| Name | topsis-Aryan-7019 JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | None |
| Summary | A Python package implementing the TOPSIS method |
| upload_time | 2025-01-25 16:37:20 |
| maintainer | None |
| docs_url | None |
| author | Aryan |
| requires_python | >=3.6 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Topsis-Aryan-102217019
A Python package for implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, used for multi-criteria decision analysis.
## Installation
```bash
pip install Topsis-Aryan-102217019
```
## Usage
The package can be used through command line:
```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
### Example
```bash
topsis data.xlsx "1,1,1,1,1" "+,+,+,+,+" output.csv
```
### Input Format
* Input File:
* The file can be in CSV or Excel format (.csv or .xlsx).
* The first column should contain the names of the objects/variables.
* The remaining columns must contain numeric values only (criteria values).
### Parameters
1. <InputDataFile>: The input file name along with its path (e.g., data.csv or data.xlsx).
2. <Weights>: A comma-separated string of numeric weight values for each criterion (e.g., "1,1,1,1").
3. <Impacts>: A comma-separated string of + or - symbols, indicating whether the criterion is beneficial (+) or non-beneficial (-) (e.g., "+,+,-,+").
4. <ResultFileName>: The desired output file name including its path (e.g., output.csv).
### Output
The output file (CSV format) will include:
All input data columns
Additional columns:
TOPSIS Score: The calculated score for each alternative.
Rank: The rank of each alternative based on the TOPSIS score (higher score = better rank).
## License
MIT License
## Author
Aryan Sharma
Roll Number: 102217019
If you have any questions or suggestions, feel free to reach out!
Raw data
{
"_id": null,
"home_page": null,
"name": "topsis-Aryan-7019",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": null,
"author": "Aryan",
"author_email": "asharma27_be22@thapar.edu",
"download_url": "https://files.pythonhosted.org/packages/a7/3a/8ed51132fd5b5d20c2d73ad59a891e6a6fc3ab29f102198ed747d207cfb4/topsis_aryan_7019-0.0.1.tar.gz",
"platform": null,
"description": "# Topsis-Aryan-102217019\r\n\r\nA Python package for implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, used for multi-criteria decision analysis.\r\n\r\n## Installation\r\n\r\n```bash\r\npip install Topsis-Aryan-102217019 \r\n```\r\n\r\n## Usage\r\n\r\nThe package can be used through command line:\r\n\r\n```bash\r\ntopsis <InputDataFile> <Weights> <Impacts> <ResultFileName>\r\n```\r\n\r\n### Example\r\n\r\n```bash\r\ntopsis data.xlsx \"1,1,1,1,1\" \"+,+,+,+,+\" output.csv\r\n```\r\n\r\n### Input Format\r\n* Input File:\r\n * The file can be in CSV or Excel format (.csv or .xlsx).\r\n * The first column should contain the names of the objects/variables.\r\n * The remaining columns must contain numeric values only (criteria values).\r\n### Parameters\r\n1. <InputDataFile>: The input file name along with its path (e.g., data.csv or data.xlsx).\r\n2. <Weights>: A comma-separated string of numeric weight values for each criterion (e.g., \"1,1,1,1\").\r\n3. <Impacts>: A comma-separated string of + or - symbols, indicating whether the criterion is beneficial (+) or non-beneficial (-) (e.g., \"+,+,-,+\").\r\n4. <ResultFileName>: The desired output file name including its path (e.g., output.csv).\r\n\r\n### Output\r\nThe output file (CSV format) will include:\r\n\r\nAll input data columns\r\nAdditional columns:\r\nTOPSIS Score: The calculated score for each alternative.\r\nRank: The rank of each alternative based on the TOPSIS score (higher score = better rank).\r\n\r\n## License\r\nMIT License\r\n\r\n## Author\r\nAryan Sharma\r\nRoll Number: 102217019\r\n\r\nIf you have any questions or suggestions, feel free to reach out!\r\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A Python package implementing the TOPSIS method",
"version": "0.0.1",
"project_urls": {
"Source Repository": "https://github.com/aryansharma19992e/topsis"
},
"split_keywords": [],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "f89a2da41aed1d9a5a603cd8a1806311646b3ad13a4da390fc60e1bde9eeb1bb",
"md5": "ef00f2bac8d893d0b10944b4bf27f7a8",
"sha256": "0dc74b5bf4f7592bfc863d016dbecee06fe10ab42d375bc9790104973368a2ac"
},
"downloads": -1,
"filename": "topsis_Aryan_7019-0.0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "ef00f2bac8d893d0b10944b4bf27f7a8",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 5003,
"upload_time": "2025-01-25T16:37:18",
"upload_time_iso_8601": "2025-01-25T16:37:18.641462Z",
"url": "https://files.pythonhosted.org/packages/f8/9a/2da41aed1d9a5a603cd8a1806311646b3ad13a4da390fc60e1bde9eeb1bb/topsis_Aryan_7019-0.0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "a73a8ed51132fd5b5d20c2d73ad59a891e6a6fc3ab29f102198ed747d207cfb4",
"md5": "e2adcc9cbdbae22f50c1abd7d19ef104",
"sha256": "ae2e9ac11abf4a8873ab767cf8e03e562a38d1912d41db5b5b085672aaf8fb2e"
},
"downloads": -1,
"filename": "topsis_aryan_7019-0.0.1.tar.gz",
"has_sig": false,
"md5_digest": "e2adcc9cbdbae22f50c1abd7d19ef104",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 4563,
"upload_time": "2025-01-25T16:37:20",
"upload_time_iso_8601": "2025-01-25T16:37:20.310496Z",
"url": "https://files.pythonhosted.org/packages/a7/3a/8ed51132fd5b5d20c2d73ad59a891e6a6fc3ab29f102198ed747d207cfb4/topsis_aryan_7019-0.0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-25 16:37:20",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "aryansharma19992e",
"github_project": "topsis",
"github_not_found": true,
"lcname": "topsis-aryan-7019"
}
