Lesson 3
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Hello Creative Python

A hands-on first look at how we can use Python for creative coding. See tutorials at Skills Labs.
Lesson Table of Contents

Video

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Lesson outline

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Lesson 3: Skills Lab 1

Hello Creative Python!

Objective

Gain foundational Python skills and learn creative coding techniques through browser-based tutorials to prepare for data visualization work.

Outline

This hands-on session introduces Skills Lab 1, which provides practical Python instruction through two sequential tutorials designed for different experience levels. Both tutorials use browser-based environments requiring no local installation.

Tutorial structure

Skills Lab 1 offers two complementary tutorials with distinct entry points.

  • Tutorial 1 is oriented towards those with less prior Python experience.
  • Tutorial 2 should be accessible after Tutorial 1 but may be quick for those with Python expertise.
  • Goal is to complete tutorials 1 and 2.
  • All tutorials available at https://mooc.interactivedatascience.courses/labs.
Technical setup

Both tutorials run entirely in the browser with no installation required.

  • Tutorial 1 uses Jupyter Lite for an interactive Python learning environment.
  • Tutorial 2 uses the Sketchingpy Sketchbook for creative coding.
  • You only need a web browser to access both environments.
  • No local software installation or configuration needed.
Sketchingpy code example

The lecture demonstrates basic Sketchingpy syntax for creating interactive graphics.

```python import sketchingpy import time

sketch = sketchingpy.Sketch2D(500, 400)

center_x = 500 / 2 center_y = 400 / 2 end_x = 100 end_y = 50 sketch.draw_line(center_x, center_y, end_x, end_y)

def draw_moving_line(target): mouse = sketch.get_mouse() sketch.draw_line(250, 200, mouse.get_pointer_x(), mouse.get_pointer_y())

sketch.on_step(draw_moving_line)

sketch.show() ```

This:

  • Creates a drawing canvas (sketch).
  • Draws static elements (lines).
  • Responds to user input (mouse position).
Flipped classroom approach

This lecture follows a flipped classroom model to support hands-on exploration.

Connection to course themes

These exercises build technical foundations supporting the four perspectives introduced in Lecture 1.

  • Data Visualization as Representation: Learning to map data to visual elements using code.
  • Data Visualization as Task: Understanding how to build tools for specific user needs.
  • Data Visualization as Message: Creating graphics that convey meaning and emotion.
  • Data Visualization as Dialogue: Building interactive experiences that respond to user input.
Creative coding philosophy

The use of Sketchingpy emphasizes code as a creative medium.

  • Code as a creative medium.
  • Iteration and experimentation.
  • Direct manipulation of visual elements.
  • Building custom visualizations beyond pre-made chart types.
Additional resources

Optional materials to supplement your learning experience.

  • Lecture video available at https://vimeo.com/1049110017.
  • Optionally review A Byte of Python at https://python.swaroopch.com if you want to brush up on (or learn) the fundamentals of Python.
  • Skills Labs at https://mooc.interactivedatascience.courses/labs.
  • Sketchingpy Online Sketchbook at https://editor.sketchingpy.org/.

Take Aways

Browser-based tools like Jupyter Lite and Sketchingpy make Python programming accessible with no installation barriers while creative coding provides a foundation for building custom data visualizations.

  • Tutorial 1 covers Python fundamentals for those with less prior experience.
  • Tutorial 2 introduces creative coding with Sketchingpy.
  • Both tutorials are browser-based with instant feedback and no setup required.
  • Code serves as a creative medium for direct manipulation of visual elements.
  • These technical skills support all four course perspectives on data visualization.

Citations

[1] Thanks to https://badriadhikari.github.io/data-viz-workshop-2021/minards/ for imagery.

[2] A. Pottinger and Sketchingpy Contributors, "Sketchingpy," Sketchingpy Project, 2025. Available: https://sketchingpy.org/

[3] C. H. Swaroop, "A Byte of Python," 2003. Available: https://python.swaroopch.com

License

This lesson is part of Interactive Data Science and Visualization and is released under a CC-BY-NC 4.0 license.

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Written materials

In addition to the video, you may also:

Exercise

Revisit the visualization from the last exercise. What might the 4 perspectives say about this visualization?
  • What visual encoding devices are used?
  • What task or user journey is being accomplished through the piece?
  • Does the piece try to also convey an emotion and, if so, how?
  • Does the piece invite the reader to reach their own conclusions about the data and, if so, how?
Please write 4 - 8 sentences.

Note that the Zulip community is not available to this MOOC. Please consider sharing your exercise via social media such as Bluesky with the tag #OpenDataVizSciCourse.

Reading

Optionally review A Byte of Python if you want to brush up on (or learn) the fundamentals of Python.

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Works cited

This is the works cited from the lecture. Note that additional sources may be used in exercises and other supporting documentation.