Baseline Scientific Python conda enviroment using VSCode for Jupyter

setup-jupyter-py-sci-conda-env.sh

#!/bin/sh -
# 
# Install baseline scientific packages including Jupyter kernel (e.g. into 'base' enviroment):
conda install sympy tabulate seaborn scipy ipykernel
# That's it, you don't need to install all of Jupyter into the enviroment if you "connect" from another 
# Jupyter instance, e.g. through VS Code with the Jupyter extension, to this enviroment; the 'ipykernel' 
# is sufficient. NumPy, matplotlib, pandas, etc. are part of the dependencies of above packages. Install 
# panda's recommended dependencies if numeric operations need a speed up, esp. for large data sets; ref. 
# https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#install-recommended-dependencies
# Use 'pytables' or 'h5py' for very large data sets. For interactive plots, use 'bokeh'. Other interesting 
# packages are e.g. at https://numfocus.org/sponsored-projects/?_sft_project_category=python-interface.
# 
# VS Code shall have the following extensions (plus dependencies) installed: 
#   - Jupyter
#   - Pylance/Python
#   - Python Environment Manager
# Then configure VS Code to select the Python Interpreter from the desired conda enviroment (e.g. 'base').
# VS Code / Python Environment Manager did not immediately identify the conda enviroment, but eventually 
# did after an editor restart. Not sure I did something extra in the configuration, though. Suggestion from 
# VS Code / Python was then also to set 'terminal.integrated.inheritEnv' to false, which I did, too; ref. 
# https://code.visualstudio.com/updates/v1_36#_launch-terminals-with-clean-environments.
# 
#EOF