How to run a Streamlit app in place of the regular Jupyter notebook server, so your JupyterHub provides authentication for the app
Extend JupyterHub so colleagues and clients can interact with secure user-friendly versions of your notebooks
Streamlit is an exciting new framework for building beautiful data science dashboards. But how can you share your app with your clients?
How to share a Jupyter presentation as a standalone VirtualBox image when your client isn’t able to work with containers or the cloud
A lot of data science visualizations are really throwaway web apps that aren’t intended to be public, or even cloud-based for private users. It’s not worth the infrastructure (and security risk) of locking down a hosting service to serve these given their short shelf-life. In fact, the web technologies employed are really only ‘borrowed’ from different scenarios where cloud or internet is important. It is only by accident that our visualizations were built with web-style technologies in the first place… so can we shift our thinking to take our head out of the clouds completely?
Ideonate has released ContainDS: a simple user interface allowing you to easily run Jupyter Lab or Notebooks through virtual environments on your Windows or Mac computer.
An easier way to run reusable and independent Jupyter environments without learning Docker
Recently you may have seen the very insightful article called Jupyter is the new Excel, written by Semi Koen. She describes how many professionals immediately reach for Excel when faced with data-heavy analysis tasks, but she argues that for many ‘big data’ challenges it would make sense to work within a Jupyter Notebook instead.
I am pleased to announce the release of nb2xls: Convert Jupyter notebooks to Excel Spreadsheets, through a new ‘Download As’ option or via nbconvert on the command line.
In a data science or machine learning project, you may prepare and study images or other data within a Jupyter notebook then need to annotate the data to augment the training or fix errors in your source data.