Summary and Schedule
This lesson is an introduction to programming in Python 3 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 teaches Python using the JupyterLab Desktop application.
Prerequisites
- Learners must come with a laptop on which they have privileges sufficient to install Python. Phones and tablets are not acceptable.
- While alternative JupyterLab installations are acceptable, the first class session is dedicated to the installation of JupyterLab Desktop.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Installing JupyterLab Desktop |
How do I install JupyterLab Desktop? How do I configure JupyterLab Desktop so I can find my files again? |
| Duration: 00h 30m | 2. Running and Quitting | How can I run Python programs? |
| Duration: 00h 45m | 3. Variables and Assignment | How can I store data in programs? |
| Duration: 01h 05m | 4. Data Types and Type Conversion |
What kinds of data do programs store? How can I convert one type to another? |
| Duration: 01h 25m | 5. 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? |
| Duration: 01h 50m | 6. Libraries |
How can I use software that other people have written? How can I find out what that software does? |
| Duration: 02h 10m | 7. Reading Tabular Data into DataFrames | How can I read tabular data? |
| Duration: 02h 30m | 8. Pandas DataFrames | How can I do statistical analysis of tabular data? |
| Duration: 03h 00m | 9. Plotting |
How can I plot my data? How can I save my plot for publishing? |
| Duration: 03h 30m | 10. Lists | How can I store multiple values? |
| Duration: 03h 50m | 11. For Loops | How can I make a program do many things? |
| Duration: 04h 15m | 12. Conditionals | How can programs do different things for different data? |
| Duration: 04h 40m | 13. Looping Over Data Sets | How can I process many data sets with a single command? |
| Duration: 04h 55m | 14. Writing Functions | How can I create my own functions? |
| Duration: 05h 20m | 15. Variable Scope |
How do function calls actually work? How can I determine where errors occurred? |
| Duration: 05h 40m | 16. Programming Style |
How can I make my programs more readable? How do most programmers format their code? How can programs check their own operation? |
| Duration: 06h 10m | 17. Wrap-Up |
What have we learned? What else is out there and where do I find it? |
| Duration: 06h 30m | 18. Feedback | How did the class go? |
| Duration: 06h 45m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Getting the Data
The data we will be using is taken from the gapminder dataset. To obtain it, download and unzip the file python-novice-gapminder-data.zip.
In order to follow the presented material, you should launch the JupyterLab Desktop app from your home directory.