Jupyter is one of the most popular data science tools around. It's an essential in helping data scientists manage their projects and even work together. Take a look inside Jupyter and find out how to get started quickly.
by Graham Wheeler on #Jupyter, #Python, 2016-02-27 20:00 The version of Python running the notebooks created by Jupyter Hub can contain the full Anaconda distribution of packages plus some extra packages like pint and pyserial. This is tutorial on running Project Jupyter Notebook on an Amazon EC2 instance. Installing and using the multi-language Jupyter notebook environment. Posts about Jupyter Notebooks written by spaghettidba A system for assigning and grading notebooks. Contribute to jupyter/nbgrader development by creating an account on GitHub.
Installing and using the multi-language Jupyter notebook environment. Posts about Jupyter Notebooks written by spaghettidba A system for assigning and grading notebooks. Contribute to jupyter/nbgrader development by creating an account on GitHub. A Jupyter/Jupyterlab extension to make, download and extract archive files. - hadim/jupyter-archive Convenience scripting for managing a local Jupyter docker image for running notebooks - sernst/Jupyter-Docker A Jupyter kernel for CASA. Contribute to aardk/jupyter-casa development by creating an account on GitHub.
Create interactive screencasts inside Jupyter Notebook that anybody can play back - willkessler/jupytergraffiti jovyan@jupyter-user:~$ wget https://github.com/uc-cdis/cdis-data-client/releases/download/0.3.0/dataclient_linux.zip Resolving github-production-release-asset-2e65be.s3.amazonaws.com Connecting to github-production-release-asset-2e65be.s3… Building data science pipelines with a Jupyter container that uses Mysql, Postgres, and Redis with docker compose for deployment. Examples included: stock price plotting, mysql etl, redis caching, and core demos Kernels can now opt to be interrupted by a message sent on the control channel instead of a system signal. See Kernel specs and Kernel interrupt (PR #294). Create interactive, shareable notebooks with live code, equations, visualizations, and text using Jupyter.
Written by Dan Park the 03 Feb 2019 - Tagged: Guide | Python | AWS | Jupyter To run any Jupyter Notebook, you will need to install Jupyter. The easiest way to do this is by installing Anaconda: https://www.anaconda.com/download/ One of the ideas pitched by Daniel Mietchen at the London Open Research Data do-a-thon for Open Data Day 2017 was to analyse Jupyter Notebooks mentioned in PubMed Central. Jupyter Notebook (GraphFrames and NetworkX) This notebook is shipped by the bitcoin-notebook container and it is called blockchain.ipynb. Parses a Jupyter book HTML and extracts the images. The first line downloads the file and stores it in the file indicated by the URL. The second line imports the file to Jupyter Notebook.
The Jupyter notebook enables you to fetch raw files and download them, and even exposes a download button. Due to security and compliant reasons, you would want to limit your data scientists to access data from the notebook instance only and…
