gdmo 0.0.19

GDMO native classes for standardized interaction with data objects within Azure Databricks. Contains TimeSeriesForecasting, APIRequest, Landing, and Delta functions.

gdmo

GDMO native classes for standardized interaction with data objects within Azure Databricks

This custom library allows our engineering team to use standardized packages that strip away a load of administrative and repetitive tasks from their daily object interactions. The current classes supported (V0.1.0) are:

Installation

Install this library using pip:

pip install gdmo

Usage

Forecast - Forecast

Standardized way of forecasting a dataset. Input a dataframe with a Series, a Time, and a Value column, and see the function automatically select the right forecasting model and generate an output.

Example usage:

from gdmo import TimeSeriesForecast
forecaster = TimeSeriesForecast(spark, 'Invoiced Revenue')\
                    .set_columns('InvoiceDate', 'ProductCategory', 'RevenueUSD')\
                    .set_forecast_length(forecast_length)\
                    .set_last_data_point(lastdatamonth)\
                    .set_input(df)\
                    .set_growth_cap(0.02)\
                    .set_use_cap_growth(True)\
                    .set_modelselection_breakpoints(12, 24)\
                    .set_track_outcome(False)\
                    .build_forecast()

forecaster.inspect_forecast()

API - APIRequest

Class to perform a standard API Request using the request library, which allows a user to just add their endpoint / authentication / method data, and get the data returned without the need of writing error handling or need to understand how to properly build a request.

Example usage:

request = APIRequest(uri)\
            .set_content_type('application/json') \
            .set_header('bearer xxxxx') \
            .set_method('GET') \
            .set_parameters({"Month": "2024-01-01"})\
            .make_request()

response = request.get_json_response()
display(response)