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1

plotly.py

The interactive graphing library for Python

plotly.py is a browser-based, open source graphing library for Python that lets you create beautiful, interactive, publication-quality graphs. Built on top of plotly.js, it is a high-level, declarative charting library that ships with more than 30 chart types. Everything from statistical charts and scientific charts, through to maps, 3D graphs and animations, plotly.py lets you create them all. Graphs made with plotly.py can be viewed in Jupyter notebooks, standalone HTML files, or hosted...

Downloads: 13 This Week

Last Update: 4 days ago
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2

Kedro

A Python framework for creating reproducible, maintainable code

Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems...

Downloads: 11 This Week

Last Update: 7 days ago
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3

Jupyter Dash

Dash v2.11+ has Jupyter support built in

Dash 2.11 and later supports running Dash apps in classic Jupyter Notebooks and in JupyterLab without the need to update the code or use the additional JupyterDash library. If you are using an earlier version of Dash, you can run Dash apps in a notebook using JupyterDash. This page documents additional options available when running Dash apps in notebooks as well as troubleshooting information.

Downloads: 9 This Week

Last Update: 2023-12-18
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4

Jupyter Themes

Custom Jupyter Notebook Themes

...It ships a catalog of popular dark and light themes and lets you customize fonts, font sizes, cell widths, and toolbar visibility so the notebook matches your preferred reading and coding ergonomics. The theming system adjusts CodeMirror syntax highlighting to keep code legible against chosen backgrounds and provides options to harmonize matplotlib/plotly colors for cohesive visuals. Installation and usage are streamlined through a single jt command, with flags to set or preview themes and a built-in restore option to return to defaults. Because it modifies the Notebook’s CSS, the project focuses on lightweight overrides rather than invasive changes, minimizing breakage across notebook versions. ...

Downloads: 1 This Week

Last Update: 2025-08-25
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