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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Debugging algorithms

There is a debugger we can run in Python using the breakpoint() function (which is built-in). We can introduce this code into our program and insert it where we are unsure of our code. Adding breakpoint() will then check for bugs and errors. When we run a breakpoint() function, we'll get a pdb output, which stands for Python Debugger. As a note, this built-in function appears in Python 3.7 and newer versions. The previous debugger for Python 3.6 and older was pdb.set_trace().

When we run the debugger, we can use four commands:

  • c: Continues the execution
  • q: Quits the debugger/execution
  • n: Steps to the next line within the function
  • s: Steps to the next line in this function or a called function

Let's take a look at a code and run each of the commands outlined:

number = 5
number2 = 'five'

Looking at this code, you can see the breakpoint() command after...