monkstools 0.4

Tools for the Monks advertising platform

monkstools

monkstools for team

begin to work. by Xiaowen kang. 2023.8.24. check . well done. by xiaowen kang. 2023.8.24 prepare for pypi package. by xiaowen kang. 2023.8.24.

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1. Use Case

Analyze and calculate ROI (Return On Investment) based on given datasets: one reflecting group demographics and another indicating secondary preferences.

2. Sample Code

from monkstools.top_module import TopModule

def main():
    # Sample user data
    data = {
        "person_group": "TensorData Representation",  # Replace with actual data
        "secondary_preference": "Preferences Dataset"  # Replace with actual data
    }

    # Utilizing monkstools for ROI computation
    instance = TopModule(data)
    instance.calculate_roi()
    instance.display_results()

if __name__ == "__main__":
    main()

3. Documentation

monkstools Library Guide

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Class: TopModule

• Description: Central module for ROI calculations integrating PersonGroup and SecondaryPreference sub-modules.
• Methods:
  • __init__(self, data: dict): Constructor expecting a dictionary containing data for person_group and secondary_preference.
  • calculate_roi(): Executes ROI calculation, invoking the analyze methods of sub-modules.
  • display_results(): Outputs the computed ROI results.

Class: PersonGroup

• Description: Analyzes specific group data.
• Methods:
  • __init__(self, tensor_data: str): Constructor expecting a string representation of the group data.
  • analyze(): Analyzes the group data.

Class: SecondaryPreference

• Description: Focuses on secondary preference analysis.
• Methods:
  • __init__(self, preferences_data: str): Constructor expecting a string representation of preference data.
  • analyze(): Analyzes the preference data.

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To leverage this library, ensure monkstools is installed and data provided matches expected formats.

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xiaowen kang. 2023.8.23