Topsis-Sarthak-101903774 0.3

A Python package implementing TOPSIS technique.

TOPSIS-Python

Submitted By: Sarthak Arora

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What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution. More details at wikipedia.

How to use this package:

The Topsis-Sarthak-101903774 has a function topsis() that takes 4 parameters, i.e., inputFile, weights, impacts, and outputFile, and returns the resulting dataframe having Topsis Score and Rank as additional columns.

Where,

• inputFile: Can be either csv file or pandas dataframe. Input file must contain three or more columns, where, First column is the object/variable name like M1, M2, M3, M4, etc. Also, from 2nd to last columns must contain numeric values only.
• weights: In the form of string having numerical values separated by commas.
• impacts: In the form of string having + or - values separated by commas. Here, + refers to positive impact, whereas, - refers to negative impact.
• outputFile (optional): csv file in which output of the function will be stored.

For Example,

Method 1: By passing csv file as input

>>> import Topsis_Sarthak_101903774 as s
>>> inputFile = "input.csv"
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = s.topsis(inputFile, weights, impacts)

Sample Input

Dataset

Fund Name	P1	P2	P3	P4	P5
M1	0.65	0.42	3.3	46.3	12.67
M2	0.81	0.66	4.9	51.4	14.44
M3	0.87	0.76	6	65.4	18.26
M4	0.87	0.76	4.2	40.7	11.63
M5	0.75	0.56	6.8	57.5	16.4
M6	0.64	0.41	5.3	44.7	12.76
M7	0.77	0.59	4.7	49.8	13.97
M8	0.7	0.49	3.1	43.9	12.05

Weights

weights = "1,1,1,2,1"

Impacts

impacts = "+,+,-,+,+"

Sample Output

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.65	0.42	3.3	46.3	12.67	0.41202513	7
M2	0.81	0.66	4.9	51.4	14.44	0.510060544	2
M3	0.87	0.76	6	65.4	18.26	0.685105262	1
M4	0.87	0.76	4.2	40.7	11.63	0.433129944	5
M5	0.75	0.56	6.8	57.5	16.4	0.469643489	3
M6	0.64	0.41	5.3	44.7	12.76	0.225789842	8
M7	0.77	0.59	4.7	49.8	13.97	0.451566364	4
M8	0.7	0.49	3.1	43.9	12.05	0.418005937	6