Data Visualization

ECharts66.3k★

ECharts renders everything from simple bar charts to complex 3D globes, geomaps, and real-time streaming dashboards, all client-side with no server dependencies. It handles everything from a simple dashboard widget to a full geographic heatmap with 100K data points. Apache Foundation project, TypeScript core. The feature set is massive: 20+ chart types, animations, responsive layouts, dataset transforms, and a visual builder at echarts.apache.org that lets you configure charts before writing code. It handles real-time streaming data and lazy-loads chart types you don't use. Everything is free. No paid tier, no hosted version, no premium charts behind a paywall. Apache 2.0 license means you can use it in commercial products without restrictions. Solo developers get a production-grade charting library for $0. Teams of any size get the same thing. The community is enormous. StackOverflow answers for almost any question. The catch: the API surface is huge. Configuration objects get deeply nested and the docs, while comprehensive, assume you already know what you want. If you need something quick and simple, Chart.js gets you there faster. If you need advanced interactivity or large datasets, ECharts is the better pick.

Streamlit44.5k★

Streamlit turns Python scripts into web apps in minutes: dashboards, demos, internal tools. No HTML, no CSS, no JavaScript. You write Python, and Streamlit gives you a web interface with charts, tables, sliders, and file uploads. Apache 2.0, Python, owned by Snowflake. The workflow is dead simple: write a Python script using Streamlit's API (`st.write`, `st.chart`, `st.slider`), run `streamlit run app.py`, and you have a web app. It re-runs the script top-to-bottom on every interaction, which is brilliant for prototyping and awkward for complex state management. The library is fully free. Streamlit Community Cloud hosts your apps for free with generous limits: public apps, GitHub-connected deployment. Snowflake offers Streamlit in Snowflake for enterprise data apps. Solo data scientists: this is your tool. Build demos, share analyses, prototype ML interfaces. Small teams: great for internal dashboards. Medium to large: works for internal tools, but production customer-facing apps will outgrow the re-run-on-every-click model. The catch: Streamlit re-executes your entire script on every user interaction. For simple apps, fine. For apps with expensive computations, you need caching decorators everywhere. The layout system is limited: you get a single column by default and basic column/tab options. If you need a real web app with complex navigation and state, you've outgrown Streamlit.

kibana21.1k★

Kibana is the visualization layer for Elasticsearch. If your data lives in an Elastic cluster, this is how you build dashboards, run queries, and monitor everything from logs to APM traces. The UI handles everything from ad-hoc searches to complex aggregations without writing code. Self-hosting is free under the Elastic License 2.0, but running Elasticsearch underneath is the real commitment. Plan for a dedicated cluster, index management, and ongoing maintenance. This is not a weekend project for small teams. Solo developers and small teams doing log analysis can get by with the free tier and a managed Elastic Cloud instance (starts at $95/mo). Enterprise teams running their own clusters get the full power but need dedicated ops. The paid tiers add security features (SSO, field-level permissions), machine learning, and cross-cluster search. The catch: Kibana without Elasticsearch is nothing, and Elasticsearch at scale is expensive to operate. If you just need dashboards over Postgres or CSV data, look at Metabase or Grafana instead.

Mapbox GL JS12.3k★

Mapbox GL JS renders interactive, GPU-accelerated maps in the browser that you can tilt, rotate, fly through, and layer custom data on. It renders vector tiles using WebGL, which means smooth 60fps panning and zooming with 3D building extrusions, custom styling, and data visualization layers. Here's where it gets complicated. Mapbox GL JS v1 was open source (BSD). Version 2+ switched to a proprietary license. You can view the source code, but you can't use it without a Mapbox access token, and that token ties you to Mapbox's pricing. Free tier: 50,000 map loads/month. After that, $5 per 1,000 loads. There's no self-hosting option for v2+. You need Mapbox's tile servers and API. The library itself loads from npm but phones home for tiles and authentication. Solo developers and small projects: the free tier is generous. 50K loads covers most side projects and small apps. Medium teams: costs become real. A site with 500K map loads/month runs ~$2,250/mo. That adds up fast. The catch: the license switch burned the community hard. MapLibre GL JS forked from the last open source version and is actively maintained. If you want truly free maps, start there instead.

superset10.5k★

Apache Superset does that. Connect it to your database, write SQL or use the visual query builder, and create shareable dashboards. It handles most common databases (Postgres, MySQL, BigQuery, Snowflake, ClickHouse) and the chart library is extensive. Apache project, used by Airbnb, Lyft, and others in production. The catch: self-hosting Superset is where it gets real. Versioning is a beast. It is very easy to fall out of compliance with updates before you realize it. The Docker setup works but keeping it maintained takes genuine ops effort. And the learning curve for non-technical users is steeper than paid alternatives like Metabase or Looker.

pygraphistry2.5k★

PyGraphistry is the Python client for Graphistry, a GPU-accelerated graph visualization platform. Load a few million nodes and edges, get an interactive zoomable graph in your browser. Free Python library, paid server tiers. The library is open source and easy to use. Pandas DataFrame in, visualization out. You point it at the Graphistry Hub (free tier with limits) or your own enterprise server. The rendering happens server-side on a GPU, which is what makes this scale to graphs that would melt D3 or sigma.js. For data scientists doing exploratory analysis on biggish graphs, this is the right tool. The free Hub tier is fine for personal use and demos. Teams that need private data or higher limits pay for the enterprise server, which is real money. Quote-required, not self-serve. If you can't justify that, look at Memgraph Lab, Neo4j Bloom on Aura's free tier, or roll your own with sigma.js. The catch is the GPU dependency. The client library is free but useless without a Graphistry server, and you can't self-host the server free tier. Plan around that before you build it into a product.

vizzu-lib2.0k★

Vizzu does something no other charting library does well. It's built around the idea that data stories are told through transitions, not static images. You feed it a dataset and describe how you want it visualized. Change the description, and it animates the transition. The same data can flow from a grouped bar chart to a treemap to a bubble chart with physics-based animation. It's impressive to watch and makes presentations and dashboards feel alive. Completely free under Apache 2.0. No paid tier, no premium chart types, no watermarks. For standard charting, Apache ECharts or Chart.js are the workhorses. For custom data art, D3 is still king. Vizzu fills the niche of animated data storytelling that nothing else does as cleanly. The catch: this is a presentation and storytelling tool, not a drop-in replacement for Chart.js or D3. If you need standard dashboards with tooltips, drill-downs, and real-time updates, Vizzu isn't built for that. The API is also different from what you're used to. It's not "make me a bar chart," it's "here's data, here's how to arrange it." There's a learning curve. The community is small and documentation could be better.