A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Overview

Cookiecutter Data Science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Project homepage

Requirements to use the cookiecutter template:


  • Python 2.7 or 3.5+
  • 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 -c v1 https://github.com/drivendata/cookiecutter-data-science

asciicast

New version of Cookiecutter Data Science


Cookiecutter data science is moving to v2 soon, which will entail using the command ccds ... rather than cookiecutter .... The cookiecutter command will continue to work, and this version of the template will still be available. To use the legacy template, you will need to explicitly use -c v1 to select it. Please update any scripts/automation you have to append the -c v1 option (as above), which is available now.

The resulting directory structure


The directory structure of your new project looks like this:

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── 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.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Contributing

We welcome contributions! See the docs for guidelines.

Installing development requirements


pip install -r requirements.txt

Running the tests


py.test tests
Zipline, a Pythonic Algorithmic Trading Library

Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backte

Quantopian, Inc. 15.7k Jan 07, 2023
🍊 :bar_chart: :bulb: Orange: Interactive data analysis

Orange Data Mining Orange is a data mining and visualization toolbox for novice and expert alike. To explore data with Orange, one requires no program

Bioinformatics Laboratory 3.9k Jan 05, 2023
collection of interesting Computer Science resources

collection of interesting Computer Science resources

Kirill Bobyrev 137 Dec 22, 2022
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

Kenji Hiranabe 3.2k Jan 08, 2023
A mathematica expression evaluator with PokemonTypes

A simple mathematical expression evaluator that uses Pokemon types to replace symbols.

Arnav Jindal 2 Nov 14, 2021
ReproZip is a tool that simplifies the process of creating reproducible experiments from command-line executions, a frequently-used common denominator in computational science.

ReproZip ReproZip is a tool aimed at simplifying the process of creating reproducible experiments from command-line executions, a frequently-used comm

267 Jan 01, 2023
Doing bayesian data analysis - Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

Doing_bayesian_data_analysis This repository contains the Python version of the R programs described in the great book Doing bayesian data analysis (f

Osvaldo Martin 851 Dec 27, 2022
OPEM (Open Source PEM Fuel Cell Simulation Tool)

Table of contents What is PEM? Overview Installation Usage Executable Library Telegram Bot Try OPEM in Your Browser! MATLAB Issues & Bug Reports Contr

ECSIM 133 Jan 04, 2023
3D visualization of scientific data in Python

Mayavi: 3D visualization of scientific data in Python Mayavi docs: http://docs.enthought.com/mayavi/mayavi/ TVTK docs: http://docs.enthought.com/mayav

Enthought, Inc. 1.1k Jan 06, 2023
PsychoPy is an open-source package for creating experiments in behavioral science.

PsychoPy is an open-source package for creating experiments in behavioral science. It aims to provide a single package that is: precise enoug

PsychoPy 1.3k Dec 31, 2022
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Dec 31, 2022
A modular single-molecule analysis interface

MOSAIC: A modular single-molecule analysis interface MOSAIC is a single molecule analysis toolbox that automatically decodes multi-state nanopore data

National Institute of Standards and Technology 35 Dec 13, 2022
Efficient Python Tricks and Tools for Data Scientists

Why efficient Python? Because using Python more efficiently will make your code more readable and run more efficiently.

Khuyen Tran 944 Dec 28, 2022
An open-source application for biological image analysis

CellProfiler is a free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure

CellProfiler 734 Jan 08, 2023
artisan: visual scope for coffee roasters

Artisan Visual scope for coffee roasters WARNING: pre-release builds may not work. Use at your own risk. Summary Artisan is a software that helps coff

Artisan – Visual Scope for Coffee Roasters 705 Jan 05, 2023
A framework for feature exploration in Data Science

Beehive A framework for feature exploration in Data Science Background What do we do when we finish one episode of feature exploration in a jupyter no

Steven IJ 1 Jan 03, 2022
SeqLike - flexible biological sequence objects in Python

SeqLike - flexible biological sequence objects in Python Introduction A single object API that makes working with biological sequences in Python more

186 Dec 23, 2022
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code

A Python framework for creating reproducible, maintainable and modular data science code.

QuantumBlack Labs 7.9k Jan 01, 2023
Python Data Science Handbook: full text in Jupyter Notebooks

Python Data Science Handbook This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. How to Use th

Jake Vanderplas 36.9k Dec 28, 2022
CoCalc: Collaborative Calculation in the Cloud

logo CoCalc Collaborative Calculation and Data Science CoCalc is a virtual online workspace for calculations, research, collaboration and authoring do

SageMath, Inc. 1k Dec 29, 2022