Python Review

• A brief review of Python basics including syntax, core datatypes, file I/O, functions, error handling, and classes.

Classes and Objects

• An introduction to creating and using user-defined objects in Python.
• Describes how to use the class statement to create new objects and presents details on various special methods that can be defined to customize object behavior.
• Commonly used object oriented programming techniques are also presented.

Advanced I/O Handling

• An in-depth examination of the Python I/O system including text handling, binary data handling, and different I/O models such as blocking, non-blocking, and event-driven I/O.

Idiomatic Data Handling

• An introduction to various tools and techniques for effective data processing. o Discusses different options for creating data structures and gives an inside look at how the built-in datatypes are put together along with their performance and memory usage properties.
• Participants will learn how to apply list, set, and dictionary comprehensions to various problems in data handling.

Inside the Python Object Model

• A detailed tour of how the Python object system is implemented.
• Topics include the definition of objects, object representation, attribute binding, inheritance, descriptors, properties, slots, private attributes, static methods, and class methods.
• Cover important details concerning Python memory management and garbage collection.

Testing, Debugging and Logging

• Coverage of how to test and debug Python programs with a focus on three major topics.
• First, testing Python programs with the doctest and unittest modules is described.
• Next, the Python Debugger and profiler are presented. o Finally, the logging package is described.

Packages and Distribution

• How to organize Python code into packages and how to distribute packages to users and programmers.
• Covers the underlying mechanics of how packages are put together and the distutils module for creating distributions.
• Also covers more advanced packaging options such as Distribute and setuptools.

Iterators, Generators, Coroutines

• The section starts with a description of the iteration protocol and moves on to practical use of generators and coroutines.
• A major focus on this section is on the use of generators and coroutines to set up processing pipelines, much like pipes in Unix programming.
• You will see how generators and coroutines can lead to very elegant programming abstractions for processing data and how such programs can be used to process huge datafiles and streaming I/O.

Functional Programming

• Advanced details of how to program with functions in Python.
• Discusses more advanced features of functions including variadic parameters, nested functions, closures, lazy evaluation, anonymousfunctions (lambda), and function attributes.

Metaprogramming

• Loosely defined, metaprogramming refers to programs that are able to manipulate their own program structure (functions, classes, etc.) or the structure of other programs as data.
• This section introduces and covers practical examples of Python’s metaprogramming features including function decorators, class decorators, metaclasses, and context managers.
• A major emphasis of this section is to understand how advanced programming frameworks utilize these features to provide a richer programming environment for their end users.

Extension Programming

• How to create C and C++ extensions to Python. Covers the absolute basics of the Python C API followed by some details on using the ctypes library and Swig code generator.
• A major focus of this section is on how to organize extension code so that it can more seamlessly integrate with the Python environment.
• Topics include memory management, data handling, encapsulation, and common pitfalls.

Concurrent Programming with Threads

• An introduction to programming with Python threads.
• Starts with the basics of using the threading library and dives into a variety of more advanced topics including a survey of how and when to use the different thread synchronization primitives, queues, deadlock avoidance, and thread debugging.
• Also includes detailed information on how the Python interpreter executes programs and properties of the global interpreter lock (GIL).

Multiprocessing

• An introduction to the multiprocessing library that allows programs to distribute their work across independent processes or machines.
• Covers processes, queues, pipes, connections and process pools.