Topsis-Ishav-101903773 1.0.0

A Python package implementing TOPSIS technique by Ishav_101903773.

TOPSIS-Python

Submitted By: Ishav Gupta

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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-Ishav-101903773 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 required csv file as output

>>> import Topsis_Ishav_101903773 as ans
>>> inputFile = "input.csv"
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = ans.topsis(inputFile, weights, impacts,"Any_file_name_here.csv")

Method 2: By leaving the last argument blank

>>> import Topsis_Ishav_101903773 as ans
>>> inputFile = "input.csv"
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = ans.topsis(inputFile, weights, impacts)  #the output file will be named as 101903773.csv

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