This tutorial covers key visualization libraries from Matplotlib, Holoviews, and Seaborn to support Civil Engineers and Hydrologists training with fundamental skills in Python. The .ipynb files in this tutorial have been modified from other, with the original authors referenced the respective readme.md files.
Please fork this repo to your GitHub account. Next, identify a folder location where you would like to work in a development environment. Using the command prompt, change your working directory to this folder and git clone https://github.com/USERID/CommunicatingData-Plotting
git clone https://github.com/USERID/CommunicatingData-Plotting
It is a best practice to create a virtual environment when starting a new project, as a virtual environment essentially creates an isolated working copy of Python for a particular project. I.e., each environment can have its own dependencies or even its own Python versions. Creating a Python virtual environment is useful if you need different versions of Python or packages for different projects. Lastly, a virtual environment keeps things tidy, makes sure your main Python installation stays healthy and supports reproducible and open science.
cd CommunicatingData-Plotting
conda env create -f 310environment.yml
conda activate p310env
In some cases:
conda install ipykernel
python -m ipykernel install --user --name=p310env