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Retail Data Science Tools

Project description

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⚡ Democratizing retail data analytics for all retailers ⚡

🤔 What is PyRetailScience?

pyretailscience is a Python package designed for performing analytics on retail data. Additionally, the package includes functionality for generating test data to facilitate testing and development.


To install pyretailscience, use the following pip command:

pip install pyretailscience

Quick Start

Generating Simulated Data

The pyretailscience package provides a command-line interface for generating simulated transaction data.


pyretailscience --config_file=<config_file_path> [--verbose=<True|False>] [--seed=<seed_number>] [output]

Options and Arguments

  • --config_file=<config_file_path>: The path to the configuration file for the simulation. This is a required argument.
  • --verbose=<True|False>: Optional. Set to True to see debug messages. Default is False.
  • --seed=<seed_number>: Optional. Seed for the random number generator used in the simulation. If not provided, a random seed will be used.
  • [output]: Optional. The path where the generated transactions will be saved in parquet format. If not provided, the transactions will be saved in the current directory.


# Get the default transaction config file
# Generate the data file
pyretailscience --config_file=default_data_config.yaml --seed=123 transactions.parquet

This command will generate a file named transactions.parquet with the simulated transaction data, using the configuration file at default data configuration file, and a seed of 123 for the random number generator.


We welcome contributions from the community to enhance and improve pyretailscience. To contribute, please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Make your changes and commit them with clear messages.
  4. Push your changes to your fork.
  5. Open a pull request to the main repository's main branch.

Please make sure to follow the existing coding style and provide unit tests for new features.


Made with


This project is licensed under the Elastic License 2.0 - see the LICENSE file for details.

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