Search Results for "jupyter"
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Showing 50 open source projects for "jupyter"
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Pretty Jupyter
Creates dynamic html report from jupyter notebook.
Pretty Jupyter is an easy-to-use package that allows to create beautiful & dynamic HTML reports. Most of the features require little to no work to get working and greatly improve the quality of the output report, or even the developer’s comfort when creating the report. For example, tabs make some visualizations much more comfortable. The features are integrated directly into the output page, therefore there is no need to have an interpreter running in the backend. -
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Best-of Jupyter
A ranked list of awesome Jupyter Notebook, Hub and Lab projects
This curated list contains 300 awesome open-source projects with a total of 360K stars grouped into 13 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. -
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OCaml Jupyter
An OCaml kernel for Jupyter (IPython) notebook
An OCaml kernel for Jupyter Notebook. This provides an OCaml REPL with a great user interface such as markdown/HTML documentation, LaTeX formula by MathJax, and image embedding. OCaml Jupyter requires the libraries zlib, libffi, libgmp, libzmq 5+. -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
Jupyter Docker Stacks provides a curated set of ready-to-run Docker container images that bundle Jupyter applications with popular data science and computing tools, enabling users to quickly start working in a reproducible environment. These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run consistently across machines. ... -
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Jupyter Nbextensions Configurator
A jupyter notebook serverextension providing config interfaces
A server extension for jupyter notebook which provides configuration interfaces for notebook extensions (nbextensions). The jupyter_nbextensions_configurator jupyter server extension provides graphical user interfaces for configuring which nbextensions are enabled (load automatically for every notebook). In addition, for nbextensions which include an appropriate yaml descriptor file (see below), the interface also renders their markdown readme files, and provides controls to configure the nbextensions' options. -
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Jupyter Notebook Tools for Sphinx
Sphinx source parser for Jupyter notebooks
nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluated notebooks – i.e. notebooks without stored output cells – will be automatically executed during the Sphinx build process. -
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notebooker
Productionise & schedule your Jupyter Notebooks
Productionise and schedule your Jupyter Notebooks, just as interactively as you wrote them. Notebooker is a webapp which can execute and parametrise Jupyter Notebooks as soon as they have been committed to git. The results are stored in MongoDB and searchable via the web interface, essentially turning your Jupyter Notebook into a production-style web-based report in a few clicks. -
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Jupynium
Selenium-automated Jupyter Notebook that is synchronised with NeoVim
It's just like a markdown live preview, but it's Jupyter Notebook live preview. Jupynium uses Selenium to automate Jupyter Notebook, synchronizing everything you type on Neovim. Never leave Neovim. Switch tabs on the browser as you switch files on Neovim. Note that it doesn't sync from Notebook to Neovim so only modify from Neovim. -
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nbdime
Tools for diffing and merging of Jupyter notebooks
nbdime provides tools for diffing and merging Jupyter notebooks. Jupyter notebooks are useful, rich media documents stored in a plain text JSON format. This format is relatively easy to parse. However, primitive line-based diff and merge tools do not handle well the logical structure of notebook documents. nbdime, on the other hand, provides “content-aware” diffing and merging of Jupyter notebooks. -
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pythonocc-demos
Examples and demos for the pythonocc CAD package
Some pythonocc-related code snippets, examples, Jupyter notebooks etc. -
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Spyder notebook plugin
Jupyter notebook integration with Spyder
Spyder plugin to use Jupyter notebooks inside Spyder. Currently, it supports basic functionality such as creating new notebooks, opening any notebook in your filesystem and saving notebooks at any location. You can also use Spyder's file switcher to easily switch between notebooks and open an IPython console connected to the kernel of a notebook to inspect its variables in the Variable Explorer. -
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Graph Notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop
The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs. -
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plotly.py
The interactive graphing library for Python
...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 online using Chart Studio Cloud. -
