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This workshop takes learners through the basics of programming in Python via the Jupyter Lab interface and culminates with exploration and visualization of real-world bicycle count data from the City of Toronto. The file 'lecture-notes-python-workshop.ipynb' can be opened and run with Jupyter notebook or Jupyter Lab, and the material is designed to be presented as a participatory live-coding workshop in which learners follow along as the instructor projects their code. The file 'lecture-notes-python-workshop.html' is a rendered version of the .ipynb notebook file that can be viewed in a browser.
This material focuses on using the package pandas for working with spreadsheet-type data and the packages matplotlib and seaborn for data visualization.

This material is based on workshops (https://uoftcoders.github.io/2018-07-12-utoronto/) hosted by UofT Coders (https://uoftcoders.github.io), inspired by the Data Carpentry Ecology Python lesson (https://datacarpentry.org/python-ecology-lesson/).

• college_spadina_2010_2017.csv(CSV | 48 KB)
• faculty_notes.pdf(PDF | 135 KB)
• lecture-notes-python-workshop.html(HTML | 2 MB)
• lecture-notes-python-workshop.ipynb(IPYNB | 2 MB)
• college_spadina_excel_screenshot.jpg(JPG | 177 KB)
• jupyter-start-screen.jpg(JPG | 38 KB)
• split-apply-combine.png(PNG | 25 KB)
• License terms

Researchers should cite this work as follows:

• Bonsma-Fisher, M. (2018). Working with spreadsheet-style data in Python with pandas and seaborn. QUBES Educational Resources. doi:10.25334/Q4PF1D

  BibTex | EndNote

1. Activity Length
2. Audience Level
3. bicyle
4. data
5. Faculty
6. Graduate
7. Instructional Setting
8. introductory
9. Lab
10. Majors
11. matplotlib
12. More than 1 hour
13. Non-majors
14. open
15. pandas
16. python
17. seaborn
18. Spreadsheet
19. Teaching material
20. Undergraduate