Archive

Evidence is "Business Intelligence as Code"; which means it allows you to build reports, and standard business data visualization ( charts, and basic graphics) using sql with markdown as the glue between various data and visualization elements. You can put to gether a dynamic report with a few interactive elements (e.g: filter data, change date ranges) that can be exported as "static" web pages. The exported web pages will also have interactive elements (e.g: tooltip on hover, charts that re-render when you change the data criteria). You can see the github; and examples

datasette is another player in this space -- "data applications using sql" space, but evidence goes much further by supporting various data backends, and having first class support for thinking in terms of reports, and standard visualization components like charts. Support for markdown is naturally expected.

The combination of markdown + code does put it in a group with jupyter, but the availability of dynamic controls also make you think of streamlit, but does not go as far as observable because observable, given it's pedigree with d3 etc., is a lot more capable visulization tool (think maps, and advanced dataviz techniques).

Fundamental rule -- Friends don't let friends make certain types of data visualization - What are they and why are they bad.

Vega-Lite Vega is a declarative format for creating, saving, and sharing visualization designs. With Vega, visualizations are described in JSON, and generate interactive views using either HTML5 Canvas or SVG.

Domain Storytelling - Domain Storytelling Domain Storytelling is a technique to transform domain knowledge into effective business software. It brings together domain experts and development teams. The domain experts can see immediately whether we understand their story correctly.

Visualization Stories

• Spain lives in flats: why we have built our cities vertically

Python Libraries

Manim Community is the library developed by "3blue1green" and the community to produce dynamic visualizations.

Javascript libraries

Apache ECharts via A love letter to Apache Echarts

What makes it stand out from other charting libraries is that it manages to cover a very wide range of use cases while remaining simple to use. On top of that, the documentation never failed to point me in the right direction. And most importantly, the results look good and are interactive.

Bar chart code.. I like the direct injection into an HTML element:

		const bar = echarts.init(document.getElementById('bar'));
bar.setOption({
  legend: {},
  tooltip: {},
  dataset: {
    // dataset.source contains your data in table form
    source: [
      ['repo', 'stars'],
      ['Mikochi', 129],
      ['crud_benchmark', 40],
      ['sql-repository', 7],
    ],
  },
  xAxis: {
    // category will use the first column for the x axis
    type: 'category',
  },
  yAxis: {},
  series: [
    // this creates a bar chart
    { type: 'bar' },
  ],
});

Blogs

¶ Truth & Beauty - Data visualization by Moritz Stefaner

Courses

• Data Visualization – Data Visualization with R by Andrew Heiss at GSU.

Projects

ClickPy is an analytics dashboard to explore PyPI using Clickhouse backend.

Skills

tufte-viz Claude Code skill — Edward Tufte data visualization principles · GitHub

See also Display Tables

Home page: Processing.org

The Nature of Code (A CC by NC licensed #book) uses processing.

an online resource and print book exploring the unpredictable evolutionary and emergent properties of nature in software via the creative coding framework Processing. 2024 version with a new and rebooted version of this book built around JavaScript and the p5.js library. The book has a few new coding tricks this time, but it’s the same old nature—birds still flap their wings, and apples still fall on our heads.

Welcome to py5! is a new version of Processing for Python 3.9+. The goal of py5 is to create a version of Processing that is integrated into the Python ecosystem. Built into the library are thoughtful choices about how to best get py5 to work with other popular Python libraries and tools such as Jupyter, numpy, shapely, trimesh,matplotlib, and Pillow. needs Java 17