Topsis Implementation in Python
This Python package implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision-making.
Features
- Calculate TOPSIS scores and rank alternatives.
- Input data via CSV files.
- Comprehensive validation of inputs.
Installation
To install the package, use the following command:
pip install TOPSIS-JASHANDEEP-102218023
Usage
Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and result csv filename followed by .csv extension.
topsis "sample.csv" "1,1,1,1" "+,-,+,+" "result.csv"
1 maintainer
Metadata
1 maintainer
Release files for TOPSIS-JASHANDEEP-102218023 1.1.2
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| topsis_jashandeep_102218023-1.1.2.tar.gz | 3.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| TOPSIS_JASHANDEEP_102218023-1.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.4 kB
Release files / topsis_jashandeep_102218023-1.1.2.tar.gz
| Download URL | topsis_jashandeep_102218023-1.1.2.tar.gz |
|---|---|
| Size | 3.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/5.1.1 CPython/3.10.11
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Release files / TOPSIS_JASHANDEEP_102218023-1.1.2-py3-none-any.whl
| Download URL | TOPSIS_JASHANDEEP_102218023-1.1.2-py3-none-any.whl |
|---|---|
| Size | 4.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.11
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Security for TOPSIS-JASHANDEEP-102218023 1.1.2
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