pygal
Apache Superset
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pygal | Apache Superset | |
---|---|---|
3 | 3 | |
2,600 | 34,745 | |
0.3% | - | |
7.7 | 9.9 | |
3 months ago | about 3 years ago | |
Python | Python | |
GNU Lesser General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pygal
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ECharts for Python
> There is a snapshot library for pyecharts that allows you to convert the HTML produced by the library into formats like JPEG, PNG, PDF and SVG.
One alternative is Pygal: https://github.com/Kozea/pygal/
Even though the library is not actively "developed" but it is a complete library in my opinion.
I feel like with d3.js and eCharts, modern data visualization requires you to run analytics processes first then outputting a JSON then writing the visualization code with JavaScript.
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Homebrew Crafting rules and analysis
I used Pygal to generate the charts, and it uses a unique colour per dataset, so 20 colours for each level. I just didn't see a need to change it.
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[OC] I created graphs that show the page count per chapter for the top 20 most popular manga on MyAnimeList. (Notes and interactive charts in comments)
pygal (To generate the png and interactive charts)
Apache Superset
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Using KeyCloak(OpenID Connect) with Apache SuperSet
The first difference is that after pull request 4565 was merged, you can no longer do:
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Open Source Analytics Stack: Bringing Control, Flexibility, and Data-Privacy to Your Analytics
Open-source BI platforms such as Metabase (website, GitHub) and Apache SuperSet (website, GitHub) are easy to deploy without IT involvement. Metabase lets you build dashboards from the data in your warehouse easily, with no SQL, or, if you have data engineering or science know-how, inside more powerful and flexible notebooks or with SQL itself. Similarly, Apache SuperSet helps businesses explore and visualize data from simple line charts to detailed geospatial charts.
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Ask HN: What low-code “dashboarding“ SaaS would you recommend in 2021?
Check out Superset. https://github.com/apache/incubator-superset
It’s modern, easy to extend. From the same author of apache airflow.
What are some alternatives?
matplotlib - matplotlib: plotting with Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
bokeh - Interactive Data Visualization in the browser, from Python
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
seaborn - Statistical data visualization in Python
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
Altair - Declarative statistical visualization library for Python
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
bqplot - Plotting library for IPython/Jupyter notebooks
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications