Advanced Python for Ecologists and Evolutionary Biologists

Session 3 – 03:00:00 – L Classes and Objects

This session introduces the fundamentals of object-oriented programming in Python, explaining how commonly used data types are implemented as classes. Participants will learn how to define custom classes and shift from an imperative to an object-oriented approach. The session also explores when object-oriented design is appropriate. In the practical component, participants will create simple classes to solve biological problems and gain experience structuring code into reusable components.

Key Topics:
Classes, instances, methods vs. functions, self, constructors, magic methods, library vs. client code.

Session 4 – 03:00:00 – Object-Oriented Programming

Building on the previous session, this session covers advanced object-oriented programming concepts common across many languages. Key ideas such as inheritance, method overriding, and composition are introduced with a focus on their practical use and implementation in Python. Participants will also explore design considerations when structuring object-oriented code. The practical component involves developing a simulation using multiple interacting classes.

Key Topics:
Inheritance, class hierarchies, method overriding, superclasses and subclasses, polymorphism, composition, multiple inheritance.

Session 5 – 03:00:00 – Functional Programming in Python

This session introduces key concepts of functional programming and contrasts them with object-oriented approaches, focusing on how each style supports different design goals. Topics include state and mutability, side effects, and first-class functions. Participants will explore Python’s built-in functional tools and learn how to create their own. The session concludes with a brief look at how functional techniques can simplify parallel programming. The practical component includes hands-on use of functional tools and custom function development.

Key Topics:
State and mutability, side effects, first-class functions, higher-order functions, declarative programming, lazy evaluation, parallelism.

Session 6 – 03:00:00 – Exception Handling

This session covers the principles of handling runtime errors in Python. It begins by distinguishing syntax errors from exceptions and introduces the use of try, except, else, and finally blocks. Participants will explore how exceptions can propagate through a program and how this affects design decisions. The session also includes guidance on using built-in exception types and defining custom exceptions for specific error cases. The practical involves updating code to incorporate structured error handling.

Key Topics:
Exception classes, try/except syntax, exception propagation, context managers, defining and raising exceptions.

Session 7- 03:00:00 – Performance Optimization

This session introduces tools for measuring and improving the performance of Python code. Participants will learn to benchmark runtime and memory usage, profile code to identify bottlenecks, and apply targeted optimizations. Different tools will be compared based on their strengths in various scenarios. The practical component involves analyzing real code, measuring performance, and making improvements.

Key Topics:
Benchmarking, function and line profiling, timing, profiler overhead, performance tuning strategies.

Session 8- 03:00:00 – Unit Testing

This session introduces the purpose and benefits of testing in software development. Participants will use Python’s built-in testing tools to explore various testing scenarios, then implement the same tests using the pytest framework to compare usability and readability. The session also covers specialized tests and how code structure can affect testability. In the practical, participants will build and run test suites for existing code.

Key Topics:
Test design, built-in testing tools, pytest framework, writing test cases, test suites, testing and program design.