lor-gdp-tools 0.1.9

This is a utility package designed to enable data scientitists and analysts to easily access GDP data within a python environment

Gdp Tools Package

This is a utility package designed to enable data scientists and analysts to easily access GDP data within a python environment

Requirements:

• The data professional should be able to clone the package at the start of new project and when in production
• The package will contain a number of support functions serving the following objectives: -- Accessing data on GDP Base/CIM/Warehouse -- Accessing data in temp storage (LAB) -- Querying that data in SQL -- utlising that data as a Python/PySpark DataFrame -- Storing processed data to the temp storage space (LAB)
• any data access configs should available to be used

Project Installation

This project uses Conda to manage dependencies. For the fastest way of obtaining conda, install Miniconda. After installation, to install the dependencies, run this command in the root directory of the project:

conda env update environment.yml --prune

Project Organisation

├── README.md          <- The top-level README for developers using this project.
|
├── .github            <- Github related files.
│   └── workflows      <- Github workflows for Github Actions.
|
├── config             <- Config files for the project. By default this will include the AML
|                         staging config.
├── data
│   ├── external       <- Data from third party sources.
│   ├── processed      <- The final, canonical data sets for modelling and analysis.
│   ├── raw            <- The original, immutable data dump.
|   └── sql            <- SQL used to query data.
│
├── models_store       <- 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.
│                         `1.0-jqp-initial-data-exploration`.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── gdp_tools   <- Source code for use in this project.
│   ├── __init__.py    <- Makes gdp_tools a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modelling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
|   |
|   ├── tests          <- Scripts used to perform unit tests.
|   |                     Check tests > data > test_make_dataset.py for an example.
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
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├── environment.yml    <- Conda environment file.
├── setup.py           <- Makes project pip installable (pip install -e .) so src can be imported.