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
🍊 :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
A flexible package manager that supports multiple versions, configurations, platforms, and compilers.

Spack Spack is a multi-platform package manager that builds and installs multiple versions and configurations of software. It works on Linux, macOS, a

Spack 3.1k Dec 31, 2022
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
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
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
SCICO is a Python package for solving the inverse problems that arise in scientific imaging applications.

Scientific Computational Imaging COde (SCICO) SCICO is a Python package for solving the inverse problems that arise in scientific imaging applications

Los Alamos National Laboratory 37 Dec 21, 2022
Program that estimates antiderivatives utilising Maclaurin series.

AntiderivativeEstimator Program that estimates antiderivatives utilising Maclaurin series. Setup: Needs Python 3 and Git installed and added to PATH.

James Watson 3 Aug 04, 2021
AnuGA for the simulation of the shallow water equation

ANUGA Contents ANUGA What is ANUGA? Installation Documentation and Help Mailing Lists Web sites Latest source code Bug reports Developer information L

Geoscience Australia 147 Dec 14, 2022
Animation engine for explanatory math videos

Manim is an engine for precise programatic animations, designed for creating explanatory math videos. Note, there are two versions of manim. This repo

Grant Sanderson 48.9k Jan 03, 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
Incubator for useful bioinformatics code, primarily in Python and R

Collection of useful code related to biological analysis. Much of this is discussed with examples at Blue collar bioinformatics. All code, images and

Brad Chapman 560 Dec 24, 2022
collection of interesting Computer Science resources

collection of interesting Computer Science resources

Kirill Bobyrev 137 Dec 22, 2022
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
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
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
Algorithms covered in the Bioinformatics Course part of the Cambridge Computer Science Tripos

Bioinformatics This is a repository of all the algorithms covered in the Bioinformatics Course part of the Cambridge Computer Science Tripos Algorithm

16 Jun 30, 2022
Data intensive science for everyone.

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

Galaxy Project 1k 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
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
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