Book Image

Getting Started with Python

By : Fabrizio Romano, Benjamin Baka, Dusty Phillips
Book Image

Getting Started with Python

By: Fabrizio Romano, Benjamin Baka, Dusty Phillips

Overview of this book

This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You’ll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you’ve covered core topics, you’ll understand the joy of unit testing and just how easy it is to create unit tests. By the end of this Learning Path, you will have built components that are easy to understand, debug, and can be used across different applications. This Learning Path includes content from the following Packt products: • Learn Python Programming - Second Edition by Fabrizio Romano • Python Data Structures and Algorithms by Benjamin Baka • Python 3 Object-Oriented Programming by Dusty Phillips
Table of Contents (31 chapters)
Title Page
Copyright and Credits
About Packt
Stacks and Queues
Hashing and Symbol Tables

Algorithm design paradigms

In general, we can discern three broad approaches to algorithm design. They are:

  • Divide and conquer
  • Greedy algorithms
  • Dynamic programming

As the name suggests, the divide and conquer paradigm involves breaking a problem into smaller sub problems, and then in some way combining the results to obtain a global solution. This is a very common and natural problem solving technique, and is, arguably, the most commonly used approach to algorithm design.

Greedy algorithms often involve optimization and combinatorial problems; the classic example is applying it to the traveling salesperson problem, where a greedy approach always chooses the closest destination first. This shortest path strategy involves finding the best solution to a local problem in the hope that this will lead to a global solution.

The dynamic programming approach is useful when our sub problems overlap. This is different from divide and conquer. Rather than break our problem into independent sub problems,...