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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}
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. In the first half of the course we'll introduce the concepts of Python and in the second half we'll apply these techniques to the data to perform the analysis.
You need to understand the concepts of files and directories and have a basic knowledge of Shell before tackling this lesson
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.