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Advanced Python: Best Practices and Design Patterns

COURSE TYPE

Advanced

Course Number

1906

Duration

4 Days

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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 Python in order to solve commonly recurring, real-world software design programs, thereby avoiding pitfalls and greatly improving the effectiveness of your programming efforts. In addition, you’ll boost your Python proficiency with best practices in Object-Oriented Programming, testing, debugging, measuring and improving application performance, and developing RESTful services.

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

Important Course Information

Requirements

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

    Software

  • Concepts taught are applicable to all Linux distributions on Windows, UNIX, Linus, and Mac OS. 
  • 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

    • 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
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    Tuition due 30 days after you attend your course.

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    Tuition — Standard: $3285   Government: $2890

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    Tuition

    Standard

    Government

    In Classroom or
    Online

    Standard

    $3285

    Government

    $2890

    Private Team Training

    Contact Us »

    Course Tuition Includes:

    After-Course Instructor Coaching
    When you return to work, you are entitled to schedule a free coaching session with your instructor for help and guidance as you apply your new skills.

    After-Course Computing Sandbox
    You'll be given remote access to a preconfigured virtual machine for you to redo your hands-on exercises, develop/test new code, and experiment with the same software used in your course.

    Free Course Exam
    You can take your Learning Tree course exam on the last day of your course or online any time after class.

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    Training Hours

    Standard Course Hours: 9:00 am – 4:30 pm
    *Informal discussion with instructor about your projects or areas of special interest: 4:30 pm – 5:30 pm

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