1.apache / superset
Apache Superset is a Data Visualization and Data Exploration Platform
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25 repositories by Attention Score · May 7, 2026 – Nov 2, 2026 (UTC)
Apache Superset is a Data Visualization and Data Exploration Platform
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
Streamlit — A faster way to build and share data apps.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
🕸️ Web apps in pure Python 🐍
matplotlib: plotting with Python
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval 🔄.
Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
Data Apps & Dashboards for Python. No JavaScript Required.
Turns Data and AI algorithms into production-ready web applications in no time.
Statistical data visualization in Python
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Vizro is a low-code toolkit for building high-quality data visualization apps.
Python Data. Leaflet.js Maps.
Impress your boss and turn a Jupyter notebook into a beautiful, shareable web app — no callbacks, no frontend, no rewrite.
Visualize and compare datasets, target values and associations, with one line of code.
Spatial Single Cell Analysis in Python
Missing data visualization module for Python.
No-code in the front, Python in the back. An open-source framework for creating data apps.
Visualize large time series data with plotly.py
Main yt repository
Turn CSV, Excel, and read-only databases into verified, editable business reports.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.
ML powered analytics engine for outlier detection and root cause analysis.