1.plotly / plotly.py
The interactive graphing library for Python :sparkles:
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31 repositories by Attention Score · Oct 4, 2026 – Nov 2, 2026 (UTC)
The interactive graphing library for Python :sparkles:
3D visualization and mesh analysis for science and engineering
PyGWalker: Turn your dataframe into an interactive UI for visual analysis
Rich-powered CLI that breaks each HTTP request into DNS, connect, TLS, wait, and transfer phases with waterfall timelines, compact summaries, or metrics-only output.
Vizro is a low-code toolkit for building high-quality data visualization apps.
napari: a fast, interactive, multi-dimensional image viewer for python
Investigate malicious Windows logon by visualizing and analyzing Windows event log
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
Fast data visualization and GUI tools for scientific / engineering applications
git-cola: The highly caffeinated Git GUI
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
A Python package to visualise neuroanatomical data in atlas space
Realtime Web Apps and Dashboards for Python and R
Main repository for Vispy
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Automatically visualize your pandas dataframe via a single print! 📊 💡
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Python library that makes it easy for data scientists to create charts.
A python module for scientific analysis of 3D data based on VTK and Numpy
Entity Relation Diagrams generation tool
Visualize large time series data with plotly.py
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
An intuitive library to add plotting functionality to scikit-learn objects.
An awesome iTerm2 backend for Matplotlib, so you can plot directly in your terminal.
Reconquer the canvas: beautiful Tikz figures without clunky Tikz code
Modern, interactive charts for Jupyter notebooks with Apache ECharts
Sequential model-based optimization with a `scipy.optimize` interface
Visualization Package for NetworkX
MCP server for spatial transcriptomics analysis through natural language interfaces.