pyscaffoldext-dsproject-vscode 0.1.2

PyScaffold extension for Data Science projects with VS Code container integration

    # pyscaffoldext-dsproject-vscode

    This extension is forked from the awesome [PyScaffold DSProject](https://github.com/pyscaffold/pyscaffoldext-dsproject) extension.

    This version of the extension extends the functionality by adding a scaffold component for using the VS Code container development integration.

    To benefit from this setup, you must use VS Code with Python and Remote - Containers extensions installed. Docker should also be installed on the host machine.

    ## Usage

    Just install this package with `pip install pyscaffoldext-dsproject-vscode`
    and note that `putup -h` shows a new option `--dsproject-vscode`.
    Creating a data science project with VS Code container development integration is then as easy as:

    ```
    putup --dsproject-vscode my_ds_project
    ```

    ----------------------------

    # pyscaffoldext-dsproject

    [PyScaffold] extension tailored for *Data Science* projects. This extension is inspired by
    [cookiecutter-data-science] and enhanced in many ways. The main differences are that it
    1. advocates a proper Python package structure that can be shipped and distributed,
    2. uses a [conda] environment instead of something [virtualenv]-based and is thus more suitable
       for data science projects,
    3. more default configurations for [Sphinx], [py.test], [pre-commit], etc. to foster
       clean coding and best practices.

    Also consider using [dvc] to version control and share your data within your team.
    Read [this blogpost] to learn how to work with JupyterLab notebooks efficiently by using a
    data science project structure like this.

    The final directory structure looks like:
    ```
    ├── AUTHORS.rst             <- List of developers and maintainers.
    ├── CHANGELOG.rst           <- Changelog to keep track of new features and fixes.
    ├── LICENSE.txt             <- License as chosen on the command-line.
    ├── README.md               <- The top-level README for developers.
    ├── configs                 <- Directory for configurations of model & application.
    ├── 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                    <- Directory for Sphinx documentation in rst or md.
    ├── environment.yaml        <- The conda environment file for reproducibility.
    ├── 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 description,
    │                              e.g. `1.0-fw-initial-data-exploration`.
    ├── references              <- Data dictionaries, manuals, and all other materials.
    ├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures             <- Generated plots and figures for reports.
    ├── scripts                 <- Analysis and production scripts which import the
    │                              actual PYTHON_PKG, e.g. train_model.
    ├── setup.cfg               <- Declarative configuration of your project.
    ├── setup.py                <- Use `python setup.py develop` to install for development or
    |                              or create a distribution with `python setup.py bdist_wheel`.
    ├── src
    │   └── PYTHON_PKG          <- Actual Python package where the main functionality goes.
    ├── tests                   <- Unit tests which can be run with `py.test`.
    ├── .coveragerc             <- Configuration for coverage reports of unit tests.
    ├── .isort.cfg              <- Configuration for git hook that sorts imports.
    └── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.
    ```

    See a demonstration of the initial project structure under [dsproject-demo] and also check out
    the the documentation of [PyScaffold] for more information.


    ## Usage

    Just install this package with `pip install pyscaffoldext-dsproject`
    and note that `putup -h` shows a new option `--dsproject`.
    Creating a data science project is then as easy as:
    ```
    putup --dsproject my_ds_project
    ```

    ## Note

    This project has been set up using PyScaffold 3.2. For details and usage
    information on PyScaffold see https://pyscaffold.org/.

    [PyScaffold]: https://pyscaffold.org/
    [cookiecutter-data-science]: https://github.com/drivendata/cookiecutter-data-science
    [Miniconda]: https://docs.conda.io/en/latest/miniconda.html
    [Jupyter]: https://jupyter.org/
    [dsproject-demo]: https://github.com/pyscaffold/dsproject-demo
    [Sphinx]: http://www.sphinx-doc.org/
    [py.test]: https://docs.pytest.org/
    [conda]: https://docs.conda.io/
    [virtualenv]: https://virtualenv.pypa.io/
    [pre-commit]: https://pre-commit.com/
    [dvc]: https://dvc.org/
    [this blogpost]: https://florianwilhelm.info/2018/11/working_efficiently_with_jupyter_lab/

Platform: any Classifier: Development Status :: 4 - Beta Classifier: Programming Language :: Python Description-Content-Type: text/markdown Provides-Extra: testing