sandhya-goyal/topsis_102017182

Topsis_102017182

Topsis

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

In a general sense, it is the aspiration of human being to make "calculated" decision in a position of multiple selection. In scientific terms, it is the intention to develop analytical and numerical methods that take into account multiple alternatives with multiple criteria. TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is one of the numerical methods of the multi-criteria decision making

Installation

Use the package manager pip to install the package

pip install Topsis_102017182_sandhya_goyal

Import

from Topsis_102017182_sandhya_goyal import topsis

Usage

topsis module has a function names get_score which takes 3 arguements as:

1. dataframe - It is a pandas dataframe which has atleast 3 columns(including the first column with names). It should only have numerical values. Any non-numerical value should be encoded before passing it to function.
2. weights - It is a string of comma(,) separated numbers which tell the weight of each criteria.
3. impacts - It is a string of comma(,) separated + and - sign showing the impact of criteria on decision making.

The function return the original pandas dataframe with 2 more columns added, which are Topsis Score and Rank.

topsis.get_score(dataframe,weights,impacts)

Example

data.csv (Input):

contestent	sur	tal	lah	pitch	sharpness
a	4	18	6.5	35	15
b	6	14	4	38	19
c	6	12	5	42	20
d	8	26	7	50	25
e	3	14	6	40	14

import pandas as pd
df = pd.read_csv('data.csv')
weights = "+,+,+,+,+"
impacts = "1,1,1,1,1"
print(topsis.get_score(df,weights,impacts))

Output:

contestent	sur	tal	lah	pitch	sharpness	output
a	4	18	6.5	35	15	3
b	6	14	4	38	19	4
c	6	12	5	42	20	2
d	8	26	7	50	25	1
e	3	14	6	40	14	5