Expand upon your fundamental Python programming skills to build reliable and stable applications. In this training course, you learn to implement Gang of Four (GoF) design patterns in order to solve commonly recurring, real-world software design programs, thereby avoiding pitfalls and greatly improving the effectiveness of your programming efforts.
Select specific date to see price, venue and full details.
Learning Objectives
You Will Learn How To
- Employ design patterns and best practices in Python applications
- Unit test, debug, and instal Python programs and modules
- Profile program execution and improve performance
- Apply advanced Python programming features for efficient, reliable, maintainable programs
Pre-Requisites
Requirements:
- Working knowledge of Python programming to the level of:
- Course, Python Programming Introduction, or at least three to six months of Python programming experience
Software:
- Concepts taught are applicable to all Linux distributions
Course Content
Course Outline
Object-Oriented Programming in Python
- Extending classes to define subclasses
- Inheriting from multiple superclasses and mix-in classes
- Adding properties to a class
- Defining abstract base classes
Exploring Python Features
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
Verifying Code and Unit Testing
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
Detecting Errors and Debugging Techniques
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
Implementing Python Design Patterns
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
Interfacing with REST Web Services and Clients
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 and Improving Application Performance
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
Installing and Distributing Modules
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
Concurrent Execution
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
Exams & Certification
End of course exam.
Related Courses
- Applied Data Science with Python and Jupyter
- Building Intelligent Recommender Systems
- Building Transformer-based Natural Language Processing Applications
- Data Analysis with Python
- Fundamentals of Accelerated Computing with CUDA Python
- Fundamentals of Deep Learning
- Fundamentals of Deep Learning for Natural Language Processing
