TOPSIS implementation using Python
Project description
TOPSIS-Yash-102303701
TOPSIS-Yash-102303701 is a Python package that implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision-making (MCDM).
TOPSIS is widely used in decision analysis to rank alternatives based on their distance from an ideal best solution and an ideal worst solution. This package allows users to apply TOPSIS easily on tabular data stored in CSV files through a simple command-line interface.
The package validates inputs, handles common user errors, and generates a ranked output with TOPSIS scores, making it suitable for academic assignments as well as real-world decision analysis problems.
Features
- Command-line implementation of TOPSIS
- Supports multiple decision criteria
- Handles both beneficial (+) and non-beneficial (-) attributes
- Input validation for files, weights, and impacts
- Outputs TOPSIS score and rank for each alternative
Installation
Install the package directly from PyPI:
pip install TOPSIS-Yash-102303701
Usage
Run the TOPSIS analysis from the command line:
topsis input.csv "1,1,1,2,1" "+,+,-,+,+" output.csv
Input File Format
- The input file must be a CSV file
- The first column should contain the names of alternatives
- All remaining columns must contain numeric values only
- The number of weights and impacts must match the number of criteria columns
Output File
The output CSV file contains:
- All original input columns
- A new column named Topsis Score
- A new column named Rank, where rank 1 indicates the best alternative
Author
Yash Sharma
Roll No: 102303701
License
This project is developed for academic and educational purposes.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file topsis_yash_102303701-0.0.3.tar.gz.
File metadata
- Download URL: topsis_yash_102303701-0.0.3.tar.gz
- Upload date:
- Size: 3.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
b9fb47af99111d7dc56ab825b997e2e14eb4f9bb86d61b66422d00b7b1deb9fc
|
|
| MD5 |
29136f9e0f0ca27ed740ed5f12b5dff9
|
|
| BLAKE2b-256 |
cd055dc9a3233fb942ac70ea34341ca2c21ef28c0a156cd37cf5628b5960be2d
|
File details
Details for the file topsis_yash_102303701-0.0.3-py3-none-any.whl.
File metadata
- Download URL: topsis_yash_102303701-0.0.3-py3-none-any.whl
- Upload date:
- Size: 3.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
92ffd7871cdbdc6182c18604eddd39a10cdb03ed2d2d2eeb425484c49859b31d
|
|
| MD5 |
934710ba0a88d157aa30d7315ce0dc44
|
|
| BLAKE2b-256 |
d63ba2f885e326640867d89af3dfcebb544752077784f06923ae59d2cc7058fe
|