semvec/pypi-packages huggingface dataset:
Topsis-Dishavpreet-102353006 0.0.2
Implementation of TOPSIS method

# Topsis-Dishavpreet-102353006

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method in Python.

This project was developed as part of an academic assignment.

---

## Installation

Install the package using pip:

pip install Topsis-Dishavpreet-102353006

---

## Usage

After installation, run the following command from the command line:

topsis input.csv "1,1,1,1,1" "+,+,+,+,+" output.csv

---

## Parameters

input.csv  
Input CSV file containing alternatives and criteria.  
The first column should be non-numeric.

weights  
Comma-separated numerical weights.  
Example:  
"1,1,1,1,1"

impacts  
Comma-separated impacts.  
Use + for benefit and - for cost criteria.  
Example:  
"+,+,-,+,+"

output.csv  
Output CSV file containing TOPSIS score and rank.

---

## Example

topsis data.csv "1,1,1" "+,+,+" result.csv

---

## Requirements

Python 3.x  
numpy  
pandas
Topsis-Antigravity-101010 1.0.0
A Python package for TOPSIS implementation
Requires Python: >=3.6

# TOPSIS-Antigravity-101010

This is a Python package to implement the TOPSIS technique for Multi-Criteria Decision Making.

## Installation

```bash
pip install Topsis-Antigravity-101010
```

## Usage

Command Line:
```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

Example:
```bash
topsis data.csv "1,1,1,1" "+,-,+,+" result.csv
```

## Input File Format
CSV file with 3 or more columns.
First column is the object/fund name.
Rest columns are numeric parameters.
systembolaget-api 0.0.3
A python API wrapper for systembolaget.se
Requires Python: >=3.10

<p align="center">
  <img src="https://i.imgur.com/WiaFd47.png" />
</p>

# SystembolagetAPI


[![PyPI version](https://img.shields.io/pypi/v/systembolaget-api?style=for-the-badge)](https://pypi.org/project/systembolaget-api/) [![License](https://img.shields.io/badge/license-WTFPL-green?style=for-the-badge)](https://github.com/dunderrrrrr/systembolaget-api/blob/main/LICENSE) ![Python](https://img.shields.io/badge/Python-3.10-blue?style=for-the-badge)


SystembolagetAPI lets you search and filter for products on [Systembolaget](https://www.systembolaget.se/) using their unofficial client api.

## Install

SystembolagetAPI is available on [PyPI](https://pypi.org/project/systembolaget-api/).

```bash
uv add systembolaget-api
pip install systembolaget-api
```

## Usage

```python
from systembolaget_api import (
    FilterOptions,
    SystembolagetAPI,
    SystembolagetClient,
)

client = SystembolagetClient()
api = SystembolagetAPI(client)

# Simple query only
api.search("Nils Oscar")

# Query with filters
api.search("wine", FilterOptions(price_max=200, country=["France"]))

# Everything
api.search(
    query="red wine",
    options=FilterOptions(
        price_min=100,
        price_max=500,
        country=["France", "Italy"],
        vintage=[2018, 2019],
    ),
)

