topsis-teena-102217049 1.0.2

A Python package to calculate TOPSIS rankings

Topsis-Package

Topsis-Package is a Python library for implementing the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for multi-criteria decision analysis. It simplifies ranking and decision-making based on criteria weights and performance data.

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Installation

Install the package directly from PyPI:

pip install topsis-teena-102217049

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Usage

Input

Prepare a CSV file with the following structure:

• The first row contains column headers (criteria names).
• The first column contains the names of alternatives (e.g., products or options).
• The remaining columns are numeric values representing the performance scores for each criterion.

Example Input CSV (data.csv):

Alternative, Criterion 1, Criterion 2, Criterion 3, Criterion 4
Option A, 250, 16, 12, 5
Option B, 200, 22, 8, 6
Option C, 300, 18, 15, 4
Option D, 275, 20, 14, 7

Running the Package

To execute, you will need:

• Path to the input CSV file.
• A comma-separated string of weights (e.g., "0.4,0.3,0.2,0.1").
• A comma-separated string of impacts (e.g., "+,+,-,-").

Example Code:

topsis input.csv "0.4,0.3,0.2,0.1" "+,+,-,-" result.csv

Input parameters

• input_file = "data.csv"
• weights = "0.4,0.3,0.2,0.1"
• impacts = "+,+,-,-"

Output

The package will create a new CSV file with an additional column, "Topsis Score", and the final "Rank" for each alternative.

Example Output:

Alternative, Criterion 1, Criterion 2, Criterion 3, Criterion 4, Topsis Score, Rank
Option A, 250, 16, 12, 5, 0.78, 2
Option B, 200, 22, 8, 6, 0.56, 4
Option C, 300, 18, 15, 4, 0.84, 1
Option D, 275, 20, 14, 7, 0.64, 3

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Features

• Simple Input Format: Provide your data in CSV format.
• Customizable Weights and Impacts: Define criteria importance and type (beneficial or non-beneficial).
• Automated Output: Generates scores and ranks for all alternatives.

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Requirements

This package requires Python 3.7 or higher. Install any missing dependencies using pip.

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Author

Developed by Teena Sapra.

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