Topsis-navreen-102017150 0.0.1

A python package for topsis score computation.

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TOPSIS

Navreen Waraich

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This is a Python package curated to show topsis (Technique for Order of preference by similarity to Ideal Solution) ranking to measure the relative performance of each category by mathematical computation

 

UCS633 Project Submission

Name - Navreen Waraich Roll no. - 102017150

The following are the pre-requisites:

• Data: The dataset of which topsis score is to be calculated.
• Weights: A List (int/float) of weights of all columns.
• Impacts: A List ('+'/'-') of all impacts of all columns.

Installation

Use the package manager pip to install foobar.

pip install Topsis-navreen-102017150

This will install the topsis package in your workspace.

The following exceptions are handled and should be taken care of:

• To check number of weights, impacts and number of columns to be same.
• To check contents of both weights and impacts list for any discrepancy.

Write the following in the editor to run the package

import Topsis-navreen-102017150

Sample Input

Following is the input dataset.

Fund Name	P1	P2	P3	P4	P5
M1	0.67	0.45	6.5	42.6	12.56
M2	0.6	0.36	3.6	53.3	14.47
M3	0.82	0.67	3.8	63.1	17.1
M4	0.6	0.36	3.5	69.2	18.42
M5	0.76	0.58	4.8	43	12.29
M6	0.69	0.48	6.6	48.7	14.12
M7	0.79	0.62	4.8	59.2	16.35
M8	0.84	0.71	6.5	34.5	10.64

weights = 1,1,1,1,1
impacts = '+','-','+','-','+'

Output :  
|Fund Name|P1  |P2  |P3 |P4  |P5   |Topsis Score|Rank|
|---------|----|----|---|----|-----|------------|----|
|M1       |0.93|0.86|4.1|46.1|13   |0.368067725 |8   |
|M2       |0.67|0.45|6.1|44  |12.81|0.629815594 |1   |
|M3       |0.72|0.52|3.8|32.7|9.44 |0.488377092 |5   |
|M4       |0.73|0.53|4.1|45  |12.59|0.489923292 |4   |
|M5       |0.71|0.5 |3.4|55.5|15.03|0.461216998 |6   |
|M6       |0.74|0.55|7  |63.3|17.9 |0.603048108 |3   |
|M7       |0.95|0.9 |5.1|41.8|12.19|0.416449713 |7   |
|M8       |0.63|0.4 |7  |63.5|17.88|0.621465197 |2   |