Search Results for "jupyter notebook" - Page 3
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Showing 79 open source projects for "jupyter notebook"
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1
Bloxs
Build dashboards in Jupyter Notebook with numeric and chart boxes
Bloxs is a simple Python package that helps you display information in an attractive way (formed in blocks). Perfect for building dashboards, reports and apps in the notebook. -
2
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. -
3
ipycytoscape
A Cytoscape Jupyter widget
A widget enabling interactive graph visualization with cytoscape.js in JupyterLab and the Jupyter Notebook. -
4
AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
...These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance. -
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5
Jupyter Notebooks as PDF
Save Jupyter Notebooks as PDF
This Jupyter notebook extension allows you to save your notebook as a PDF. To make it easier to reproduce the contents of the PDF at a later date the original notebook is attached to the PDF. Unfortunately not all PDF viewers know how to deal with attachments. PDF viewers known to support downloading of file attachments are: Acrobat Reader, pdf.js and evince. -
6
Pandas TA
Python 3 Pandas Extension with 130+ Indicators
Technical Analysis Indicators - Pandas TA is an easy-to-use Python 3 Pandas Extension with 130+ Indicators. Pandas Technical Analysis (Pandas TA) is an easy-to-use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: Candle Pattern(cdl_pattern), Simple Moving Average (sma) Moving Average Convergence Divergence (macd), Hull Exponential Moving Average... -
7
ReinventCommunity
Jupyter Notebook tutorials for REINVENT 3.2
This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2. -
8
Kale
Kubeflow’s superfood for Data Scientists
...The self-service nature of Kubeflow make it extremely appealing for Data Science use, at it provides an easy access to advanced distributed jobs orchestration, re-usability of components, Jupyter Notebooks, rich UIs and more. Still, developing and maintaining Kubeflow workflows can be hard for data scientists, who may not be experts in working orchestration platforms and related SDKs. Additionally, data science often involve processes of data exploration, iterative modelling and interactive environments (mostly Jupyter notebook). -
9
earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples
earthengine-py-notebooks is a comprehensive collection of hundreds of Jupyter Python notebooks that serve as examples and tutorials for using the Google Earth Engine Python API. These notebooks are organized into thematic areas such as image processing, machine learning, visualization, filtering, and asset management, exposing users to real geospatial analysis tasks. The repository makes it easier to explore Earth Engine’s large geospatial data catalog, interactively display map layers, and... -
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10
nbdev template
Template for nbdev projects
Write, test, document, and distribute software packages and technical articles, all in one place, your notebook. Traditional programming environments throw away the result of your exploration in REPLs or notebooks. nbdev makes exploration an integral part of your workflow, all while promoting software engineering best practices. -
11
Jupytab
Display in Tableau data from Jupyter notebooks
Jupytab allows you to explore in Tableau data which is generated dynamically by a Jupyter Notebook. You can thus create Tableau data sources in a very flexible way using all the power of Python. This is achieved by having Tableau access data through a web server created by Jupytab. -
12
GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on... -
13
Python Handout
Turn Python scripts into handouts with Markdown and figures
Handout is a lightweight library for embedding rich, interactive components such as exercises, charts, and interactive diagrams directly into static documents like Markdown, Jupyter notebooks, or static HTML pages, enabling authors to create more engaging technical handouts, tutorials, and interactive essays. It’s particularly aimed at educators, presenters, and researchers who want to make their written material come alive with runnable demonstrations and interactive problem sets without... -
14
TensorWatch
Debugging, monitoring and visualization for Python Machine Learning
TensorWatch is an open source debugging and visualization platform created by Microsoft Research to support machine learning, deep learning, and reinforcement learning workflows. It enables developers to observe training behavior in real time through interactive visualizations, primarily within Jupyter Notebook environments. The tool treats most data interactions as streams, allowing flexible routing, storage, and visualization of metrics generated during model training. A distinctive capability is its “lazy logging” mode, which lets users query live training processes without pre-instrumenting all metrics ahead of time. TensorWatch supports multiple chart types and can be extended with custom visualizers and dashboards, making it highly adaptable for research workflows. ... -
