Build a chart quickly from tidy data without reformatting it for each plot type.
Get a good-looking chart out of the box using Chartify's default styles.
Fall back to the underlying Bokeh API when you need finer control than Chartify offers.
Export a chart as a PNG image for reports or presentations.
| spotify/chartify | pallets/quart | dataherald/dataherald | |
|---|---|---|---|
| Stars | 3,632 | 3,632 | 3,633 |
| Language | Python | Python | Python |
| Setup difficulty | easy | easy | moderate |
| Complexity | 2/5 | 2/5 | 4/5 |
| Audience | data | developer | pm founder |
Figures from each repo's GitHub metadata at analysis time.
PNG export requires an additional Chrome and chromedriver setup step.
Chartify is a Python library created by Spotify to make building charts and graphs easier for people who work with data. The goal is to reduce the time spent wrestling with data formatting before you can get a chart to appear. Most charting tools require data to be shaped in specific ways for each chart type, but Chartify uses a consistent format across all of its plot types, so you learn the pattern once and apply it everywhere. The library comes with sensible default styles, meaning you can get a good-looking chart quickly without spending time adjusting colors, fonts, or layout. The API is designed to be straightforward to learn, and there is a tutorial notebook that walks through the core concepts step by step. Under the hood, Chartify is built on top of Bokeh, which is a separate charting library for Python. This matters because it means if you ever need to do something Chartify does not directly support, you can drop down to the underlying Bokeh tools without switching libraries entirely. Installation is done through pip, the standard Python package manager. Exporting charts as PNG image files requires an additional optional setup step involving Google Chrome. The library supports Python 3.9, 3.10, and 3.11. Documentation, a tutorial, and example notebooks are all available online.
Chartify is Spotify's Python charting library that uses one consistent data format and good default styles to make building charts faster.
Mainly Python. The stack also includes Python, Bokeh.
No license terms are stated in the README.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.