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cookiecutter-python-analysis

Cookiecutter inspired template for CorrelAid Python analysis projects.

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

What is this project about?

summarize in three sentences what this project is about and what central features it has.

Setup

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data                <- see README in data folder
│   ├── processed_gdpr        
│   ├── processed     
│   └── raw        
│
├── 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),
│                         and a short `-` delimited description, e.g.
│                         `01-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

Installing Packages

How can a environment for your project be created/updated?

Please make sure that the setup steps are:

  • platform-independent (e.g. be aware of issues like this), at least MacOS and Windows (this is important in case CorrelAid employees have to provide support after the project has ended.
  • computer-independent: must work for all team members!

Data

You need the following data files in order to run this project:

include output from tree command (or similar on windows)

Developer information

[the following can also be moved to the wiki if you decide to have one]

Definition of Done

Default Definition of Done can be found here. Adapt if needed.

Code styling

How to operate this project?

[the following can also be moved to the wiki if you decide to have one]

explain how the output(s) of this project can be handled/operated, for example:

  • how to create reports
  • where to create/find the data visualizations
  • how to update data
  • what would need to be updated if someone wanted to re-run your analysis with different data

Limitations

be honest about the limitations of your project, e.g.:

  • methodological: maybe another model would be more suitable?
  • reproducibility: what are limits of reproducibility? is there something hard-coded/specific to the data that you used?
  • best practices: maybe some code is particularly messy and people working on it in the future should know about it in advance?