numbers, strings (text), collections, and so on are associated with a name. Usually, we say that this name is the name of a variable. You can see the variable as being like a box, which you can use to hold data.
So, you have all the objects you need; what now? Well, we need to use them, right? We may want to send them over a network connection or store them in a database. Maybe display them on a web page or write them into a file. In order to do so, we need to react to a user filling in a form, or pressing a button, or opening a web page and performing a search. We react by running our code, evaluating conditions to choose which parts to execute, how many times, and under which circumstances.
And to do all this, basically we need a language. That's what Python is for. Python is the language we'll use together throughout this book to instruct the computer to do something for us.
Now, enough of this theoretical stuff; let's get started.
Enter the Python
Python is the marvelous creation of Guido Van Rossum, a Dutch computer scientist and mathematician who decided to gift the world with a project he was playing around with over Christmas 1989. The language appeared to the public somewhere around 1991, and since then has evolved to be one of the leading programming languages used worldwide today.
I started programming when I was 7 years old, on a Commodore VIC-20, which was later replaced by its bigger brother, the Commodore 64. Its language was BASIC. Later on, I landed on Pascal, Assembly, C, C++, Java, JavaScript, Visual Basic, PHP, ASP, ASP .NET, C#, and other minor languages I cannot even remember, but only when I landed on Python did I finally have that feeling that you have when you find the right couch in the shop. When all of your body parts are yelling, Buy this one! This one is perfect for us!
It took me about a day to get used to it. Its syntax is a bit different from what I was used to, but after getting past that initial feeling of discomfort (like having new shoes), I just fell in love with it. Deeply. Let's see why.
About Python
Before we get into the gory details, let's get a sense of why someone would want to use Python (I would recommend you to read the Python page on Wikipedia to get a more detailed introduction).
To my mind, Python epitomizes the following qualities.
Portability
Python runs everywhere, and porting a program from Linux to Windows or Mac is usually just a matter of fixing paths and settings. Python is designed for portability and it takes care of specific operating system (OS) quirks behind interfaces that shield you from the pain of having to write code tailored to a specific platform.
Coherence
Python is extremely logical and coherent. You can see it was designed by a brilliant computer scientist. Most of the time, you can just guess how a method is called, if you don't know it.
You may not realize how important this is right now, especially if you are at the beginning, but this is a major feature. It means less cluttering in your head, as well as less skimming through the documentation, and less need for mappings in your brain when you code.
Developer productivity
According to Mark Lutz (Learning Python, 5th Edition, O'Reilly Media), a Python program is typically one-fifth to one-third the size of equivalent Java or C++ code. This means the job gets done faster. And faster is good. Faster means a faster response on the market. Less code not only means less code to write, but also less code to read (and professional coders read much more than they write), less code to maintain, to debug, and to refactor.
Another important aspect is that Python runs without the need for lengthy and time-consuming compilation and linkage steps, so you don't have to wait to see the results of your work.
An extensive library
Python has an incredibly wide standard library (it's said to come with batteries included). If that wasn't enough, the Python community all over the world maintains a body of third-party libraries, tailored to specific needs, which you can access freely at the Python Package Index (PyPI). When you code Python and you realize that you need a certain feature, in most cases, there is at least one library where that feature has already been implemented for you.
Software quality
Python is heavily focused on readability, coherence, and quality. The language uniformity allows for high readability and this is crucial nowadays where coding is more of a collective effort than a solo endeavor. Another important aspect of Python is its intrinsic multiparadigm nature. You can use it as a scripting language, but you also can exploit object-oriented, imperative, and functional programming styles. It is versatile.
Software integration
Another important aspect is that Python can be extended and integrated with many other languages, which means that even when a company is using a different language as their mainstream tool, Python can come in and act as a glue agent between complex applications that need to talk to each other in some way. This is kind of an advanced topic, but in the real world, this feature is very important.
Satisfaction and enjoyment
Last, but not least, there is the fun of it! Working with Python is fun. I can code for 8 hours and leave the office happy and satisfied, alien to the struggle other coders have to endure because they use languages that don't provide them with the same amount of well-designed data structures and constructs. Python makes coding fun, no doubt about it. And fun promotes motivation and productivity.
