Programming with Python

The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis.

Arthritis Inflammation

We are studying inflammation in patients who have been given a new treatment for arthritis.

There are 60 patients, who had their inflammation levels recorded for 40 days. We want to analyze these recordings to study the effect of the new arthritis treatment.

To see how the treatment is affecting the patients in general, we would like to:

  1. Calculate the average inflammation per day across all patients.
  2. Plot the result to discuss and share with colleagues.

3-step flowchart shows inflammation data records for patients moving to the Analysis step
where a heat map of provided data is generated moving to the Conclusion step that asks the
question, How does the medication affect patients?

Data Format

The data sets are stored in comma-separated values (CSV) format:

The first three rows of our first file look like this:

0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1
0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1

Each number represents the number of inflammation bouts that a particular patient experienced on a given day.

For example, value “6” at row 3 column 7 of the data set above means that the third patient was experiencing inflammation six times on the seventh day of the clinical study.

In order to analyze this data and report to our colleagues, we’ll have to learn a little bit about programming.

Prerequisites

You need to understand the concepts of files and directories and how to start a Python interpreter before tackling this lesson. This lesson sometimes references Jupyter Notebook although you can use any Python interpreter mentioned in the Setup.

The commands in this lesson pertain to Python 3.

Getting Started

You can click on the following button to open an interactive Python programming environment called Jupyter in your browser. Binder

Alternatively (assuming you have some time on your hand), follow the directions on the “Setup” page to download data and install a Python interpreter on your computer.

Schedule

Setup Download files required for the lesson
00:00 1. Python Fundamentals What basic data types can I work with in Python?
How can I create a new variable in Python?
Can I change the value associated with a variable after I create it?
00:20 2. Analyzing Patient Data How can I process tabular data files in Python?
01:20 3. Visualizing Tabular Data How can I visualize tabular data in Python?
How can I group several plots together?
02:10 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.