Plotting and Programming in Python

This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references the Jupyter Notebook, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

Under Design

This lesson is currently in its early design stage; please check the design notes to see what we have so far. Contributions are very welcome: we would be particularly grateful for exercises and for commentary on the ones already there.


  1. Learners need to understand what files and directories are, what a working directory is, and how to start a Python interpreter.

  2. Learners must install Python before the class starts.

  3. Learners must get the gapminder data before class starts: please download and unzip the file

    Please see the setup instructions for details.


Setup Download files required for the lesson
00:00 1. Running and Quitting How can I run Python programs?
00:15 2. Variables and Assignment How can I store data in programs?
00:35 3. Data Types and Type Conversion What kinds of data do programs store?
How can I convert one type to another?
00:40 4. Built-in Functions and Help How can I use built-in functions?
How can I find out what they do?
What kind of errors can occur in programs?
01:00 5. Lists How can I store multiple values?
01:20 6. Tuples & Dictionaries How can I add semantic value to my collections of multiple values?
01:35 7. Break Break
01:50 8. Libraries How can I use software that other people have written?
How can I find out what that software does?
02:00 9. Reading Tabular Data into DataFrames How can I read tabular data?
02:20 10. Plotting How can I plot my data?
02:25 11. Writing Functions How can I create my own functions?
02:50 12. For Loops How can I make a program do many things?
03:05 13. Conditionals How can programs do different things for different data?
03:25 14. Wrap-Up Exercise How can we put together all of today’s material?
03:45 15. Pandas DataFrames How can I do statistical analysis of tabular data?
03:50 16. Command-Line Programs How can I write Python programs that will work like Unix command-line tools?
04:37 17. Defensive Programming How do I predict and avoid user confusion?
04:52 18. Refactoring When should I reorganize my code so it is more clear and readable for others?
How can I organize my code so that it is useable in other places?
Why do I almost always want to write my code as though it will be used somewhere else?
05:03 19. Running Scripts and Importing How can I import some of my work even if it is part of a program?
05:33 20. Programming Style How can I make my programs more readable?
How do most programmers format their code?
How can programs check their own operation?
05:43 21. Wrap-Up What have we learned?
What else is out there and where do I find it?
06:03 Finish

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