14
Behaviour Suite Reinforcement Learning
bsuite is a collection of carefully-designed experiments
...Each experiment in bsuite is meticulously designed to capture key challenges in RL, such as exploration, credit assignment, and stability. The framework supports automated logging and analysis, generating standardized output compatible with Jupyter notebooks for streamlined evaluation. It also integrates easily with existing RL libraries and can be used locally or via cloud computing platforms, including Google Cloud. -
15
mediapy
This Python library makes it easy to display images and videos
Read/write/show images and videos in an IPython/Jupyter notebook. -
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Otter-Grader
A Python and R autograding solution
...Otter supports local grading through parallel Docker containers, grading using the autograder platforms of 3rd party learning management systems (LMSs), the deployment of an Otter-managed grading virtual machine, and a client package that allows students to run public checks on their own machines. Otter is designed to grade Python scripts and Jupyter Notebooks, and is compatible with a few different LMSs, including Canvas and Gradescope. -
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Multik
Multidimensional array library for Kotlin
Multidimensional array library for Kotlin. -
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ipytest
Pytest in IPython notebooks
ipytest allows you to run Pytest in Jupyter notebooks. ipytest aims to give access to the full pytest experience and to make it easy to transfer tests out of notebooks into separate test files. -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work. -
20
GoNB
GoNB, a Go Notebook Kernel for Jupyter
Go is a compiled language, but with very fast compilation, that allows one to use it in a REPL (Read-Eval-Print-Loop) fashion, by inserting a "Compile" step in the middle of the loop -- so it's a Read-Compile-Run-Print-Loop — while still feeling very interactive. GoNB leverages that compilation speed to implement a full-featured (at least it's getting there) Jupyter notebook kernel. As a side benefit it works with packages that use CGO — although it won't parse C code in the cells, so it can't be used as a C kernel. It already includes many goodies: cache between cell of results, contextual help and auto-complete (with gopls), compilation error context (by mousing over), bash command execution, images, html, etc. ... -
21
Passport Index Dataset
Passport Index 2023: visa requirements for 199 countries, in .csv
There are 6 datasets with identical visa requirements data. Three datasets are matrix and three are long (tidy) formats. Each comes in 3 versions: with country codes as specified in ISO-2 (two-letter codes), ISO-3 (three-letter codes), and full country names from no particular standard. In distance matrices (files with matrix in the filename), the first column represents a passport (=from), each remaining column represents a destination (=to). Files in tidy format (with tidy in filename)... -
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Django Notebook
Django + shell_plus + Jupyter notebooks made easy
Django + shell_plus + Jupyter notebooks made easy. A Jupyter notebook with access to objects from the Django ORM is a powerful tool to introspect data and run ad-hoc queries. Built-in integration with the imported objects from django-extensions shell_plus. Saves the state between sessions so you don't need to remember what you did. Inheritance diagrams on any object, including ORM models. -
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PyQuil
A Python library for quantum programming using Quil
...However, you can also keep reading below to get started with running your first quantum program. Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter Notebook tutorials and examples. To run them in a preconfigured execution environment on Binder, click the "launch binder" badge at the top of the README or the link here! To learn more about the tutorials and how you can add your own, visit the rigetti/forest-tutorials repository. If you'd rather set everything up locally, or are interested in contributing to pyQuil, continue to the next section for instructions on installing pyQuil and the Forest SDK. -
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databooks
A CLI tool to reduce the friction between data scientists
databooks is a package to ease the collaboration between data scientists using Jupyter notebooks, by reducing the number of git conflicts between different notebooks and resolution of git conflicts when encountered. Simply specify the paths for notebook files to remove metadata. By doing so, we can already avoid many of the conflicts. Specify the paths for notebook files with conflicts to be fixed. Then, databooks finds the source notebooks that caused the conflicts and compares them (so no JSON manipulation!) ... -
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DeepMind Research
Implementations and code to accompany DeepMind publications
...Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. The codebase is primarily Jupyter Notebooks and Python, reflecting an emphasis on experimentation and pedagogy rather than production packaging.
