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Advanced Python: Best Practices and Design Patterns
Level: Advanced
Rating:
4.57/5 Based on 211 Reviews
This advanced Python training course will expand upon your fundamental Python programming skills to build reliable and stable applications. You will learn to employ design patterns and best practices in Python applications, exploit the OOP features in Python for stable, reliable programs and, create and manage concurrent threads of control. Additionally, learn to generate and consume REST web service requests and responses and increase performance with efficient use of data structures and native code. 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.
Key Features of this Advanced Python Training:
- After-course instructor coaching benefit
- Learning Tree end-of-course exam included
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
Choose the Training Solution That Best Fits Your Individual Needs or Organizational Goals
LIVE, INSTRUCTOR-LED
In Class & Live, Online Training
- 4-day instructor-led training course
- After-course instructor coaching benefit
- Learning Tree end-of-course exam included
TRAINING AT YOUR SITE
Team Training
- Bring this or any training to your organization
- Full - scale program development
- Delivered when, where, and how you want it
- Blended learning models
- Tailored content
- Expert team coaching
Customize Your Team Training Experience
CONTACT USSave More On Training with FlexVouchers – A Unique Training Savings Account
Our FlexVouchers help you lock in your training budgets without having to commit to a traditional 1 voucher = 1 course classroom-only attendance. FlexVouchers expand your purchasing power to modern blended solutions and services that are completely customizable. For details, please call 888-843-8733 or chat live.
In Class & Live, Online Training
Time Zone Legend:
Note: This course runs for 4 Days *
*Events with the Partial Day Event clock icon run longer than normal but provide the convenience of half-day sessions.
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Nov 17 - 20 9:00 AM - 4:30 PM EST Online (AnyWare)
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Dec 15 - 18 9:00 AM - 4:30 PM EST Online (AnyWare)
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Feb 16 - 19 9:00 AM - 4:30 PM EST New York / Online (AnyWare)
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Mar 29 - Apr 1 9:00 AM - 4:30 PM EDT Toronto / Online (AnyWare)
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May 18 - 21 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare)
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Jun 15 - 18 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare)
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Aug 17 - 20 9:00 AM - 4:30 PM EDT New York / Online (AnyWare)
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Guaranteed to Run
When you see the "Guaranteed to Run" icon next to a course event, you can rest assured that your course event — date, time — will run. Guaranteed.Partial Day Event
Learning Tree offers a flexible schedule program. If you cannot attend full day sessions, this option consists of four-hour sessions per day instead of the full-day session.Advanced Python Course Information
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
- Working knowledge of Python programming to the level of:
Software
- Concepts taught are applicable to all Linux distributions
Advanced Python 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
Team Training
Advanced Python Training FAQs
Can I learn Advanced Python Training online?
Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.
Your Training Comes with a 100% Satisfaction Guarantee!*
*Partner-delivered courses may have different terms that apply. Ask for details.