From da428619e1779230275a5f64e4c70143e37a1cb2 Mon Sep 17 00:00:00 2001 From: Karl Lorey Date: Wed, 14 Jun 2017 10:27:36 +0200 Subject: [PATCH] Add directory structure to README, improves #10 The file structure is an essential part of the provided benefit and should be in the readme file. --- README.md | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/README.md b/README.md index 925456f..dff8cf3 100644 --- a/README.md +++ b/README.md @@ -32,6 +32,57 @@ $ conda install cookiecutter [![asciicast](https://asciinema.org/a/9bgl5qh17wlop4xyxu9n9wr02.png)](https://asciinema.org/a/9bgl5qh17wlop4xyxu9n9wr02) +### 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` +│ +├── 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.testrun.org +``` + ## Contributing We welcome contributions! [See the docs for guidelines](https://drivendata.github.io/cookiecutter-data-science/#contributing).