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    "info": {
        "author": "Florian Wilhelm",
        "author_email": "florian.wilhelm@gmail.com",
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            "Operating System :: Microsoft :: Windows",
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            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3 :: Only",
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        "description": "[![Build Status](https://api.cirrus-ci.com/github/pyscaffold/pyscaffoldext-dsproject.svg?branch=master)](https://cirrus-ci.com/github/pyscaffold/pyscaffoldext-dsproject)\n[![ReadTheDocs](https://readthedocs.org/projects/pyscaffold/badge/?version=latest)](https://pyscaffold.org/projects/dsproject/en/latest)\n[![Coveralls](https://img.shields.io/coveralls/github/pyscaffold/pyscaffoldext-dsproject/master.svg)](https://coveralls.io/r/pyscaffold/pyscaffoldext-dsproject)\n[![PyPI-Server](https://img.shields.io/pypi/v/pyscaffoldext-dsproject.svg)](https://pypi.org/project/pyscaffoldext-dsproject)\n[![Conda-Forge](https://img.shields.io/conda/vn/conda-forge/pyscaffoldext-dsproject.svg)](https://anaconda.org/conda-forge/pyscaffoldext-dsproject)\n[![Downloads](https://pepy.tech/badge/pyscaffoldext-dsproject/month)](https://pepy.tech/project/pyscaffoldext-dsproject)\n\n# pyscaffoldext-dsproject\n\n[PyScaffold] extension tailored for *Data Science* projects. This extension is inspired by\n[cookiecutter-data-science] and enhanced in many ways. The main differences are that it\n1. advocates a proper Python package structure that can be shipped and distributed,\n2. uses a [conda] environment instead of something [virtualenv]-based and is thus more suitable\n   for data science projects,\n3. more default configurations for [Sphinx], [pytest], [pre-commit], etc. to foster\n   clean coding and best practices.\n\nAlso consider using [dvc] to version control and share your data within your team.\nRead [this blogpost] to learn how to work with JupyterLab notebooks efficiently by using a\ndata science project structure like this.\n\nThe final directory structure looks like:\n```\n\u251c\u2500\u2500 AUTHORS.md              <- List of developers and maintainers.\n\u251c\u2500\u2500 CHANGELOG.md            <- Changelog to keep track of new features and fixes.\n\u251c\u2500\u2500 CONTRIBUTING.md         <- Guidelines for contributing to this project.\n\u251c\u2500\u2500 Dockerfile              <- Build a docker container with `docker build .`.\n\u251c\u2500\u2500 LICENSE.txt             <- License as chosen on the command-line.\n\u251c\u2500\u2500 README.md               <- The top-level README for developers.\n\u251c\u2500\u2500 configs                 <- Directory for configurations of model & application.\n\u251c\u2500\u2500 data\n\u2502   \u251c\u2500\u2500 external            <- Data from third party sources.\n\u2502   \u251c\u2500\u2500 interim             <- Intermediate data that has been transformed.\n\u2502   \u251c\u2500\u2500 processed           <- The final, canonical data sets for modeling.\n\u2502   \u2514\u2500\u2500 raw                 <- The original, immutable data dump.\n\u251c\u2500\u2500 docs                    <- Directory for Sphinx documentation in rst or md.\n\u251c\u2500\u2500 environment.yml         <- The conda environment file for reproducibility.\n\u251c\u2500\u2500 models                  <- Trained and serialized models, model predictions,\n\u2502                              or model summaries.\n\u251c\u2500\u2500 notebooks               <- Jupyter notebooks. Naming convention is a number (for\n\u2502                              ordering), the creator's initials and a description,\n\u2502                              e.g. `1.0-fw-initial-data-exploration`.\n\u251c\u2500\u2500 pyproject.toml          <- Build configuration. Don't change! Use `pip install -e .`\n\u2502                              to install for development or to build `tox -e build`.\n\u251c\u2500\u2500 references              <- Data dictionaries, manuals, and all other materials.\n\u251c\u2500\u2500 reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.\n\u2502   \u2514\u2500\u2500 figures             <- Generated plots and figures for reports.\n\u251c\u2500\u2500 scripts                 <- Analysis and production scripts which import the\n\u2502                              actual PYTHON_PKG, e.g. train_model.\n\u251c\u2500\u2500 setup.cfg               <- Declarative configuration of your project.\n\u251c\u2500\u2500 setup.py                <- [DEPRECATED] Use `python setup.py develop` to install for\n\u2502                              development or `python setup.py bdist_wheel` to build.\n\u251c\u2500\u2500 src\n\u2502   \u2514\u2500\u2500 PYTHON_PKG          <- Actual Python package where the main functionality goes.\n\u251c\u2500\u2500 tests                   <- Unit tests which can be run with `pytest`.\n\u251c\u2500\u2500 .coveragerc             <- Configuration for coverage reports of unit tests.\n\u251c\u2500\u2500 .isort.cfg              <- Configuration for git hook that sorts imports.\n\u2514\u2500\u2500 .pre-commit-config.yaml <- Configuration of pre-commit git hooks.\n```\n\nSee a demonstration of the initial project structure under [dsproject-demo] and also check out\nthe documentation of [PyScaffold] for more information.\n\n\n## Usage\n\nJust install this package with `conda install -c conda-forge pyscaffoldext-dsproject`\nand note that `putup -h` shows a new option `--dsproject`.\nCreating a data science project is then as easy as:\n```bash\nputup --dsproject my_ds_project\n```\n\n\n<!-- pyscaffold-notes -->\n\n## Making Changes & Contributing\n\nThis project uses [pre-commit], please make sure to install it before making any\nchanges:\n\n```bash\nconda install pre-commit\ncd pyscaffoldext-dsproject\npre-commit install\n```\n\nIt is a good idea to update the hooks to the latest version:\n\n```bash\npre-commit autoupdate\n```\n\nPlease also check PyScaffold's [contribution guidelines].\n\n[PyScaffold]: https://pyscaffold.org/\n[cookiecutter-data-science]: https://github.com/drivendata/cookiecutter-data-science\n[Miniconda]: https://docs.conda.io/en/latest/miniconda.html\n[Jupyter]: https://jupyter.org/\n[dsproject-demo]: https://github.com/pyscaffold/dsproject-demo\n[Sphinx]: https://www.sphinx-doc.org/\n[pytest]: https://docs.pytest.org/\n[conda]: https://docs.conda.io/\n[Conda-Forge]: https://anaconda.org/conda-forge/pyscaffoldext-dsproject\n[virtualenv]: https://virtualenv.pypa.io/\n[pre-commit]: https://pre-commit.com/\n[dvc]: https://dvc.org/\n[this blogpost]: https://florianwilhelm.info/2018/11/working_efficiently_with_jupyter_lab/\n[pre-commit]: https://pre-commit.com/\n[contribution guidelines]: https://pyscaffold.org/en/latest/contributing.html\n\n\n",
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