Log On/Register  

855.838.5028

Advanced Python: Best Practices and Design Patterns

Duration: 4 Days
Course Price: $2,990

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 common, real-world software design programs, as well as apply proven solutions to commonly recurring problems — thereby avoiding pitfalls and greatly improving the effectiveness of your programming efforts.

You Will Learn How To

  • Employ design patterns and best practices in Python applications
  • Unit test, debug, and install Python programs and modules
  • Profile program execution and improve performance
  • Apply advanced Python programming features for efficient, reliable, maintainable programs

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 common, real-world software design programs, as well as apply proven solutions to commonly recurring problems — thereby avoiding pitfalls and greatly improving the effectiveness of your programming efforts.

You Will Learn How To

  • Employ design patterns and best practices in Python applications
  • Unit test, debug, and install Python programs and modules
  • Profile program execution and improve performance
  • Apply advanced Python programming features for efficient, reliable, maintainable programs

Requirements:

  • Working knowledge of Python programming to the level of:
    • Course 1905, Python Programming Introduction, or at least three to six months of Python programming experience

Software:

  • Concepts taught are applicable to all Linux distributions

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

  • Customizing 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 behavior

  • Mocking dependent objects with the Mock package
  • Asserting correct code behavior 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

Behavioral patterns

  • Utilizing 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
  • Synchronizing threads using locks

Heavy-weight processes

  • Launching operating system commands as subprocesses
  • Synchronizing processes with queues
  • Parallelizing execution using process pools and Executors
Learn More
Please type the letters below so we know you are not a robot (upper or lower case):