Summary and Schedule
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.
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.
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 3.7+. Newer versions usually have better error printouts, so using newer Python versions is recommend if possible.
To get started, follow the directions on the Setup page to download data and install a Python interpreter.
|Setup Instructions||Download files required for the lesson|
|Duration: 00h 00m||1. Python Fundamentals||
What basic data types can I work with in Python?
How can I create a new variable in Python?
How do I use a function?
Can I change the value associated with a variable after I create it?
|Duration: 00h 30m||2. Analyzing Patient Data||How can I process tabular data files in Python?|
|Duration: 01h 30m||3. Visualizing Tabular Data||
How can I visualize tabular data in Python?
How can I group several plots together?
|Duration: 02h 20m||4. Storing Multiple Values in Lists||How can I store many values together?|
|Duration: 03h 05m||5. Repeating Actions with Loops||How can I do the same operations on many different values?|
|Duration: 03h 35m||6. Analyzing Data from Multiple Files||How can I do the same operations on many different files?|
|Duration: 03h 55m||7. Making Choices||How can my programs do different things based on data values?|
|Duration: 04h 25m||8. Creating Functions||
How can I define new functions?
What’s the difference between defining and calling a function?
What happens when I call a function?
|Duration: 04h 55m||9. Errors and Exceptions||
How does Python report errors?
How can I handle errors in Python programs?
|Duration: 05h 25m||10. Defensive Programming||How can I make my programs more reliable?|
|Duration: 06h 05m||11. Debugging||How can I debug my program?|
|Duration: 06h 55m||12. Command-Line Programs||How can I write Python programs that will work like Unix command-line tools?|
|Duration: 07h 25m||Finish|
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
This lesson is designed to be run on a personal computer. All of the software and data used in this lesson are freely available online, and instructions on how to obtain them are provided below.
In this lesson, we will be using Python 3 with some of its most popular scientific libraries. Although one can install a plain-vanilla Python and all required libraries by hand, we recommend installing Anaconda, a Python distribution that comes with everything we need for the lesson. Detailed installation instructions for various operating systems can be found on The Carpentries template website for workshops and in Anaconda documentation.
- Download python-novice-inflammation-data.zip and python-novice-inflammation-code.zip.
- Create a folder called
swc-pythonon your Desktop.
- Move downloaded files to
- Unzip the files.
You should see two folders called
code in the
swc-python directory on your
To start working with Python, we need to launch a program that will interpret and execute our Python commands. Below we list several options. If you don’t have a preference, proceed with the top option in the list that is available on your machine. Otherwise, you may use any interface you like.
A Jupyter Notebook provides a browser-based interface for working with Python. If you installed Anaconda, you can launch a notebook in two ways:
- Launch Anaconda Navigator. It might ask you if you’d like to send anonymized usage information to Anaconda developers: Make your choice and click “Ok, and don’t show again” button.
- Find the “Notebook” tab and click on the “Launch” button: Anaconda will open a new browser window or tab with a Notebook Dashboard showing you the contents of your Home (or User) folder.
- Navigate to the
datadirectory by clicking on the directory names leading to it:
- Launch the notebook by clicking on the “New” button and then selecting “Python 3”:
1. Navigate to the
On Windows, you can use its native Command Prompt program. The
easiest way to start it up is pressing Windows Logo
cmd, and hitting
Return. In the Command Prompt, use the following command to
navigate to the
cd /D %userprofile%\Desktop\swc-python\data
2. Start Jupyter server
python -m notebook
3. Launch the notebook by clicking on the “New” button on the right and selecting “Python 3” from the drop-down menu:
IPython is an alternative solution situated somewhere in between the plain-vanilla Python interpreter and Jupyter Notebook. It provides an interactive command-line based interpreter with various convenience features and commands. You should have IPython on your system if you installed Anaconda.
To start using IPython, execute:
To launch a plain-vanilla Python interpreter, execute:
If you are using Git Bash on
Windows, you have to call Python via