Book Image

Applied Computational Thinking with Python

By : Sofía De Jesús, Dayrene Martinez
Book Image

Applied Computational Thinking with Python

By: Sofía De Jesús, Dayrene Martinez

Overview of this book

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
Table of Contents (21 chapters)
Section 1: Introduction to Computational Thinking
Section 2:Applying Python and Computational Thinking
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
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Working with dictionaries and lists

Before we get too deep into dictionaries and lists, it's important to note that Python does not contain arrays in the built-in functions. We can use lists and perform a lot of the traditional functions on lists that we'd use for arrays. However, for more robust functionalities with arrays, a library is needed, such as NumPy.

Python has four collection data types, which are as follows:

  • Lists: Ordered and changeable; can have duplicates
  • Tuples: Ordered and unchangeable; can have duplicates
  • Sets: Unordered and unindexed; cannot have duplicates
  • Dictionaries: Unordered, changeable, and indexed; cannot have duplicates

As noted, we won't be going into NumPy libraries or other data libraries just yet. For now, we're going to focus on dictionaries and lists.

Defining and using dictionaries

You may recall that we used dictionaries in Chapter 3, Understanding Algorithms and Algorithmic Thinking, when...