Git Product home page Git Product logo

python-project-template's Introduction

Azavea Data Analytics team python project template

A file structure template, development environment and rule set for python data analytics projects on the data analytics team

Getting Started

Change the name of folder that contains this whole repo: python-project-template -> {your project name}

From within the repo directory, first remove git tracking from the project

rm -rf .git

The project template uses a placeholder name of 'da-project'. Change that name in the following files/directories (relative to the repo root):

  • da-project/ (change the name of the folder)
  • ./docker/run/
  • ./docker/build/

If you have not already done so, build the Docker image (you will only need to do this once)

docker/build

Run a Docker container:

docker/run

This will open a bash shell within the Docker container. Within the container the 'project' directory on the host machine (as specified as a parameter of run above) will map to /opt/src/ within the container. You can now access the full file structure of this template from within the container.

Run a Jupyter Notebook within Docker container:

docker/jupyter

You will need to open the link that is displayed in your terminal.

To exit:

exit

Initialize a new git repository:

git init

Project Organization

├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── interm         <- Intermediate data that has been transformed
│   ├── processed      <- The final, canonical data sets for modeling
│   └── raw            <- The original, immutable data dump
│
├── guide              <- A set of markdown files with documented best practices, guidelines and rools for collaborative projects
│
├── 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
│
└── da-project         <- Source code for use in this project.
    │
    ├── 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

Project based on the cookiecutter data science project template. #cookiecutterdatascience

python-project-template's People

Contributors

simonkassel avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

python-project-template's Issues

Jupyter

Make it easy to run jupyter notebooks in docker container

jupyterlab password

enable user to sign into jupyterlab server with a password rathet than having to copy and paste a token from console

Data documentation

Add detailed markdown descriptions of four different data sub-directories

add manual

large markdown document with standards and practices for DA data projects

add tests

add tests that we can run to determine whether python packages are installed and working correctly

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.