#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS HOW TO#
#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS INSTALL#
#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS CODE#
#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FREE#
#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS WINDOWS#

At the end run вЂњgit checkout masterвЂќ to go back to the current state. If you want to temporarily go back to a commit, checkout the files, and come back to where you are then you can simply checkout the desired commit. Or run git merge + git push from command line, Once your changes are approved you can merge them from GitHub UI. It shows you rich diffs & lets you comment on any notebook cell to discuss changes with your team. You can use ReviewNB to solve the notebook diffвЂ™ing problem. But in case of Jupyter, GitHub shows JSON diffs which are really hard to read (see below). GitHub pull request are fantastic for peer review as they let you see changes side-by-side & comment on them. On the next page provide title & describe your changes in brief, hit вЂњCreate pull requestвЂќ again. Go to your Project page -> Pull requests tab -> click вЂњNew pull requestвЂќ.Д¬hoose which branch youвЂ™d like to merge into master. You can create pull requests from GitHub UI. Most likely, youвЂ™d want to first share it with your peers, get their feedback before merging it into master branch. LetвЂ™s say youвЂ™ve been working on feature branch for a while, and itвЂ™s ready for prime time. > git push -set-upstream origin customer_data_insightsДЄnd then do git push to push your commits to this newly created branch. Setup your name & email in git by running following commands on terminal

#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS INSTALL#

Download and install the latest version of Git.

If you donвЂ™t have a GitHub account please create one here.

Easily share your notebooks for others to view.

Get feedback & discuss notebook changes with your peers.

Review Jupyter notebook pull requests on GitHub.

#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS HOW TO#

Learn how to revert to a specific notebook version.Push your notebooks to a GitHub repository in cloud.

#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FREE#

Feel free to skip a section if youвЂ™re already familar with it. This is a hands on tutorial for beginners & is meant to be comprehensive. Advanced Git users might want to check out integration workflow, real world examples and FAQs for making notebooks play nicely with GitHub. This article is a Git 101 for Jupyter users. Amy says that she has already received lots of interest and feedback.How to use Git / GitHub with Jupyter Notebook This is just the start for SAS in the Jupyter world.

The GitHub project has all of the doc and step-by-step instructions for installation.

#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS WINDOWS#

In the SAS Tech Talk video with Amy, we were running on my Windows laptop using Chrome, connecting to a Linux instance of Jupyter and SAS. And you can access it from a browser on any system: Windows, Mac, Linux.whatever.

Access to the OS shell to install/configure the Jupyter Notebook infrastructure and the sas_kernel.Д®nd users of Jupyter Notebook do not need special privileges.

Python 3 installed on the same machine (that's basically part of Linux).

SAS 9.4 or later running on Windows, Linux, or even z/OS ( see support for SASPy, the underlying package).

Here's what you need to run SAS with Jupyter: This allows you to move easily between Python and SAS in a single environment.

#HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS CODE#

From within a Python language notebook, you can inject your SAS program code and pull in SAS results. These magic commands look almost just like SAS macro calls (imagine that!). The second way that you can run SAS code is by using special Jupyter 'magics' supported by the sas_kernel. nbextensions/showSASLog) that can show you the SAS log. The most natural method is to create a new SAS notebook, available from the New menu in the Jupyter Home window and from the File menu in an active notebook:ДЇrom a SAS notebook, you can enter and run SAS code directly from a cell: Within Jupyter, the sas_kernel provides multiple ways to access SAS programming methods. Visit the project on GitHub: sas_kernel by sassoftware If you want to learn more about Jupyter and see the SAS support in action, then you can watch the video here. My colleague Amy Peters announced this during a SAS Tech Talk show at SAS Global Forum 2016. With this new project on the /sassoftware page, SAS contributes new support for running SAS from within Jupyter Notebooks - a popular browser-based environment used by professors and data scientists. We've just celebrated Earth Day, but I'm here to talk about Jupyter - and the SAS open source project that opens the door for more learning.

How to run jupyter notebook on windows