15
PixieDust
Python Helper library for Jupyter Notebooks
PixieDust is an open source Python helper library that works as an add-on to Jupyter notebooks to improve the user experience of working with data. It also fills a gap for users who have no access to configuration files when a notebook is hosted on the cloud. -
16
TensorFlow Course
Simple and ready-to-use tutorials for TensorFlow
This repository houses a highly popular (~16k stars) set of TensorFlow tutorials and example code aimed at beginners and intermediate users. It includes Jupyter notebooks and scripts that cover neural network fundamentals, model training, deployment, and more, with support for Google Colab. -
17
Dive-into-DL-TensorFlow2.0
Dive into Deep Learning
...In addition, this project also refers to the project Dive-into-DL-PyTorch , which refactored PyTorch in the Chinese version of this book, and I would like to express my gratitude here. This repository mainly contains two folders, code and docs (plus some data stored in data). The code folder is the relevant jupyter notebook code for each chapter (based on TensorFlow2); the docs folder is the relevant content in the book. -
18
Azure Machine Learning Python SDK
Python notebooks with ML and deep learning examples
...Because it is designed to work with Azure Machine Learning compute instances, many notebooks can be executed directly in the cloud without additional setup, but they can also run locally with the appropriate SDK and packages installed. Each notebook includes code, narrative explanations, and example workflows that help users build reproducible machine learning solutions, which are key for operationalizing models in production. -
19
2D Structural Analysis
Determine the bending moments, shear forces, axial forces and displace
2D Structural Analysis in Python by Ritchie Vink A collection examples of 2D Finite Element Analysis (FEA) made with Jupyter Notebook Lab - https://jupyter.org/ To install Jupyter - https://jupyter.org/install ===== App is available on Play Store -> https://play.google.com/store/apps/details?id=com.ulm.struct If you want to download a python editor for your android smartphone follow this link -> https://play.google.com/store/apps/details?id=com.ulm.python -
20
pyfolio
Portfolio and risk analytics in Python
pyfolio is a Python library for performance and risk analysis of financial portfolios developed by Quantopian Inc. It works well with the Zipline open source backtesting library. At the core of pyfolio is a so-called tear sheet that consists of various individual plots that provide a comprehensive image of the performance of a trading algorithm. Here's an example of a simple tear sheet analyzing a strategy. Quantopian also offers a fully managed service for professionals that includes... -
21
Jupyter Themes
Custom Jupyter Notebook Themes
jupyter-themes brings theme management to classic Jupyter Notebooks with a command-line tool that can restyle the interface, code cells, and UI chrome in seconds. 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. -
22
Jupyter Server Proxy
Jupyter notebook server extension to proxy web services.
Jupyter Server Proxy lets you run arbitrary external processes (such as RStudio, Shiny Server, Syncthing, PostgreSQL, Code Server, etc) alongside your notebook server and provide authenticated web access to them using a path like /rstudio next to others like /lab. Alongside the Python package that provides the main functionality, the JupyterLab extension (@jupyterhub/jupyter-server-proxy) provides buttons in the JupyterLab launcher window to get to RStudio for example. -
23
GeoNotebook
A Jupyter notebook extension for geospatial visualization and analysis
GeoNotebook is an open-source extension to the Jupyter Notebook ecosystem that equips users with powerful geospatial visualization and analysis capabilities directly within the notebook interface. It integrates with GeoJS and other geospatial services to enable rich, interactive map rendering, layer control, and GIS data manipulation alongside traditional code and markdown cells in a Jupyter environment. -
24
Python/xarray tutorial
Python/xarray tutorial for GEOS-Chem users
If the page is loaded successfully, you should see a Jupyter notebook interface. Then, click on the first notebook to get started. Jupyter combines Python code, execution results, plots, custom texts, and even Latex formulas in a single page. Besides using the Jupyter program, you can also view the static notebook on GitHub (e.g the first notebook). Python is free & open-source so can be easily installed on any machines. -
25
pivottablejs
Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook
PivotTable.js is a Javascript Pivot Table and Pivot Chart library with drag-drop interactivity, and it can now be used with Jupyter/IPython Notebook via the pivottablejs module. I first built PivotTable.js with a plan to build an in-browser data analysis tool, and got as far as one where you could load up a CSV file in the browser for display. Since then, however, the Jupyter project has gathered steam and now provides a browser-based interface to some of the most powerful data processing libraries in the world, so it makes sense to interface with it.
