Package for calculating TOPSIS score and ranking of a given dataframe

Submitted by:

• Prabhnoor Singh
• 102083037
• 3CO12

Description

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multiple-criteria decision making (MCDM) method.

1. Usage

a) topsis.norm_dataframe(data)

Normalize all the columns except 1st(treated as index); distributive normalization

b) topsis.topsis_calc(data,weights,impacts)

Calculates and returns the topsis score and rank for the given arguments

2. Arguments

a) topsis.norm_dataframe(data)

1. data

A dataframe with m rows and n columns; First column is treated as index; All the calculations are done from second column onwards

b) topsis.topsis_calc(data,weights,impacts)

1. data

A dataframe with m rows for m alternatives and n columns for n-1 criterions. First column is treated as index

2. weights

A numeric list with length equal to number of columns (from second to last columns) in dataframe for weights of criterions.

3. impacts

A character list of "+" and "-" signs for the way that each criterion influences on the alternatives.

3. Value (return)

a) topsis.norm_dataframe(data)

A normalized dataframe (distributive normalization)

b) topsis.topsis_calc(data,weights,impacts)

Input dataframe with 2 additional columns

• TOPSIS Score

  TOPSIS score of alternatives.

• Rank

  Rank of alternatives based on TOPSIS scores.

4. Installation

> pip install Topsis-Prabhnoor-102083037

5. Example

>>> import pandas as pd
>>> from topsispackage_prabhnoorsingh import topsis
>>> raw=pd.DataFrame({"CR": ['M1', 'M2', 'M3', 'M4', 'M5'], "A": [250, 200, 300, 275, 225], "B": [16, 16, 32, 32, 16], "C": [12, 8, 16, 8, 16], "D": [5, 3, 4, 4, 2]})
>>> w=[0.25,0.25,0.25,0.25]
>>> i=['-','+','+','+']
>>> topsis.norm_dataframe(raw)
>>> topsis.topsis_calc(raw,w,i)