TOPSIS implementation for multi-criteria decision making
Project description
📊 TOPSIS — A Python Package for Multi-Criteria Decision Making
This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm — a widely used multi-criteria decision analysis method.
📌 What is TOPSIS?
TOPSIS is an algorithm that helps in ranking and selecting from a set of alternatives based on multiple criteria.
It is based on the concept that the chosen alternative should have the shortest distance from the ideal solution and the farthest from the negative-ideal solution.
It is especially useful in areas like:
- Decision-making systems
- Recommendation engines
- Business and financial analysis
- Supply chain and logistics
🚀 Features
- Simple and clean API
- Accepts CSV or pandas DataFrame input
- Supports weighted and normalized decision matrices
- Command-line interface support
- Generates final rankings of alternatives
📦 Installation
Install the package directly from PyPI:
pip install topsis-nandini==0.1.11
🛠️ Usage
# 1. Command Line Interface
Once installed, you can use `topsis` as a CLI tool:
Topsis-nandini input.csv "0.3,0.5,0.2" "+,+,-" output.csv
This will:
Read data from input.csv
Apply weights 0.3,0.5,0.2
Apply impacts +,+,-
Write ranked output to output.csv
# 2. Use inside Python script
from topsis_package.topsis import topsis
topsis("data.csv", "0.4,0.3,0.3", "+,-,+", "result.csv")
📁 Project Structure
topsis_nandini/
│
├── topsis
│ ├── __init__.py
│ ├── __main__.py
│ └── topsis.py
├── README.md
├── LICENSE
├── setup.py
└── pyproject.toml
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
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_nandini-0.1.12.tar.gz.
File metadata
- Download URL: topsis_nandini-0.1.12.tar.gz
- Upload date:
- Size: 4.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d6e365f3189b1839714287dcfcb1f43ef08fd0ecf5d305a37ceba131caa9de1b
|
|
| MD5 |
4bac89fb04e8a3d64d48ef7290f1acaf
|
|
| BLAKE2b-256 |
17cb23f417824e1f7704cb7777f9be37ddafd127281534e6895f363a9f9c3edd
|
File details
Details for the file topsis_nandini-0.1.12-py3-none-any.whl.
File metadata
- Download URL: topsis_nandini-0.1.12-py3-none-any.whl
- Upload date:
- Size: 5.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3f5274a319dffedda79eacc1ff61417f238107b938e95e77b41c510b9f10ec69
|
|
| MD5 |
1bb5cb2c00cd36efa0e0d8c003befc8f
|
|
| BLAKE2b-256 |
013a20f6e5f2d1cf0285490821a82767f6e1aad305b7602f4d5bb8b9befc6836
|