semvec/pypi-packages huggingface dataset:
Topsis-Ananya-102303594 0.2
A Python package implementing the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method
Requires Python: >=3.8

# TOPSIS Command Line Tool

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method.

## Installation
```bash
pip install Topsis-Ananya-102303594

##Usage

topsis data.csv "1,1,1,1" "+,+,+,+" result.csv

##Input

CSV file with alternatives

Weights for criteria

Impacts (+ or -)

##Output

CSV file with TOPSIS score and rank

##Author
Ananya Singh


---

## 🔁 WHY VERSION CHANGE REQUIRED?

PyPI **does not allow overwriting** `0.1`  
So we bump version to `0.2`

---

## 🚀 REBUILD + REUPLOAD (COMMANDS)

Run **inside `assi-1/part-2`**

```bash
python -m build


---

## 🔁 WHY VERSION CHANGE REQUIRED?

PyPI **does not allow overwriting** `0.1`  
So we bump version to `0.2`

---

## 🚀 REBUILD + REUPLOAD (COMMANDS)

Run **inside `assi-1/part-2`**

```bash
python -m build
Topsis-Keshav-102303502 1.0.0
A Python package for TOPSIS implementation
Requires Python: >=3.6

# Topsis-Keshav-102303502

This is a Python package to implement the TOPSIS technique for Multi-Criteria Decision Making.

## Installation

```bash
pip install Topsis-Keshav-102303502
```

## 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.
Topsis-Charu-102303113 1.0.1
TOPSIS implementation as a Python package

# TOPSIS-Charu-102303113

A Python package to implement **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** for ranking alternatives based on multiple criteria.

TOPSIS is a Multi-Criteria Decision Making (MCDM) technique that ranks options by comparing their distance from the **ideal best** and **ideal worst** solutions.

---

## ✅ Features
- Accepts input data in CSV format
- Supports custom **weights** and **impacts**
- Generates output CSV with:
  - **Topsis Score**
  - **Rank** (Rank 1 = best)

---

## ✅ Installation
```bash
pip install Topsis-Charu-102303113
topsis-rajgupta-102303324 1.0.3
TOPSIS implementation using Python
Requires Python: >=3.7

# TOPSIS Implementation in Python

**Course:** UCS654 â€“ Predictive Analytics using Statistics  
**Assignment:** Assignment-1 (TOPSIS)  
**Author:** Raj Gupta  
**Roll Number:** 102303324  

---

##  About the Project

This project provides a Python implementation of the  
**TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method.

TOPSIS is a multi-criteria decision-making (MCDM) technique used to rank alternatives based on their distance from the ideal best and ideal worst solutions.

---


##  Installation (User Manual)

This package requires **Python 3.7 or higher**.

### Dependencies
- pandas  
- numpy  

Package listed on PyPI:- https://pypi.org/project/topsis-rajgupta-102303324/

Install the package using pip:

```bash
pip install topsis-rajgupta-102303324
```
---

## Usage

Run the following command in the Command Prompt / Terminal:

```bash
topsis <inputFile> <weights> <impacts> <outputFile>
```
 Example

 ```bash 
 topsis sample.csv "1,1,1,1" "+,+,-,+" result.csv

```
Topsis-Karanveer-102303670 0.0.9
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.
aicuflow 0.2.0
use aicuflow in your python apps
Requires Python: >=3.9

# aicuflow-python

use [aicuflow](https://aicuflow.com/?ref=aicuflow-python) in your python apps 🎉

## quickstart

```bash
pip install aicuflow
```

[check the documentation](https://aicuflow.com/docs/library/python)

## contributing

yeees you can contribute to this library!

make sure to follow the soon existing community guidelines!

### version history

using [semver](https://semver.org/lang/de/).

```txt
0.2.0 - convenience simplified syntax & dict-like objects instead of json
0.1.1 - sensor data streaming was added to the client
0.1.0 - basic version containing core file & flow featureset
0.0.1 - initial publishing
```

### contribs quickstart

```bash
# local synced dev install
git clone https://github.com/AICU-HEALTH/aicuflow-python
cd aicuflow
pip install -e .
```

### contrib guide

1. clone (see quickstart)
2. branch (yourname/your-feature-or-fix-name)
3. code (use python-black formatter pls)
4. pull request
5. review (max 3 cycles, usually within 48h)
6. be merged, such cool 🎉

## license: pretty chill

the owners have not decided on a license but shall do so soon

you are permitted to use this however, guaranteedly in any of your tests, educational and commercial apps, billed only for actual aicuflow-core useage.

anything of aicuflow-core is proprietary

if in doubt just call us!