Topsis-Keshav-Garg-102017064 0.0.1

This package performs the topsis(multiple criterion decision making) on a pandas dataframe and return the rank and topsis score.

Topsis

This is a python library which implements topsis.Topsis is a technique which is used for Multiple Criteria Decision Making(MCDM). It takes weights and impacts of criteria does the decision making by calculating the similarity to ideal solution.

Installation

Use the package manager pip to install the package

pip install Topsis_Keshav_Garg_102017064

Import

from Topsis_Keshav_Garg_102017064 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

test.csv (Input):

mobile	ram	memo	display	battery	price
a	4	128	6.5	3500	15000
b	6	64	6.4	3800	16000
c	6	128	6.8	4200	19000
d	8	256	7	5000	25000
e	3	64	6.2	4000	14000

from Topsis_Keshav_Garg_102017064 import topsis
import pandas as pd
df = pd.read_csv('./test.csv')
weights = "+,+,+,+,-"
impacts = "1,1,1,1,1"
print(topsis.get_score(df,weights,impacts))

Output:

	mobile	ram	memo	display	battery	price	Topsis Score	Rank
0	a	4	128	6.5	3500	15000	0.379477	3
1	b	6	64	6.4	3800	16000	0.341963	4
2	c	6	128	6.8	4200	19000	0.439078	2
3	d	8	256	7.0	5000	25000	0.729791	1
4	e	3	64	6.2	4000	14000	0.276912	5