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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.
We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in [comma-separated values]({{ page.root }}/reference/#comma-separated-values) (CSV) format:
- each row holds information for a single patient,
- columns represent successive days.
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
{: .source} 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.
So, we want to:
- Calculate the average inflammation per day across all patients.
- Plot the result to discuss and share with colleagues.
To do all that, we'll have to learn a little bit about programming.
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. {: .prereq}
To get started, follow the directions on the "Setup" page to download data and install a Python interpreter.