Python Master Classes

with David Beazley
Author of the Python Cookbook, 3rd Ed
Python Essential Reference, 4th Ed.
Dabeaz, LLC
5412 N Clark Street #218
Chicago, IL 60640
Follow dabeazllc on Twitter Subscribe to Newsletter

Target Audience:

This class is for programmers who are already experienced with another programming language such as C, C++, Java, Perl, PHP, etc. Some prior experience with Python is recommended, but not required as long as you are familiar with common programming concepts. [ Am I prepared? ]

Next Course Date:

  • December 11-15, 2017

Instructor: David Beazley

Price: $2750

What's Included?

  • Electronic copy of all course materials.
  • A copy of the "Python Essential Reference, 4th Ed."
  • Breakfast and lunch at local restaurants
  • Snacks
[ Register | More Information |FAQ]

Practical Python Programming

[4.5 days] This course is a fast-paced introduction to the Python programming language and its use for writing useful programs related to manipulating data, automating repetitive tasks, and scripting. Major topics include the use of Python's core data types (tuples, lists, sets, dicts, etc.) as well as how to organize code into functions, modules, and classes. Popular standard library modules and third-party extensions such as numpy and pandas are also described. Includes more than 50 hands-on exercises.

Major topics include:

  • An introduction to python
  • Working with data and collections
  • Program organization and functions
  • Modules and libraries
  • Classes and objects
  • Testing, debugging, and profiling
  • Iterators and generators
  • Text processing and parsing
  • Files and the file system
  • Accessing web services via Python
  • Parsing common data formats (XML, JSON, CSV, etc.)

Detailed Course Outline

  1. Introduction to Python. An introduction to the Python programming language. Covers details of how to start and stop the interpreter and write programs. Introduces Python's basic datatypes, files, functions, and error handling.
  2. Working with Data. A detailed tour of how to represent and work with data in Python. Covers tuples, lists, dictionaries, and sets. Students will also learn how to effectively use Python's very powerful list processing primitives such as list comprehensions. Finally, this section covers critical aspects of Python's underlying object model including variables, reference counting, copying, and type checking.
  3. Program Organization and Functions. More information about how to organize larger programs into functions and modules. A major focus of this section is on how to design functions that are reliable and can be easily reused in other settings. The organization of code into modules and some best practices for writing scripts is also covered.
  4. Classes and Objects. An introduction to object-oriented programming in Python. Describes how to create new objects, overload operators, and utilize Python special methods. Also covers some basic principles of object oriented programming including inheritance.
  5. Inside the Python Object Model. A detailed look at how objects are implemented in Python. Major topics include object representation, attribute binding, inheritance, memory management, and special properties of classes including properties, slots, and private attributes.
  6. Generators. Covers the iteration protocol, generators, and generator expressions. A major focus of this section concerns the use of generators to set up data processing pipelines--a particularly effective technique for addressing a wide variety of common systems programming problems (e.g., processing large datafiles, handling infinite data streams, etc.).
  7. Some Advanced Topics. A variety of more advanced programming topics including variable argument functions, anonymous functions (lambda), closures, decorators, static and class methods.
  8. Testing, Debugging, and Software Development Practice. This section discusses many isses that are considered important to Python software development. This includes effective use of documentation strings, program testing using both the doctest and unittest modules, and effective use of assertions. The Python logging, debugging, and profiling modules are also described.
  9. Packages. A discussion of how to organize larger Python projects into packages as well as details related to the installation of third party packages.

Course Materials

Students will receive a bound 300-page fully indexed set of lecture notes along with a complete set of more than 50 class exercises (distributed electronically). All class exercises come with solution code for later study and for use during the class.

Copyright (C) 2009-2017, Dabeaz LLC. All Rights Reserved.