Hydrogen is an interactive coding environment that supports Python, R, JavaScript and other Jupyter kernels. Checkout our Documentation and Medium blog post to see what you can do with Hydrogen.
https://nteract.gitbooks.io/hydrogen/Tags | data-science jupyter ipython repl hydrogen atom jupyter-kernels nteract execute run julia torch ijulia irkernel itorch plot image |
Implementation | Javascript |
License | MIT |
Platform | OS-Independent |
nteract is first and foremost a dynamic tool to give you flexibility when writing code, exploring data, and authoring text to share insights about the data. Edit code, write prose, and visualize.
notebook nteract data-science repl ipython jupyter jupyter-notebook desktop-application react react-components zeromq monorepo electron dataAcknowledgements - This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. Important Note - gomacro relies on the plugin package when importing third party libraries. This package works reliably on Mac OS X only with Go 1.10.2+ as long as you never execute the command strip gophernotes. If you can only compile gophernotes with Go <= 1.10.1 on Mac, consider using the Docker install and run gophernotes/Jupyter in Docker.
jupyter jupyter-notebook kernel gophernotes zeromq nteract data-science machine-learning artificial-intelligence numerical-methodsIJulia is a Julia-language backend combined with the Jupyter interactive environment (also used by IPython). This combination allows you to interact with the Julia language using Jupyter/IPython's powerful graphical notebook, which combines code, formatted text, math, and multimedia in a single document. to install IJulia.
This project showcases how you can use fastpages to create a static dashboard that update regularly using Jupyter Notebooks. Using fastpages, data professionals can share dashboards (that are updated with new data automatically) without requiring any expertise in front end development. The content of this site shows statistics and reports regarding Covid-19.
github-pages data-science jupyter analytics nteract data-visualisation matplotlib pymc3 fastai altair papermill github-actions covid-19 covid19 covid-data fastpagesIPython provides a rich toolkit to help you make the most of using Python interactively. It provides a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends.
interpreter python-interpreter curses terminal jupyter data-science notebook replJupyter Scala is a Scala kernel for Jupyter. It aims at being a versatile and easily extensible alternative to other Scala kernels or notebook UIs, building on both Jupyter and Ammonite. The current version is available for Scala 2.11. Support for Scala 2.10 could be added back, and 2.12 should be supported soon (via ammonium / Ammonite).
jupyter jupyter-notebook repl jupyter-kernelsiTorch in notebook mode works like any other IPython notebook. It provides useful inline auto-complete. Whenever you need auto-complete, use the TAB key.In addition, we introduce visualization functions for images, video, audio, html and plots.
iTorch in notebook mode works like any other IPython notebook. It provides useful inline auto-complete. Whenever you need auto-complete, use the TAB key. In addition, we introduce visualization functions for images, video, audio, html and plots.
Now both R versions are available as an R kernel in the notebook. If you have Jupyter installed, you can create a notebook using IRkernel from the dropdown menu.
jupyter jupyter-notebook jupyter-kernels r zmqThis is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.
spark pyspark data-analysis mllib ipython-notebook notebook ipython data-science machine-learning big-data bigdataPapermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. To parameterize your notebook designate a cell with the tag parameters.
jupyter notebooks notebook-generator nteract publishing pipelineThis library makes it easy to develop Plotly Dash apps interactively from within Jupyter environments (e.g. classic Notebook, JupyterLab, Visual Studio Code notebooks, nteract, PyCharm notebooks, etc.). See the notebooks/getting_started.ipynb for more information and example usage.
jupyter jupyter-notebook dash plotly-dashTel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning. Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.
gpu nvidia docker-image machine-learning deep-learning data-science cuda-kernels kaggle-competition cuda pytorch pytorch-tutorials pytorch-tutorial bootcamp meetup kaggle kaggle-scripts pycudaLets-Plot is an open-source plotting library for statistical data. It is implemented using the Kotlin programming language. The design of Lets-Plot library is heavily influenced by Leland Wilkinson work The Grammar of Graphics describing the deep features that underlie all statistical graphics.
kotlin data-science jupyter plot data-visualization pycharm plot-library jupyter-notebooks statistical-data geo-spatial ggplot datalore sciview sciview-pluginThis repo is my workspace for developing a cycle of course materials, IPython notebooks, and tutorials towards an academic urban data science course based on Python. Between Fall 2013 and Fall 2016, I was the grad student instructor (3 years) and co-lead instructor (1 year) for CP255, Urban Informatics and Visualization, at UC Berkeley. This course was developed by Paul Waddell and is ongoing at Berkeley with the fantastic contributions of @Arezoo-bz. If you're interested in these topics at all, you owe it to yourself to check out the latest iterations of Paul's excellent pedagogy in his CP255 repo. A couple years ago, I wrote this blog post describing our efforts for the course.
For further information and details continue through to the documentation.
jupyter jupyter-notebook osx ipython ipython-notebook vscode data-scienceThis repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.
scikit-learn numpy jupyter-notebook matplotlib pandasThe ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. The workspace requires Docker to be installed on your machine (📖 Installation Guide).
nlp docker kubernetes data-science machine-learning r deep-learning jupyter anaconda tensorflow gpu scikit-learn vscode jupyter-notebook data-visualization pytorch neural-networks data-analysis jupyter-labWith JupyterHub you can create a multi-user Hub which spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high performance computing group.
jupyter-notebook jupyterhub multi-user ipythonBokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients. These Jupyter notebooks provide useful Bokeh examples and a tutorial to get started. You can visualize the rendered Jupyter notebooks on NBViewer or download the repository and execute jupyter notebook from your terminal.
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