# Simple search with sorting
api.search("Nils Oscar", sort_by="ProductLaunchDate", sort_direction="Ascending")

# With filters too
api.search(
    query="wine",
    sort_by="Price",
    sort_direction="Descending",
    options=FilterOptions(price_max=200, country=["France"]),
)
```
topsispackage1.1.1 0.0.7
topsis
Keywords: python, topsis, ranking, decision making, mcdm

# TOPSIS Ranking Tool  
### By Karanveer Singh (Roll No: 102303670)

This command-line tool applies the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to rank alternatives based on multiple quantitative criteria.

---

## Installation

pip install Topsis-Karanveer-102303670

---

## Input Data Format

The input must be a CSV file with the following structure:

Column 1: Alternatives  
Column 2 onwards: Numerical criteria values

Example:

| City | AirQuality | Cost | Transport | Safety |
|------|------------|------|-----------|--------|
| C1 | 72 | 32000 | 7 | 8 |
| C2 | 88 | 47000 | 9 | 6 |
| C3 | 65 | 28000 | 6 | 9 |
| C4 | 80 | 36000 | 8 | 8 |

---

## Weights and Impacts

Weights (comma separated):

0.30,0.20,0.25,0.25

Impacts (comma separated):

+,-,+,+

---

## Usage

topsis <input_file.csv> <weights> <impacts> <output_file.csv>

Example:

topsis city.csv "0.30,0.20,0.25,0.25" "+,-,+,+" ranks.csv

---

## Output Format

The output CSV will contain:

City, Score, Rank

Example:

C1, 0.51, 3  
C2, 0.56, 2  
C3, 0.44, 4  
C4, 0.67, 1

Higher score means better rank.

---

## Error Handling

The program checks for:

Incorrect number of arguments  
Non-numeric values in criteria columns  
Invalid impact symbols (+ or - only)  
Incorrect CSV format

Appropriate error messages are displayed for invalid input.

---

## Conclusion

This tool helps in decision making by converting multiple evaluation criteria into a single composite score using the TOPSIS method.
Topsis-Anoushka-102303312 1.0.1
TOPSIS Implementation in Python

# TOPSIS

**Author:** Anoushka Singh
**Roll Number:** 102303312


---

## Overview

**TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** is a
multi-criteria decision-making (MCDM) method developed in the 1980s.  
It ranks alternatives based on their relative closeness to the ideal best and
ideal worst solutions.

This package provides a command-line implementation of the TOPSIS algorithm and
supports CSV input files.

---

## Installation

Install the package using pip:

pip install Topsis-Anoushka-102303312 

---

## Command Line Usage

After installation, run the following command in the current working directory:

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

### Example

topsis data.csv "1,2,1,1" "+,-,-,+" result.csv

---

## Input Requirements

- Input file must contain **three or more columns**
- **First column** must contain alternative names (non-numeric)
- **Second to last columns** must contain **numeric values only**
- Number of **weights**, **impacts**, and **criteria columns** must be equal
- Impacts must be:
  - `+` for benefit criteria
  - `-` for cost criteria
- Weights and impacts must be **comma-separated**
- Weights need not be normalized (handled internally)

---

## Input File Format (data.csv)

Each row represents an alternative, and each column (except the first) represents
a decision criterion such as Correlation, R², RMSE, Accuracy, etc.

Example:

| Model | Corr | Rseq | RMSE | Accuracy |
|------:|-----:|-----:|-----:|---------:|
| M1 | 0.79 | 0.62 | 1.25 | 60.89 |
| M2 | 0.66 | 0.44 | 2.89 | 63.07 |
| M3 | 0.56 | 0.31 | 1.57 | 62.87 |
| M4 | 0.82 | 0.67 | 2.68 | 70.19 |
| M5 | 0.75 | 0.56 | 1.30 | 80.39 |


---

## Output File Format (result.csv)

For weights "1,2,1,1" and impacts "+,-,-,+", the output file will contain two
additional columns: **Topsis Score** and **Rank**.

## Example Output

| Model | Corr | Rseq | RMSE | Accuracy | Topsis Score | Rank |
|------:|-----:|-----:|-----:|---------:|-------------:|-----:|
| M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.423744391359611 | 4 |
| M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.467426368298297 | 3 |
| M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.760230957034903 | 1 |
| M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.207772533881566 | 5 |
| M5 | 0.75 | 0.56 | 1.30 | 80.39 | 0.504864457803718 | 2 |


---

## Description of Output

- **Topsis Score**: Relative closeness of the alternative to the ideal solution  
- **Rank**: Rank based on TOPSIS score (1 = best alternative)

---

## License

This project is developed for academic purposes.
topsis-abhayjeet 1.1.2
Implementation of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Requires Python: >=3.7

# TOPSIS Python Package

This package provides an implementation of the TOPSIS
(Technique for Order Preference by Similarity to Ideal Solution)
method for multi-criteria decision making.

## Installation
```bash
pip install topsis-abhayjeet

Usage

topsis input.csv "1,1,1,2" "+,+,-,+" output.csv
Input Format
First column: Object/Alternative names

Remaining columns: Numeric criteria values

Output
TOPSIS Score
Rank

Author
Abhayjeet


---
`LICENSE`

```text
MIT License

Copyright (c) 2026 Abhayjeet

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, subject to the following conditions:

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND.