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
conda install pandas
conda install pytest
conda install sphinx

Now, we can go ahead and install our package into the environment by navigating to pkg and running make init which will use the Makefile to run the commands under init. We will go back to this Makefile throughout the project - it's a really easy way to track and organize simple collections of commands for different purposes. The file itself is easy to read, too.

With the environment activated and the package installed, we will be able to import fakeproj. Otherwise, the computer would have 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

Alternatively, they could use the Makefile:

conda env create -f env.yml
cd pkg
make init

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. If we hadn't used the -e option, the package would show up explicitly when we export the environment to env.yml, but we would need to reinstall it whenever we make a change (make install via the Makefile).

Another useful tip for working with packages alongside Jupyter Notebooks: add the following cell magic to the first cell in the notebook so that your import statements reflect the latest changes in your code:

%load_ext autoreload
%autoreload 2

Sphinx is a tool for automatic documentation of Python packages. It works together with docstrings to generate HTML documentation like what you would see on readthedocs. To generate documentation for the project, enter the pkg directory, and run make -C docs html. This will create a _build folder in the pkg/docs with HTML for a documents web page based on your docstrings. Big thanks to @enmyj for tackling this!

pytest is the go-to framework for unit- and integreation-testing in Python. Use make unittest, make integrationtest, and/or make alltests to run your tests.