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
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
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
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
Discontinuous Galerkin finite element method (DGFEM) for Maxwell Equations

DGFEM Maxwell Equations Discontinuous Galerkin finite element method (DGFEM) for Maxwell Equations. Work in progress. Currently, the 1D Maxwell equati

Rafael de la Fuente 9 Aug 16, 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
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
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
collection of interesting Computer Science resources

collection of interesting Computer Science resources

Kirill Bobyrev 137 Dec 22, 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
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
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
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
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
Datamol is a python library to work with molecules

Datamol is a python library to work with molecules. It's a layer built on top of RDKit and aims to be as light as possible.

datamol 276 Dec 19, 2022
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
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
CONCEPT (COsmological N-body CodE in PyThon) is a free and open-source simulation code for cosmological structure formation

CONCEPT (COsmological N-body CodE in PyThon) is a free and open-source simulation code for cosmological structure formation. The code should run on any Linux system, from massively parallel computer

Jeppe Dakin 62 Dec 08, 2022