![]() ![]() ![]() jupyter/tensorflow-notebook: Everything in jupyter/scipy-notebook image with TensorFlow.jupyter/datascience-notebook: Everything in jupyter/scipy-notebook and jupyter/r-notebook images with Julia support.jupyter/r-notebook: Jupyter Notebook/JupyterLab with R interpreter, IRKernel, and devtools.jupyter/scipy-notebook: Jupyter Notebook/JupyterLab with conda/ mamba , ipywidgets, and popular packages from the scientific Python ecosystem ( Pandas, Matplotlib, Seaborn, Requests, etc.).Jupyter Docker Stacks provide various images for developers based on their requirements such as: You can share the notebooks, Dockerfile, dependencies-list files with your colleagues, then they just run one or two commands to run the same environment. If you need additional libraries that are not preinstalled with the image, you can create your image with a Dockerfile to install those libraries.ĭocker also helps the team share the development environment by letting your peers replicate the same environment easily.Run a command to pull an image that contains Jupyter and preinstalled packages based on the image type.Install Docker and sign up for the DockerHub website (free).With Jupyter Docker Stacks, the setup environment part is reduced to just the following steps: ![]() The Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools with build-in scientific, mathematical, and data analysis libraries pre-installed. This article is the first part of the series that demonstrates how to set up Jupyter Notebook environment with Docker to consume and display financial data from Refinitiv Data Platform without need to install the steps above. The article covers Jupyter with the Python programming language. If you are using R, please see the second article. 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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