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
Book on Julia for Data Science

Book on Julia for Data Science

Julia Data Science 349 Dec 25, 2022
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Cookiecutter Data Science A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. Project homepage

6.4k Jan 02, 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
PennyLane is a cross-platform Python library for differentiable programming of quantum computers.

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

PennyLaneAI 1.6k Jan 04, 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 computer algebra system written in pure Python

SymPy See the AUTHORS file for the list of authors. And many more people helped on the SymPy mailing list, reported bugs, helped organize SymPy's part

SymPy 9.9k Jan 08, 2023
Mathics is a general-purpose computer algebra system (CAS). It is an open-source alternative to Mathematica

Mathics is a general-purpose computer algebra system (CAS). It is an open-source alternative to Mathematica. It is free both as in "free beer" and as in "freedom".

Mathics 535 Jan 04, 2023
Read-only mirror of https://gitlab.gnome.org/GNOME/pybliographer

Pybliographer Pybliographer provides a framework for working with bibliographic databases. This software is licensed under the GPLv2. For more informa

GNOME Github Mirror 15 May 07, 2022
CS 506 - Computational Tools for Data Science

CS 506 - Computational Tools for Data Science Code, slides, and notes for Boston University CS506 Fall 2021 The Final Project Repository can be found

Lance Galletti 14 Mar 23, 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
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
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
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
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
Float2Binary - A simple python class which finds the binary representation of a floating-point number.

Float2Binary A simple python class which finds the binary representation of a floating-point number. You can find a class in IEEE754.py file with the

Bora Canbula 3 Dec 14, 2021
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
Wikidata scholarly profiles

Scholia is a python package and webapp for interaction with scholarly information in Wikidata. Webapp As a webapp, it currently runs from Wikimedia To

Finn Årup Nielsen 180 Dec 28, 2022
An interactive explorer for single-cell transcriptomics data

an interactive explorer for single-cell transcriptomics data cellxgene (pronounced "cell-by-gene") is an interactive data explorer for single-cell tra

Chan Zuckerberg Initiative 424 Dec 15, 2022
3D medical imaging reconstruction software

InVesalius InVesalius generates 3D medical imaging reconstructions based on a sequence of 2D DICOM files acquired with CT or MRI equipments. InVesaliu

443 Jan 01, 2023
Veusz scientific plotting application

Veusz 3.3.1 Veusz is a scientific plotting package. It is designed to produce publication-ready PDF or SVG output. Graphs are built-up by combining pl

Veusz 613 Dec 16, 2022