bokeh / dataviz-fundamentals

Plots from "Fundamentals of Data Visualization", a book by Claus O. Wilke, created using Bokeh.

Fundamentals of Data Visualization, in Bokeh 📊

⚠️ Note: This repository is a WIP, "watch" it to keep up with updates!

Project description

This repository hosts Bokeh equivalents for various plots from Fundamentals of Data Visualization by Claus O. Wilke. It provides a collection of interactive data visualizations implemented using the Bokeh library.

The full rendered pages of this repository can be found here

Table of contents (WIP)

1. Introduction: An overview of the narrative and type of plots to expect.

2. Visualizing amounts

   • Bar plots: Representing amounts using vertical, horizontal, grouped, and stacked bars.

   • Dot plots and heatmaps: Using dots and colors to represent values.

3. Visualizing distributions

   • Single distribution histogram and density plots: Showing the distribution of a single variable using histograms or density plots.

   • Multiple distribution histogram and density plot: Comparing multiple distributions using histograms and density plots.

   • Visualizing many distributions at once using boxplots, sina plots and ridgeline plots: Illustrating the distribution of data using boxes and whiskers and the density of multiple distributions along a common axis using ridgeline plots.

4. Visualizing associations

   • Scatter plots and correlograms: Illustrating the relationship between two variables using scatter plots, correlograms and paired data points.

Local setup

To run these notebooks locally, follow these steps:

1. Clone the repository:

    git clone https://github.com/bokeh/dataviz-fundamentals.git
   

2. Navigate to the project directory via the terminal or command prompt.

3. Create a new conda environment and install the required dependencies:

    conda env create -n <name> -f environment.yml
   

replacing <name> with your preferred environment name.

4. Activate the new environment:

    conda activate <name>
   

5. Open Jupyter notebook via anaconda navigator or via the command line:

    jupyter notebook
   

6. Open the desired notebook in your web browser and run the cells.

Contributing

Contributions are welcome! If you would like to contribute to this project, please follow the guidelines below:

• Fork the repository and create your branch.

• Make your changes and ensure the code follows the project's coding style.

• Test your changes thoroughly.

• Run:

    pre-commit install
  

  to install the pre-commit hooks locally.

• Commit your changes.

• Submit a pull request with a clear description of your changes.

License & Code of Conduct

This project is licensed under the MIT and BSD 3-Clause licence. By contributing to this project, you agree to abide by the Bokeh Code of Conduct.