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
A simple computer program made with Python on the brachistochrone curve.

Brachistochrone-curve This is a simple computer program made with Python on the brachistochrone curve. I decided to write it after a physics lesson on

Diego Romeo 1 Dec 16, 2021
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 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
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
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
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

Jon C Cline 0 Sep 05, 2021
Book on Julia for Data Science

Book on Julia for Data Science

Julia Data Science 349 Dec 25, 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
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8.1k Dec 30, 2022
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
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
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
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
🍊 :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
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
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 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
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
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
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