Introduction to Python for Ecologists and Evolutionary Biologists

Session 3 – 03:00:00 – Lists and Loops
This session focuses on writing programs that handle arbitrary amounts of data—an essential capability for research applications. Participants are introduced to lists for storing collections of values and loops for processing them. Together, these tools enable more practical and scalable programming.

The session covers the syntax and structure needed to use lists and loops effectively, highlighting similarities between lists, strings, and files to promote concise coding. The practical component involves solving problems that work with larger data files.

Key Topics:
Lists and arrays, blocks and indentation, variable scope, iteration and the iteration interface, ranges.

Session 4 – 03:00:00 – Conditions and Flow Control
This session introduces conditional logic as a foundation for decision-making in programs. Participants learn how to build and combine conditions to control program flow, with an emphasis on clarity and readability.

By integrating these ideas with earlier material, students gain the tools to write programs that apply rules and enforce logic. The practical component includes complex filtering tasks using structured CSV data.

Key Topics:
Truth and falsehood, Boolean logic, identity vs. equality, statement evaluation, branching and flow control.

Session 5 – 03:00:00 – Organizing and Structuring Code
This session focuses on creating custom functions to extend Python’s capabilities. Participants learn how to write functions effectively, use named arguments and default values, and follow best practices for usability and readability.

The session also introduces the basics of automated testing, including how to write and run unit tests. During the practical component, students develop functions designed to pass a structured set of tests.

Key Topics:
Argument passing, function design, encapsulation, data flow, named arguments, default values, unit testing.

Session 6 – 03:00:00 – The Python Standard Library and Regular Expressions
This session explores the Python standard library, situating it within the broader Python ecosystem and comparing it with other libraries. The focus is on understanding Python’s module system through an in-depth look at the regular expressions (regex) module.

Participants learn how pattern matching addresses common bioinformatics challenges and gain an overview of Python’s regex tools. The session covers the fundamentals of regular expressions as a domain-specific language for describing string patterns, followed by practical exercises writing regex examples.

Key Topics:
Domain-specific languages, modules and namespaces, pattern matching, regular expressions.

Session 7- 03:00:00 – Dictionaries
This session introduces Python’s dictionary data structure as an alternative to lists for managing paired data. Using the bioinformatics example of k-mer counting, participants explore why dictionaries are better suited for certain problems and learn the syntax for creating and manipulating them.

Comparisons between list- and dictionary-based solutions illustrate appropriate use cases. Additional examples demonstrate the commonality of key-value data in bioinformatics and programming. Practical exercises include creating and using dictionaries, such as in DNA-to-protein translation.

Key Topics:
Paired data types, hashing, key uniqueness, argument unpacking, tuples.

Session 8- 03:00:00 – File Management and Housekeeping Scripts
This session covers Python’s standard library modules for file manipulation, focusing on automating common but essential housekeeping tasks in bioinformatics projects, such as renaming, moving, deleting files, and creating folders.

The practical portion centers on a bioinformatics data pre-processing challenge: organizing DNA sequences by length. This exercise highlights complexities related to program state across multiple runs, processing multiple files, and generating multiple outputs.