These are the major aspects of why I would recommend Python to everyone. Of course, there are many other technical and advanced features that I could have talked about, but they don't really pertain to an introductory section like this one. They will come up naturally, chapter after chapter, in this book.
What are the drawbacks?
Probably, the only drawback that one could find in Python, which is not due to personal preferences, is its execution speed. Typically, Python is slower than its compiled brothers. The standard implementation of Python produces, when you run an application, a compiled version of the source code called byte code (with the extension .pyc), which is then run by the Python interpreter.
The advantage of this approach is portability, which we pay for with a slowdown due to the fact that Python is not compiled down to machine level as are other languages.
However, Python speed is rarely a problem today, hence its wide use regardless of this suboptimal feature. What happens is that, in real life, hardware cost is no longer a problem, and usually it's easy enough to gain speed by parallelizing tasks. Moreover, many programs spend a great proportion of the time waiting for IO operations to complete; therefore, the raw execution speed is often a secondary factor to the overall performance. When it comes to number crunching though, one can switch to faster Python implementations, such as PyPy, which provides an average five-fold speedup by implementing advanced compilation techniques (check http://pypy.org/ for reference).
When doing data science, you'll most likely find that the libraries that you use with Python, such as Pandas and NumPy, achieve native speed due to the way they are implemented.
If that wasn't a good-enough argument, you can always consider that Python has been used to drive the backend of services such as Spotify and Instagram, where performance is a concern. Nonetheless, Python has done its job perfectly adequately.
Who is using Python today?
Not yet convinced? Let's take a very brief look at the companies that are using Python today: Google, YouTube, Dropbox, Yahoo!, Zope Corporation, Industrial Light & Magic, Walt Disney Feature Animation, Blender 3D, Pixar, NASA, the NSA, Red Hat, Nokia, IBM, Netflix, Yelp, Intel, Cisco, HP, Qualcomm, and JPMorgan Chase, to name just a few.
Even games such as Battlefield 2, Civilization IV, and QuArK are implemented using Python.
Python is used in many different contexts, such as system programming, web programming, GUI applications, gaming and robotics, rapid prototyping, system integration, data science, database applications, and much more. Several prestigious universities have also adopted Python as their main language in computer science courses.
Setting up the environment
Before we talk about installing Python on your system, let me tell you about which Python version I'll be using in this book.
Python 2 versus Python 3
Python comes in two main versions: Python 2, which is the past, and Python 3, which is the present. The two versions, though very similar, are incompatible in some respects.
In the real world, Python 2 is actually quite far from being the past. In short, even though Python 3 has been out since 2008, the transition phase from Version 2 is still far from being over. This is mostly due to the fact that Python 2 is widely used in the industry, and of course, companies aren't so keen on updating their systems just for the sake of updating them, following the if it ain't broke, don't fix it philosophy. You can read all about the transition between the two versions on the web.
Another issue that has hindered the transition is the availability of third-party libraries. Usually, a Python project relies on tens of external libraries, and of course, when you start a new project, you need to be sure that there is already a Version-3-compatible library for any business requirement that may come up. If that's not the case, starting a brand-new project in Python 3 means introducing a potential risk, which many companies are not happy to take.
At the time of writing, though, the majority of the most widely used libraries have been ported to Python 3, and it's quite safe to start a project in Python 3 for most cases. Many of the libraries have been rewritten so that they are compatible with both versions, mostly harnessing the power of the six library (the name comes from the multiplication 2 x 3, due to the porting from Version 2 to 3), which helps introspecting and adapting the behavior according to the version used. According to PEP 373 (https://legacy.python.org/dev/peps/pep-0373/), the end of life (EOL) of Python 2.7 has been set to 2020, and there won't be a Python 2.8, so this is the time when companies that have projects running in Python 2 need to start devising an upgrade strategy to move to Python 3 before it's too late.
On my box (MacBook Pro), this is the latest Python version I have:
>>> import sys
>>> print(sys.version)
3.7.0a3 (default, Jan 27 2018, 00:46:45)
[Clang 9.0.0 (clang-900.0.39.2)]
So you can see that the version is an alpha release of Python 3.7, which will be released in June 2018. The preceding text is a little bit of Python code that I typed into my console. We'll talk about it in a moment.
