AWS ready Jupyter LAB for datascience

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 -- data & knowledge experts --                              

Base for this docker image is the jupyter-pyspark-notebook

We have extended the image for use with AWS and included some Jupyter Lab extension already. Check out our cookiecutter template to get a jump start, including some instructional examples.

Python Packages added

• boto3 - python library for AWS api
• faker - create fake data
  • faker_music - faker provider for music generes and instruments
  • faker-vehicle - faker provider for vehicle related data
• s3fs - simplified interface for AWS S3
• plotly - interactive, open-source, and browser-based graphing library
• psycopg2-binary - PostgreSQL database adapter

Jupyter extensions added

• spellchecker - spell checker for English, French, German, + more
• Variable Inspector
• Code Formatter - pretty format your python code
  • iSort - required for code formatter
  • black - the actual code formatter used
• IPyDrawiO - create DrawIO images
• Language Server protocol implementation - code completion functionality
  • Jedi Langauge Server - for code completion
• Chart Editor - easy work with plotly charts
  • jupyterlab-plotly - required for chart editor to work
• ipython-sql -run sql directly in a notebook cell
• Interactive Tables

Image local build

we use python invoke instead of unix make. The advantage is, that it runs where python is installed, including MS Windows.

to run a local build

invoke build-local

The result is the tuteco/jupyter_datascience_pyspark:latest-dev container image. Be aware that the local build has the tag "latest-dev".