What customers say ...
[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. ..
Dorota Jarecka, University of Warsaw, Poland about the course "Python for Scientists and Engineers" more...
Dr. Müller is (a) very good teacher .. (I) would highly recommend this course and also Dr. Müller for this course.
Dhiraj Surve, Suzlon.com about the course "Python for Programmers" more...
I really liked the training. It matched the expectations of a student. This training is amazing.
Deepti about the tutorial "Iterators, Generators and Decorators at EuroPython 2014" more...
We had a wide range of Python experience in our group and each person gained something valuable to take away....
Dr. Ryan Woodard, Chair of Entrepreneurial Risks, ETH ETH Zurich, Switzerland more...
I enjoyed the course very much and learned a lot. My interest for quite few of topics was ignited during the course and I will into more details. I understood many principles. All in all: Very good training! Thank you very much all the best.
Dominik Schwinn, German Aerospace Stuttgart about the course "Python für Programmierer und Python für Wissenschaftler und Ingenieure" more...
Dates for Open Courses
|Leipzig||January 23 - 25, 2017||Advanced Python||English register|
Course also available as in-house training. Please ask us at firstname.lastname@example.org
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.
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.
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.
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.
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 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 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.
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
- duck typing
- monkey patching
"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.
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 can be used instead of the type None to avoid tests for None. Implementation, usage as well as advantages and disadvantages are covered.
Proxies stand for other objects. Setup and usage of proxies are covered.
The observer pattern allows several objects to have access to the same data. The principles of this pattern are shown with a comprehensive example.
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.
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.
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.
Every participant receives comprehensive printed materials that cover the whole course content as wells a CD with all source codes and used software.
The Python Academy is sponsor of PyCon Ireland 2016.
The Python Academy is sponsor of EuroSciPy 2016.
The Python Academy is sponsor of PyCon US 2016.
The Python Academy is sponsor of PyData Berlin 2016.
The Python Academy is sponsor of PyCon Sweden 2016.
The Python Academy is sponsor of Python Unconference 2015.
The Python Academy is sponsor of EuroSciPy 2015.
The Python Academy is sponsor of EuroPython 2015.
The Python Academy is sponsor of PyData Berlin 2015.
The Python Academy is sponsor of PyCon Montréal 2015.
The Python Academy is sponsor of Python BarCamp Köln 2015.
The Python Academy is sponsor of Chemnitzer Linux-Tage 2015.
The Python Academy is sponsor of Django Girls Wroclaw 2015.
The Python Academy is sponsor of PyCon Ireland 2014.
The Python Academy is sponsor of EuroSciPy 2014.
The Python Academy is sponsor of PyData London 2014.
The Python Academy is sponsor of EuroPython 2014.
The Python Academy is sponsor of PyCon 2014 Montréal.
The Python Academy is sponsor of Python BarCamp Köln 2014.
The Python Academy is sponsor of PyConDE 2013.
The Python Academy is sponsor of EuroPython 2013.
The Python Academy is sponsor of PyCon US 2013.
The Python Academy is sponsor of EuroSciPy 2013.
The Python Academy is sponsor of PyConPL 2012.
The next open cousers
Python Academy sponsors EuroPython conference 2013
Python Academy sponsors EuroSciPy conference 2013
Python Academy sponsors Python BarCamp in Cologne
Next Meeting of Leipzig Python User Group, November 12, 2013
Introduction to Django, November 11 - 13, 2013
Professional Testing with Python, November 25 - 27, 2013
Advanced Django, January 13 - 15, 2014
Python Academy sponsors PyCon US conference 2013
Python Academy founder receives PSF Community Service Award