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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.
Our imaginary colleague "Dr. Maverick" has invented a new miracle drug that promises to cure arthritis inflammation flare-ups after only 3 weeks since initially taking the medication! Naturally, we wish to see the clinical trial data, and after months of asking for the data they have finally provided us with a CSV spreadsheet containing the clinical trial data.
The CSV file contains the number of inflammation flare-ups per day for the 60 patients in the initial clinical trial, with the trial lasting 40 days. Each row corresponds to a patient, and each column corresponds to a day in the trial. Once a patient has their first inflammation flare-up they take the medication and wait a few weeks for it to take effect and reduce flare-ups.
To see how effective the treatment is we would like to:
- Calculate the average inflammation per day across all patients.
- Plot the result to discuss and share with colleagues.
{alt='3-step flowchart shows inflammation data records for patients moving to the Analysis stepwhere a heat map of provided data is generated moving to the Conclusion step that asks thequestion, How does the medication affect patients?'}
The data sets are stored in 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
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.
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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 any officially supported Python version, currently Python 3.8+. Newer versions usually have better error printouts, so using newer Python versions is recommend if possible.
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To get started, follow the directions on the Setup page to download data and install a Python interpreter.