Coffee Break Python Slicing
Coffee Break Python Slicing
24 Workouts to Master Slicing in Python, Once and for All
About the Book
Why do you waste your time PASSIVELY reading Python books?
Puzzle-based learning is an ACTIVE learning technique. With code puzzles, you will learn faster, smarter, and better.
The Coffee Break Python book series teaches you Python in byte-sized code puzzles. You solve a code puzzle a day while you enjoy your morning coffee. It’s that simple.
What do others have to say?
21,000 online Python students have already improved their coding skills with our unique puzzle-based learning technique:
"I very much enjoy your Finxter.com website because it has some real meat to the problems. Thank you so much for doing this project! I love it!"
"Your site is awesome."
"I found Finxter.com an excellent tool to brush up on my Python skills. I totally love the setup of playing against the questions - such a wonderful idea <3 ... and great questions."
Why should you read this book?
Coffee Break Python Slicing is all about growing your Python expertise—one coffee at a time. The focus lies on the important slicing technique to access consecutive data ranges. Understanding slicing thoroughly is crucial for your success as a Python developer.
This book teaches you everything you need to know about slicing in Python.
As a bonus, you will track your individual Python coding skill level throughout the book.
Who should read this book?
You are slightly beyond beginner-level in Python. For example,
- You have already experience with another programming language—it’s time to tackle Python.
- You are a professional engineer and want to brush up your Python skills.
- You are a student and need to get better at Python for academic courses.
- You have read the first book of our Coffee Break Python series.
So how do you spend your Coffee Break? Python!
2 How to Use This Book
2.1 What Is a Code Puzzle?
2.2 Why Puzzle-based Learning?
2.3 How to Measure Your Python Skills with This Book?
3 A Quick Overview of the Python Language 17
3.2 Basic Data Types
3.3 Complex Data Types
3.5 Functions and Tricks
4 Introduction Slicing
4.1 Intermezzo: Indexing Basics
4.3 The Step Size in Slicing
4.4 Overshooting Indices in Slicing
4.5 Summary Basic Python Slicing
4.6 Frequently Asked Questions
5 Code Puzzles Slicing
6 Final Remarks
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