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[The trainer] knows well what scientists need, so his hints are very practical and valuable. The hands-on course [..] covers a wide range of examples and will be very helpful in my daily work. ..

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Good course. Very competent trainer for this introduction. The course offers wide spectrum of topics and goes into depth were participants need it most.

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Highly recommended. Many aha-experiences and took home many positive inspiratons.

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Dr. Müller is (a) very good teacher .. (I) would highly recommend this course and also Dr. Müller for this course.

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Advanced Python

Dates for Open Courses

Location	Date	Course	Language
Leipzig	January 28 - 30, 2019	Advanced Python	English register

Course also available as in-house training. Please ask us at info@python-academy.de

Target Audience

This course targets medium level Python programmers who would like to dive deeper into the language. Alternatively, participants can attend the course Python for Programmers to be able to take full advantage of this advanced course.

Motivation

The Python programming language is relatively easy to learn and allows to solve real-world problem with a just a few concepts.

But it also offers several advanced features that can help to greatly improve the programming experience. The latest releases of Python 2.x and 3.x add interesting features that can be used passively without deeper understanding about how they work. The course teaches how these features work and provides details about meta-programming and other advanced techniques.

Content

Comprehensions

The principle comes from the functional language Haskell but integrates very well into Python. After list comprehension came generator expressions followed by dictionary and set comprehensions.

The course introduces this style of programming with examples focusing on advantages and disadvantages for certain tasks.

Iterators and Generators

Iterators and generator make lazy evaluation, that is generating an object just when it is needed, very convenient. The concept of yielding instead of returning plays a central role. The course shows how to use generators to simplify programming tasks. Furthermore, coroutines will be used to implement concurrent solutions. An overview over the itertools module shows how to elegantly solve iteration tasks.

Decorators

Decorator provide a very useful method to add functionality to existing functions and classes. The course uses examples for caching, proxying, and checking of arguments to demonstrate how decorators can improve code readability and can simplify solutions.

Context Managers

The with statement helps to make code more robust by simplifying exception handling. The course shows how to use the with statement with the standard library and how to write your own objects that take advantage of with. The contextlib from the standard library helps to make this easier.

Descriptors

Descriptors determine how attribute of object are accessed. The course uses examples to show how descriptors work and how they can be used to customize attribute access.

Metaclasses

Metaclasses offer a powerful way to change how classes in Python behave. Whíle being an advanced feature that should be used sparingly, it can provide interesting help for complex problems. The course shows how to apply metaclasses and gives examples where they can be useful.

Conventions

Python offers a lot of functionality out of the box where other languages need to use design patterns. These patterns are general solutions for certain types of problems.

Python offers what is called the "pythonic" way for solving a problem. The course presents of a few of these solutions:

wrapping instead of inheritance
dependency injections
factories
duck typing
monkey patching
callbacks

Patterns

"It's easier to ask for forgiveness than permission (EFAP)"

One pythonic principle is "It's easier to ask for forgiveness than permission (EFAP)". Opposed to the approach to look before you leap, this principle states that you should first try an action and if it fails react appropriately. Python' strong exception handling supports this principle and helps to develop robust and fault tolerant programs.

Singleton

Singletons are objects of which only one instance is supposed to exist. Python provides several ways to implement singletons. These possibilities are shown using examples.

Null Objects

Null objects can be used instead of the type None to avoid tests for None. Implementation, usage as well as advantages and disadvantages are covered.

Proxy

Proxies stand for other objects. Setup and usage of proxies are covered.

Observer

The observer pattern allows several objects to have access to the same data. The principles of this pattern are shown with a comprehensive example.

Constructor

Parameters of constructors are often assigned to instance variables. This pattern can replace a many lines of manual assignment with only one line of code.

Good Style

Python is often described as an elegant language. Consistency is contributing to this. There are several recommendations and tools that help to check for them. The course has a closer look at the Python style guide (PEP8) and uses PyLint and pep8.py with examples. The participants are encouraged to bring their source code for style analysis.

Course Duration

3 days

Exercises

The participants can follow all steps directly on their computers. There are exercises at the end of each unit providing ample opportunity to apply the freshly learned knowledge.