#How to install jupyter notebook on docker how to#
You may think Docker is for the DevOps or the hardcore Developers only, but the Jupyter Docker Stacks simplifies how to create a ready-to-use Jupyter application with Data Science/Financial libraries in a few commands. The good news is you can reduce the effort to set up the workbench with the Docker containerization platform. If you need to share your code/project with your peers, the task to replicate the above steps in your collogues environment is very complex too. If you are using Julia, Install Julia and then its libraries.If you are using R, install R and then its libraries.Install Data Science libraries such as Matplotlib, Pandas, Plotly, Bokeh, etc.Create a new virtual environment (It is not recommended to install programs into your base environment).You need to install a lot of software and libraries in the correct order to set up your Data Science development environment.
One of the hardest parts of being Data Developers is the step to set up those tools. The Data Scientists and Financial coders need to interact with various Data Science/Financial development tools such as the Anaconda (or Miniconda) Python distribution platform, the Python programming language, the R programming language, Matplotlib library, Pandas Library, the Jupyter application, and much more.
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