ONLINE COURSE – Introduction to Python for Ecologists and Evolutionary Biologists (IPYB01) This course will be delivered live

Day 2  – Classes form 09:30 – 17:30

Session 3 : Lists and loops

• In this session we’ll start by thinking about the kinds of programs that we need to write for our research work. An important idea is that we want to write programs that can deal with arbitrary amounts of data. In order to do so, we need two things: a way of *storing* large collections of values, and a way of *processing* them. In Python, **lists** and **loops** do these jobs respectively. We’ll go over the new syntax needed for each, and see how together they allow us to write programs that are much closer to being useful in the real world. This new syntax will allow us to see how lists, strings and files all share similar behaviour and how we can take advantage of that fact to write concise code. In the practical session we’ll tackle some problems that involve larger data files. Core concepts introduced: lists and arrays, blocks and indentation, variable scoping, iteration and the iteration interface, ranges.

Session 4 : conditions and flow control

• We will start this session by using the idea of decision making as a way to introduce conditional tests, and outline the different building blocks of conditions before showing how conditions can be combined in an expressive way. We look at the different ways that we can use conditions to control program flow, and how we can structure conditions to keep programs readable. These simple ideas combine with the material we have already covered to allow us to write programs that can follow rules and enforce logic. Correspondingly, in the practical session we’ll be able to attempt some complex filtering challenges on a structured CSV file. Core concepts introduced: Truth and falsehood, Boolean logic, identity and equality, evaluation of statements, branching.

Day 3 – Classes from 09:30 – 17:30

Session 5 : Organizing and structuring code

• We’ll start off by discussing functions that we’d like to see in Python before considering how we can add to our computational toolbox by creating our own. We examine the nuts and bolts of writing functions before looking at best practice ways of making them usable. We also look at a couple of advanced features of Python – named arguments and defaults. This session ends with a first look at the concepts behind automated testing, and the easiest way to get started with tests in Python. The practical session makes extensive use of automated testing, with students writing functions to pass a series of unit tests. Core concepts introduced: argument passing, encapsulation, data flow through a program, unit testing.

Session 6 : The Python standard library and Regular expressions

• We begin this sesion by browsing the documentation for the Python standard library and discussing how it fits in with the core parts of Python that we’ve already discussed, along with other libraries of code that students may have already encountered. To explore how Python’s module system works in detail we will take a close look at one particular module: the one that deals with regular expressions. We’ll see how a range of common problems in bioinformatics can be described in terms of pattern matching, and give an overview of Python’s regex tools. We look at the building blocks of regular expressions themselves, and learn how they are a general solution to the problem of describing patterns in strings, before practising writing some specific examples of regular expressions. Core concepts introduced: domain-specific languages, modules and namespaces.

Day 4 – Classes form  09:30 – 17:30

Session 7 : Dictionaries

All of the data sets that we’ve considered so far in the course fit nicely into the list paradigm. In this session, it’s time to introduce the second major data structure offered by Python: the dictionary. To do this, we’ll look at a classic bioinformatics problem – kmer counting – and see how lists aren’t a good fit before learning the new syntax that we need to make dictionaries. Comparing the list and dictionary solutions will make it clear when we should use each approach. We’ll wrap up by discussing a few more examples of key-value data and see how the problem of storing them is a common one across bioinformatics and programming in general. In the practical session we will practice writing programs that create dictionaries, and ones that use dictionaries, including another classic bioinformatics problem: DNA to protein translation. Core concepts introduced: paired data types, hashing, key uniqueness, argument unpacking and tuples.

Session 8 : File management and housekeeping scripts

This session concerns a part of the Python standard library that is boring but useful – the modules concerned with file manipulation. We will cover the tools that Python gives us to automate the common repetitive housekeeping operations that are part of many bioinformatics projects, but rarely make it into the final publication – things like renaming, moving and deleting files, creating folders, etc. The notebook part of this session is quite brief, giving us a generous amount of practical time to tackle an example of a bioinformatics data pre-processing problem: organizing a collection of DNA sequences by length. Although the problem can be stated very concisely, we’ll quickly see that there are quite a few subtleties to it, giving us a chance to think about program state of multiple runs, processing multiple files, and creating multiple output files.