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
Idiomatic Data Handling
- An introduction to various tools and techniques for effective data processing.
- Discusses different options for creating data structures and gives an inside look at how the built-in data types are put together along with their performance and memory usage properties.
- Students 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
Packages and Distribution
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 data files and streaming I/O.
- 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, met classes, 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.
- 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 c types 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).
Prev This section introduces and covers practical examples of Python’s metaprogramming features including function decorators, class decorators, met classes, and context managers.