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

Binary search

A binary search is a search strategy used to find elements within a list by consistently reducing the amount of data to be searched and thereby increasing the rate at which the search term is found.

To use a binary search algorithm, the list to be operated on must have already been sorted.

The binary term carries a number of meanings and helps us put our minds in the right frame to understand the algorithm.

A binary decision has to be made at each attempt to find an item in the list. One critical decision is to guess which part of the list is likely to house the item we are looking for. Would the search term be in the first half of second half of the list, that is, if we always perceive the list as being comprised of two parts?

Instead of moving from one cell of the list to the other, if we employ the use of an educated guessing strategy, we are likely to arrive at the position where the item will be found much faster.

As an example, lets take it that we want to find the middle...