102003037 TOPSIS PACKAGE HIMANGI SHARMA
Roll Number : 102003037
Subgroup : 3COE18
The program takes csv file containing our data to be ranked, weights and impacts in the form of "+" or "-", seperated by commas as inputs and then outputs a resultant csv file with two additional columns of performance score and Ranks.
What is TOPSIS
TOPSIS, Technique of Order Preference Similarity to the Ideal Solution, is a multi-criteria decision analysis method (MCDA).
It chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.
Installation
How to install the TOPSIS package
using pip install:-
pip install 102003037-topsis-Himangi
For Calculating the TOPSIS Score
Open terminal and type
102003037 102003037-data.csv "1,1,1,1" "+,+,-,+" 102003037-output.csv
The output will then be saved in a newly created CSV file whose name will be provided in the command line by the user.
Input File [102003037-data.csv]:
Topsis mathematical operations to be performed on the input file which contains a dataset having different fields.
Weights ["1,1,1,1"]
The weights to assigned to the different parameters in the dataset should be passed in the argument, seperated by commas.
Impacts ["+,+,-,+"]:
The impacts are passed to consider which parameters have a positive impact on the decision and which one have the negative impact. Only '+' and '-' values should be passed and should be seperated with ',' only.
Output File [102003037-output.csv]:
This argument is used to pass the path of the result file where we want the rank and performance score to be stored.
Metadata
Release files for 102003037-topsis 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| 102003037-topsis-0.0.1.tar.gz | 2.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| 102003037_topsis-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.8 kB
Release files / 102003037-topsis-0.0.1.tar.gz
| Download URL | 102003037-topsis-0.0.1.tar.gz |
|---|---|
| Size | 2.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
eb4d3ef4c35d57888a620d78f7dbbbf592fda023b5b5f2a7950986dbb3ebe514
|
|
BLAKE2b-256 checksum How to use checksums |
91bd2ee96ada96bcd31a95554d52702ac1a2545e43cfdbb848e1ed3f26f91605
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.13
|
Release files / 102003037_topsis-0.0.1-py3-none-any.whl
| Download URL | 102003037_topsis-0.0.1-py3-none-any.whl |
|---|---|
| Size | 2.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
547e09266876f89cd4a1c3dd3730b8257481aefe05049920c8412f259f734afe
|
|
BLAKE2b-256 checksum How to use checksums |
b518983b63dec4fd384f3d5e4931ac770a22f4a3c38e772ef56422368fa0407d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.13
|
Security for 102003037-topsis 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.