This is the online textbook for the Oxford course on Python in Scientific Computing, offered to MSc students on the MSc in Mathematical Modelling and Scientific Computing, and to DPhil students. The lecturer for this course is Patrick E. Farrell. Please address all questions about the course to Prof. Farrell.

This course is a fork of the MATH50009 Principles of Programming course prepared by David A. Ham, offered to mathematics undergraduates at Imperial College London. Prof. Farrell is very grateful to Dr Ham for his kind and generous assistance in this matter. The videos recorded by Dr Ham sometimes refer to Imperial-specific matters such as assessments; please ignore these.

A suggested timetable for Oxford students might be the following:

Table 1 Chapters per week

Week of term	Chapters
Week 1	Chapters 1 and 2
Week 2	Chapter 3
Week 3	Chapter 4
Week 4	Chapter 5
Week 5	Chapter 6
Week 6	Chapter 7
Week 7	Chapter 8
Week 8	Chapter 9

but of course you can go through the course at your own pace, as suits your other assignments.

This website builds on the book Object-oriented Programming in Python for Mathematicians, written by Dr Ham. Here you’ll find the full hyperlinked book text, as well as consolidated lists of the accompanying videos.

To do the exercises, find the GitHub Classroom links on the index of exercises.

The complete book text is available on this website. However, if you’d like to have a physical copy, then please buy the book.

• Preface
  • What is this book for?
  • Who is this book for?
  • How to use this book
  • Conventions employed
  • Acknowledgements
  • Changes in the second edition

• 1. Introduction: abstraction in mathematics and programming
  • 1.1. Obtaining the right software tools
  • 1.2. Setting up a Python virtual environment
  • 1.3. Installing Python packages
  • 1.4. Glossary
• 2. Programs in files
  • 2.1. The Python interpreter
  • 2.2. Python scripts and text editors
  • 2.3. Modules
  • 2.4. Packages
  • 2.5. Testing frameworks
  • 2.6. Writing code to a specified interface
  • 2.7. Glossary
  • 2.8. Exercises
• 3. Objects and abstraction
  • 3.1. Abstraction in action
  • 3.2. Types
  • 3.3. Defining new types
  • 3.4. Encapsulation
  • 3.5. Assignment and instantiation
  • 3.6. Glossary
  • 3.7. Exercises
• 4. A matter of style
  • 4.1. The value of convention
  • 4.2. PEP 8
  • 4.3. Code layout
  • 4.4. Names
  • 4.5. Parsimony
  • 4.6. Comments
  • 4.7. Docstrings
  • 4.8. A brief diversion into cellular automata
  • 4.9. Glossary
  • 4.10. Exercises
• 5. Abstract data types
  • 5.1. Stacks
  • 5.2. Separation of concerns
  • 5.3. Algorithmic complexity
  • 5.4. Queues and deques
  • 5.5. Linked lists
  • 5.6. The iterator protocol
  • 5.7. Other abstract data types
  • 5.8. Glossary
  • 5.9. Exercises
• 6. Errors and exceptions
  • 6.1. What is an error?
  • 6.2. Exceptions
  • 6.3. Tracebacks: finding errors
  • 6.4. Raising exceptions
  • 6.5. Handling exceptions
  • 6.6. Exceptions are not always errors
  • 6.7. Glossary
  • 6.8. Exercises
• 7. Inheritance and composition
  • 7.1. An example from group theory
  • 7.2. Inheritance
  • 7.3. Calling parent class methods
  • 7.4. Creating new exception classes
  • 7.5. Glossary
  • 7.6. Exercises
• 8. Debugging and testing
  • 8.1. Pandas
  • 8.2. Debuggers
  • 8.3. Using a graphical debugger
  • 8.4. Invoking a command-line debugger
  • 8.5. Command-line debugger commands
  • 8.6. Debugging strategy
  • 8.7. Debugging tactics
  • 8.8. Glossary
  • 8.9. Exercises
• 9. Trees and directed acyclic graphs
  • 9.1. The splat and double splat operators
  • 9.2. Graph and tree definitions
  • 9.3. Data structures for trees
  • 9.4. Expression trees
  • 9.5. Avoiding recursion
  • 9.6. Representing expressions as DAGs
  • 9.7. Differentiation as an expression visitor
  • 9.8. Glossary
  • 9.9. Exercises
• 10. Assessment
  • 10.1. Tips

• 1. Getting help
  • 1.1. Writing an issue report
  • 1.2. Title
  • 1.3. Steps to reproduce
  • 1.4. Observed result
  • 1.5. Expected result
  • 1.6. Any other information
  • 1.7. Issue pitfalls
  • 1.8. An example issue report
• 2. Just enough Git to get by
  • 2.1. What is Git?
  • 2.2. What is GitHub?
  • 2.3. What is GitHub Classroom?
  • 2.4. Configuring Git
  • 2.5. Signing up to GitHub
  • 2.6. Doing exercises using GitHub or GitHub Classroom
• 3. Further object-oriented features
  • 3.1. Decorators
  • 3.2. Abstract base classes
  • 3.3. Glossary
  • 3.4. Exercises