semvec/pypi-packages huggingface dataset:
topsis-sameer-102316089 1.0.3
A Python package implementing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for multi-criteria decision making.
License: MIT
Requires Python: >=3.6

# Topsis-Sameer-102316089

A Python package that implements **TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) — a multi-criteria decision-making method.

## Installation

```bash
pip install Topsis-Sameer-102316089
```

## Usage

Run from the command line:

```bash
topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>
```

### Example

```bash
topsis data.csv "1,1,1,2" "+,+,-,+" output.csv
```

### Parameters

| Parameter | Description |
|-----------|-------------|
| `InputDataFile` | Path to input CSV/Excel file |
| `Weights` | Comma-separated weights (e.g., `"1,1,1,2"`) |
| `Impacts` | Comma-separated impacts, `+` or `-` (e.g., `"+,+,-,+"`) |
| `OutputResultFileName` | Path for the output CSV file |

## Input File Format

- The input file must contain **three or more columns**.
- The **first column** is the name/label of each alternative (e.g., M1, M2, …).
- **Column 2 onwards** must contain **numeric values only**.

### Sample Input (`data.csv`)

| Fund Name | P1   | P2   | P3  | P4   | P5    |
|-----------|------|------|-----|------|-------|
| M1        | 0.67 | 0.45 | 6.5 | 42.6 | 12.56 |
| M2        | 0.60 | 0.36 | 3.6 | 53.3 | 14.47 |
| M3        | 0.82 | 0.67 | 3.8 | 63.1 | 17.10 |

## Output File Format

The output file contains all original columns plus two new columns:

| Column | Description |
|--------|-------------|
| `Topsis Score` | The computed TOPSIS score (0 to 1) |
| `Rank` | Rank based on score (1 = best) |

### Sample Output (`output.csv`)

| Fund Name | P1   | P2   | P3  | P4   | P5    | Topsis Score | Rank |
|-----------|------|------|-----|------|-------|-------------|------|
| M1        | 0.67 | 0.45 | 6.5 | 42.6 | 12.56 | 20.58       | 2    |
| M2        | 0.60 | 0.36 | 3.6 | 53.3 | 14.47 | 40.83       | 4    |
| M3        | 0.82 | 0.67 | 3.8 | 63.1 | 17.10 | 30.07       | 3    |

## Input Validations

The program checks for the following:

1. **Correct number of parameters** — exactly 4 arguments required
2. **File not found** — shows an appropriate error message
3. **Minimum columns** — input file must have ≥ 3 columns
4. **Numeric values** — columns 2 onwards must be numeric
5. **Count match** — number of weights, impacts, and criteria columns must be equal
6. **Valid impacts** — impacts must be `+` or `-` only
7. **Comma-separated** — weights and impacts must be separated by commas

## License

MIT © Sameer
Topsis-Vishard-102317240 1.0.1
A command-line Python package to implement the TOPSIS method.
License: MIT
Requires Python: >=3.6

# TOPSIS-Vishard-102317240

## 📝 Overview

**Topsis-Vishard-102317240** is a Python library for performing **Multi-Criteria Decision Making (MCDM)** using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) algorithm. It allows you to rank alternatives based on multiple conflicting criteria, providing a robust mathematical approach to decision-making.

This package is designed to be simple, efficient, and easy to integrate into your data analysis workflows.

---

## 🔧 Installation

You can install the package directly from PyPI using pip:

```bash
pip install Topsis-Vishard-102317240
```

---

## 🚀 Usage

The package provides a command-line interface (CLI) for ease of use.

### Syntax

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

### Parameters

*   **InputDataFile**: Path to the input CSV file containing the data.
*   **Weights**: Comma-separated numbers representing the importance of each criterion (e.g., `1,1,1,2`).
*   **Impacts**: Comma-separated signs (`+` or `-`) indicating if a criterion is beneficial (`+`) or non-beneficial (`-`).
*   **ResultFileName**: Path where the output CSV file with results will be saved.

### Example

```bash
topsis data.csv "1,1,1,1,2" "+,+,-,+,+" result.csv
```

---

## 📊 Input Data Format

The input CSV file should strictly follow this structure:
1.  **First Column**: Object/Alternative Name (e.g., M1, M2, M3).
2.  **Subsequent Columns**: Numeric values for each criterion.

| Fund Name | P1 | P2 | P3 | P4 | P5 |
| :--- | :--- | :--- | :--- | :--- | :--- |
| M1 | 0.94 | 0.88 | 6.5 | 38.8 | 11.78 |
| M2 | 0.69 | 0.48 | 4.4 | 59.8 | 16.34 |
| ... | ... | ... | ... | ... | ... |

---

## 📤 Output

The output file will contain the original data along with two new columns:
*   **Topsis Score**: The calculated score for each alternative (higher is better).
*   **Rank**: The ranking of the alternative based on the score (1 is best).

| Fund Name | ... | Topsis Score | Rank |
| :--- | :--- | :--- | :--- |
| M5 | ... | 0.6988 | 1 |
| M8 | ... | 0.5955 | 2 |
| ... | ... | ... | ... |

---

## 📜 License

This project is licensed under the MIT License.

## 👤 Author

**Vishard Mehta**
*   **Email:** vmehta_be23@thapar.edu
*   **PyPI:** [Topsis-Vishard-102317240](https://pypi.org/project/Topsis-Vishard-102317240/)
langchain-openai 1.1.10
An integration package connecting OpenAI and LangChain
License: MIT
Requires Python: <4.0.0,>=3.10.0

# langchain-openai

[![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?label=%20)](https://pypi.org/project/langchain-openai/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-openai)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-openai)](https://pypistats.org/packages/langchain-openai)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain)

Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).

## Quick Install

```bash
pip install langchain-openai
```

## 🤔 What is this?

This package contains the LangChain integrations for OpenAI through their `openai` SDK.

## 📖 Documentation

For full documentation, see the [API reference](https://reference.langchain.com/python/integrations/langchain_openai/). For conceptual guides, tutorials, and examples on using these classes, see the [LangChain Docs](https://docs.langchain.com/oss/python/integrations/providers/openai).

## 📕 Releases & Versioning

See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.

## 💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).