Topsis-Prabhnoor-102083037 0.0.1

A package to find the topsis score

##Package for calculating TOPSIS score and ranking of a given dataframe

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

• topsis.norm_dataframe(data)

  • data A dataframe with m rows and n columns; 1st column is treated as index; All the calculations are done from 2nd column onwards

• topsis.topsis_calc(data,weights,impacts)

  • data A dataframe with m rows for m alternatives and n columns for n-1 criterions. 1st column is treated as index
  • weights A numeric list with length equal to number of columns (from 2nd to last columns) in dataframe for weights of criterions.
  • impacts A character list of "+" and "-" signs for the way that each criterion influences on the alternatives.

3. Value (return)

• topsis.norm_dataframe(data) A normalized dataframe (distributive normalization)
• 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. Example raw=pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv') w=[0.25,0.25,0.25,0.25] i=['-','+','+','+'] topsis_calc(raw,w,i)