Introduction to Python for Biology

Day 1

Introduction to Python

• Overview of Python as a programming language
• Understand why we use Python for scientific computing
• Differentiate between tasks that Python is suitable for and not
• Become familiar with Python syntax and formatting
• Installing Python
• Setting up your environment
• Differentiate between a Python script and a Jupyter Notebook
• Edit and run simple Python scripts using the command line
• Use Jupyter Notebooks to create and run both Markdown and Python cells
• How to read the documentation to get help

Variables and Assignment

• Assign values to variables
• Perform calculations with variables
• Differentiate between different Python variable types
• Use built-in functions to convert between Python variable types
• Import a Python library and use the functions it contains.
• Read tabular data from a file into a program.
• Select individual values and subsections from data.
• Perform operations on arrays of data
• Use comments to annotate code
• Plot simple graphs from data.

Looping and Repeating Actions

• Understand what a for loop does
• Understand what a while loop does and when to use it
• Write for loops to repeat calculations
• Trace changes to both the loop variable and other variables as a loop runs

Lists and Dictionaries

• Create and index a list of values
• Change the values of a list
• Add values to an existing list
• Reorder lists
• Slice list elements
• Create and manipulate nested lists
• Store paired data in dictionaries
• Iterate over dictionaries

Control Flow

• Write conditional statements using if, else, and elif branches
• Use Boolean operations to test conditions
• Evaluate complex conditionals using and, not, and or

Day 2

Manipulating Text

• Dealing with special characters in Python
• Use Python’s in-built string manipulation tools

Reading Text from a File

• Opening files for reading text
• Opening files for writing text
• Closing files
• Understanding paths and folders

Libraries

• Explain what software libraries are and why programmers create and use them
• Write programs that import and use libraries from Python’s standard library
• Find and read the documentation for standard libraries both interactively and online

Working with Files in Pandas

• Use Pandas to load tabular data sets
• Get basic information about the Pandas DataFrame
• Select individual values from a Pandas dataframe
• Select entire or a subset of rows or columns from a dataframe
• Create dataframes
• Filter and sort dataframes
• Handling missing values
• Split-apply-combine methods with Pandas

File Contents and Manipulation

• Delete files and folders
• List folder contents
• Run external programs
• Add flexibility to programs by taking user input
• Use command-line arguments in a program
• Handle flags and files separately in a command-line program
• Read data from standard input in a program so that it can be used in a pipeline

Day 3

Functions

• Explain the purpose of functions
• Differentiate between defining functions and calling functions
• Define functions that take parameters
• Return values from functions
• Identify local and global variable scope
• Test and debug functions
• Set default values for function parameters
• Discuss best practices on how to divide programs into small, single-purpose functions

Error-Handling

• Read a traceback, determine where an error took place, and identify the type of error that took place
• Describe the situations in which syntax errors, indentation errors, name errors, index errors, and missing file errors occur

Defensive Programming

• Understand what an assertion is
• Add assertions to check whether or not a program’s state is correct
• Add pre- and post-condition assertions to functions
• Understand test-driven development and use it when creating functions

Debugging

• Debug code with errors using systematic processes
• Discuss ways of making code less error-prone and more easily tested

Style

• Understand the reasoning behind the basic rules of coding style
• Refactor programs to make them more readable
• Use Python community coding standards (PEP-8)

Day 4

Plotting in Python

• Choose appropriate plots for different purposes
• Understand the Matplotlib library basic plotting formula
• Understand Matplotlib object hierarchy
• Create line plots, scatter plots, histograms, box plots, and bar charts using the Matplotlib library
• Combine multiple plots
• Use Pandas to do basic plotting on dataframes
• Use Seaborn to create higher-level Python plots

Day 5

Regular Expressions

• Identify instances when regular expressions will speed up workflow
• Understand what a regular expression is and how it is used
• Use the re library methods in Python
• Search for patterns in strings using regular expression operators
• Extract matched patterns in strings as groups

Graduate or postgraduate degree in Life Sciences and basic knowledge of Statistics. While some Python knowledge is useful, the course will cover basic Python skills necessary to input, clean, and explore data.

All participants must use their own computer (Windows, Macintosh, Linux), with access to a good internet connection.