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102203236-Topsis

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A Python package for TOPSIS multi-criteria decision making

Versions collected1
Direct dependencies2From release 0.0.1
Last observed20 Oct, 15:08 UTC

README

Version 0.0.1 documentation

102203236_Topsis

Overview

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making (MCDM) method used for ranking and selecting alternatives based on multiple criteria. This Python package allows users to easily apply the TOPSIS method to their datasets.

Features

  • Reads data from an Excel file.
  • Automatically normalizes the data.
  • Applies user-defined weights and impacts to criteria.
  • Computes the TOPSIS score and ranks alternatives.
  • Outputs the final decision-making table with rankings.

Installation

Install the package using pip:

pip install 102203236_Topsis

Usage

Import the package

from topsis import Topsis

Example

# Importing the package
from topsis import Topsis

# Define the input parameters
filename = "data.xlsx"
impacts = ['+','+','+','-','+']
weights = [0.25, 0.25, 0.25, 0.25, 0.25]

# Run TOPSIS
result = Topsis(filename, impacts, weights).get_result()

# Print the ranked result
print(result)

Expected Input Format

The input Excel file should contain numeric data for decision-making criteria. An example format:

Fund NameCriterion 1Criterion 2Criterion 3Criterion 4Criterion 5
Fund A2503.51200885
Fund B2754.01150790
Fund C2603.81250688

Output Format

The output DataFrame includes the original data with additional columns:

  • Score: Computed TOPSIS score.
  • Rank: Final ranking based on the score.

Dependencies

This package requires:

  • pandas
  • numpy

These dependencies will be automatically installed when using pip install 102203236_Topsis.

License

This project is licensed under the MIT License.

Contributing

Pull requests are welcome! If you have suggestions or improvements, feel free to submit an issue or PR.

Author


Enjoy using the TOPSIS package! 🚀

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Versions 1

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VersionPublishedDirect dependenciesOpen version
0.0.119 Jan 20252

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