Cookiecutter EasyData

A flexible (but opinionated) toolkit for doing and sharing reproducible data science.

EasyData started life as an experimental fork of cookiecutter-data-science where we could try out ideas before proposing them as fixes to the upstream branch. It has grown into its own toolkit for implementing a reproducible data science workflow, and is the basis of our Bus Number tutorial on Reproducible Data Science.

Tutorial

For a tutorial on making use of a previous version of this framework (available via the bus_number branch), visit: https://github.com/hackalog/bus_number/

Requirements to use this cookiecutter template:

• anaconda (or miniconda)

• python3.6+ (we use f-strings. So should you)

• Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

---

cookiecutter https://github.com/hackalog/cookiecutter-easydata

The resulting directory structure

---

The directory structure of your new project looks like this:

• LICENSE
• Makefile
  • top-level makefile. Type make for a list of valid commands
• README.md
  • this file
• catalog
  • Data catalog. This is where config information such as data sources and data transformations are saved
• data
  • Data directory. often symlinked to a filesystem with lots of space
  • data/raw
    • Raw (immutable) hash-verified downloads
  • data/interim
    • Extracted and interim data representations
  • data/processed
    • The final, canonical data sets for modeling.
• docs
  • A default Sphinx project; see sphinx-doc.org for details
• models
  • Trained and serialized models, model predictions, or model summaries
  • models/trained
    • Trained models
  • models/output
    • predictions and transformations from the trained models
• notebooks
  • Jupyter notebooks. Naming convention is a number (for ordering), the creator's initials, and a short - delimited description, e.g. 1.0-jqp-initial-data-exploration.
• references
  • Data dictionaries, manuals, and all other explanatory materials.
• reports
  • Generated analysis as HTML, PDF, LaTeX, etc.
  • reports/figures
    • Generated graphics and figures to be used in reporting
  • reports/tables
    • Generated data tables to be used in reporting
  • reports/summary
    • Generated summary information to be used in reporting
• environment.yml
  • (if using conda) The YAML file for reproducing the analysis environment
• setup.py
  • Turns contents of MODULE_NAME into a pip-installable python module (pip install -e .) so it can be imported in python code
• MODULE_NAME
  • Source code for use in this project.
  • MODULE_NAME/__init__.py
    • Makes MODULE_NAME a Python module
  • MODULE_NAME/data
    • Scripts to fetch or generate data. In particular:
    • MODULE_NAME/data/make_dataset.py
      • Run with python -m MODULE_NAME.data.make_dataset fetch or python -m MODULE_NAME.data.make_dataset process
  • MODULE_NAME/analysis
    • Scripts to turn datasets into output products
  • MODULE_NAME/models
    • Scripts to train models and then use trained models to make predictions. e.g. predict_model.py, train_model.py
• tox.ini
  • tox file with settings for running tox; see tox.testrun.org

Installing development requirements

The first time:

make create_environment
git init
git add .
git commit -m "initial import"
git branch easydata   # tag for future easydata upgrades

Subsequent updates:

make update_environment

In case you need to delete the environment later:

conda deactivate
make delete_environment