Advanced Python: Best Practices and Design Patterns

• Extending classes to define subclasses
• Inheriting from multiple superclasses and mix-in classes
• Adding properties to a class
• Defining abstract base classes

Writing "Pythonic" code

• Customising iteration and indexing with "magic" methods
• Modifying code dynamically with monkey patching

Handling Exceptions

• Raising user-defined exceptions
• Reducing code complexity with context managers and the "with" statement

Testing best practices

• Developing and running Python unit tests
• Simplifying automated testing with the Nose package

Verifying code behaviour

• Mocking dependent objects with the Mock package
• Asserting correct code behaviour with MagicMock

Identifying errors

• Logging messages for auditing and debugging
• Checking your code for potential bugs with Pylint

Debugging Python code

• Extracting error information from exceptions
• Tracing program execution with the PyCharm IDE

Structural patterns

• Implementing the Decorator pattern using @decorator
• Controlling access to an object with the Proxy pattern

Behavioural patterns

• Utilising the Iterator pattern with Python generators
• Laying out a skeleton algorithm in the Template Method pattern
• Enabling loose coupling between classes with the Observer pattern

Python REST web services

• Building a REST service
• Generating JSON responses to support Ajax clients

Python REST clients

• Sending REST requests from a Python client
• Consuming JSON and XML response data

Measuring Application Execution

• Timing execution of functions with the "timeit" module
• Profiling program execution using "cProfile"
• Manipulating an execution profile interactively with "pstats"

Employing Python language features for performance

• Efficiently applying data structures, including lists, dictionaries and tuples
• Mapping and filtering data sets using comprehensions
• Replacing the standard Python interpreter with PyPy

Managing module versions

• Installing modules from the PyPi repository using "pip"
• Porting code between Python versions

Packaging Python modules and applications

• Establishing isolated Python environments with "virtualenv"
• Building a distribution package with "setuptools"
• Uploading your Python modules to a local repository

Lightweight threads

• Creating and managing multiple threads of control with the Thread class
• Synchronising threads using locks

Heavy-weight processes

• Launching operating system commands as subprocesses
• Synchronising processes with queues
• Parallelising execution using process pools and Executors