Python Dev Environment for the CMC Dashboard

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Project created to parse TN Covid Data so that I can create a dashboard.

A unified python development environment I can use on both Mac and Windows

Features:

• zsh shell (with oh my zsh)
• python3
• Jupyter and JupyterLab

Based from How to Setup Your JupyterLab Project Environment

To Run Environment:

On Mac:

sh run.sh

On Windows:

run.bat

The image will launch the zsh shell prompt.

To run JupyterLab:

sh config/run_jupyter.sh

Python Packages

To install various python packages on image load, edit:

RUN  pip3 install --upgrade pip \
    requests \
    jupyter \
    jupyterlab \
    pycrypto \
    bash_kernel \
    && python3 -m bash_kernel.install

Add a new line after "bash_kernel " and add the python package followed by " ".

Folder layout is based on: cookiecutter data science project template.

Project Organization

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├── 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
│
├── 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

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