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

Project description

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

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Topsis_Keshav_Garg_102017064-0.0.1.tar.gz (5.8 kB view hashes)

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