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Lightning Fast Topsis Score Generator

By Vyom Chopra

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"Topsis-Vyom-101917060":

A package that comes real handy when calculating topsis score. It's one stop destination for all Topsis related work.

  "Using this topsis package, calculating topsis score is nothing more than a child's play."

The function 'build_topsis()' in this package, will return the final dataset with topsis score and corresponding rank column. This function takes three arguments:

• data: the original dataset upon which you want to calculate topsis score,
• weights: a list that contains the pre-determined weights for all the numeric columns (int/float),
• impacts: a list that contains the pre-determined impacts for all the numeric columns ('+'/'-')

To add such an amazing capability to your python workspace, simply type in the following command in the command prompt.

pip install Topsis-Vyom-101917060

This will install the topsis package in your workspace.

The build_topsis() have inbuilt functionality:

• to detect numeric columns and automatically calculate topsis score only off them.
• to check contents of both weights and impacts list for any discrepancy.
• to handle any exception raised.

Now, when you write your python code, simply add this amazing functionality into your code with just a tad bit of new line of code

import Topsis_Vyom_101917060

Sample Code

Let's see a sample case: Given below is a dataset of which, we need to find the topsis score and hence, corresponding rank.

Fund Name	P1	P2	P3	P4	P5
M1	0.93	0.86	4.1	46.1	13
M2	0.67	0.45	6.1	44	12.81
M3	0.72	0.52	3.8	32.7	9.44
M4	0.73	0.53	4.1	45	12.59
M5	0.71	0.5	3.4	55.5	15.03
M6	0.74	0.55	7	63.3	17.9
M7	0.95	0.9	5.1	41.8	12.19
M8	0.63	0.4	7	63.5	17.88

weights = [1,1,1,1,1]
impacts = ['+','-','+','-','+']

In the code editor:

import pandas as pd
import Topsis_Vyom_101917060

data = pd.read_csv(input_data_path)
dataset = build_topsis(data,weights,impacts)
print(dataset)

Hence, we get the final Output as:

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.93	0.86	4.1	46.1	13	0.368067725	8
M2	0.67	0.45	6.1	44	12.81	0.629815594	1
M3	0.72	0.52	3.8	32.7	9.44	0.488377092	5
M4	0.73	0.53	4.1	45	12.59	0.489923292	4
M5	0.71	0.5	3.4	55.5	15.03	0.461216998	6
M6	0.74	0.55	7	63.3	17.9	0.603048108	3
M7	0.95	0.9	5.1	41.8	12.19	0.416449713	7
M8	0.63	0.4	7	63.5	17.88	0.621465197	2

Isn't it amazing!!

License:

MIT