Approach and Coverage
Explore the methodology and coverage of Python data structures in this lesson. Understand simplified coding conventions, interface implementations, and performance tables focusing on Lists, USets, and SSets. Gain insights into running time analyses and module dependencies essential for efficient data structure usage.
We'll cover the following...
Methodology
The code samples in this course are written in the Python programming language. However, to make the course accessible to readers not familiar with all of Python’s constructs and keywords, the code samples have been simplified. For example, a reader won’t find any of the keywords public, protected, private, or static. A learner also won’t find much discussion about class hierarchies. Which interfaces a particular class implements or which class it extends, if relevant to the discussion, should be clear from the accompanying text.
These conventions should make the code samples understandable by anyone with a background in any of the languages from the ALGOL tradition, including B, C, C++, C#, Objective-C, D, Python, JavaScript, and so on. Readers who want the full details of all implementations are encouraged to look at the Python source code that accompanies this course.
This course mixes mathematical analyses of running times with Python source code for the algorithms being analyzed. This means that some equations contain variables also found in the source code. These variables are typeset consistently, both within the source code and within equations. The most common such variable is the variable ...