Important reminders about the language.
- Assignment by reference and mutable/immutable data types.
- Passing arguments, default values and local variables.
- Class and instance variables.
- Advanced slices and data structures.
- Introspection.
- Advanced elements of control structures: the else clause of “for, while, try/except” statements.
Hands-on work
Optimization: intersection of lists and computing algorithm complexity.
Advanced features
- Advanced use of decorators (generation to consumption, consumer pipeline).
- Decorators and design patterns.
- Closure.
Hands-on work
Chaining data consumers. Subscription to events via decorators.
Advanced Object-Oriented Programming
- The properties.
- Iterators.
- Multiple inheritance and its shortcomings.
- Context managers.
- Abstract base classes and methods (ABCs).
- Metaclasses.
Hands-on work
Implement a metaclass to create singleton classes.
Deployment and quality
- Installing third party libraries (pip, easy_install).
- The Python Package Index (PyPI).
- Packaging your libraries (distutils, setuptools).
- Deploying a standalone environment (virtualenv and buildout).
Hands-on work
Packing a library and putting it on PypI.
Parallelism: Optimizing the performance of your programs
- Profiling your programs with Timeit and cProfile.
- Parallelization: Avoid multithreading and go for multiprocessing.
- Distributed computing with the Celery library.
Hands-on work
Distribute and consolidate (Map Reduce) computing with Celery.
Libraries that help make the language a success
- Scientific computing and statistics with Numpy, Scipy, Matplotlib and Pandas.
- Artificial Intelligence and learning algorithms with Scikit-Learn.
- Finding information in XML files with ElementTree.
- Network: tcp relay with Twisted and SNMP monitoring with PySNMP.
Hands-on work
Extracting information from XML log files, filters and statistics on the collected data and graphical representation using information trends.
4d
- 28h00 
