This repo demonstrates how I like to set up a data science project in Python. There are three top-level folders in the simplest version of this file structure:

• notebooks: (initially) flat directory of notebooks
• scripts: (intitially) flat directory fo scripts
• pkg: folder containing the project's internal package

conda is where it's at for making sure you can replicate your environments over time and across projects. Plus, it prevents us from needing to reinstall our package everytime we want to use it. Here's how we initially created it:

conda create env -n ds-project-template
conda activate ds-project-template
conda install jupyterlab
pip install -e pkg

Now, if the environment is activated, we will be able to import fakeproj. Otherwise, the computer has no idea what fakeproj is. We can also export the environment:

conda env export > env.yml

This produces a yaml file capturing all of the conda and pip packages installed in your ds-project-template environment. Although the environment gives us access to fakeproj when we activate it, that is not reflected in the yaml file itself, so when you share the project with another person, they'll need to create the environment and install fakeproj into it:

conda env create -f env.yml
pip install -e pkg

The -e part of installing fakeproj stands for --editable, and it allows us to develop the package as we go. That way, when we make a change to the package, we don't have to reinstall it! You will, however, need to reimport it.