This is a Python Package implementing TOPSIS used for multi-criteria decision analysis method
Project description
Project Description
Topsis-Nandini-102017101
Topsis-Nandini-102017101 is a Python Package implementing Topsis method used for multi-criteria decision analysis. Topsis stands for ‘Technique for Order of Preference by Similarity to Ideal Solution’.
Topsis-Nandini-102017101 intends to make the process of TOPSIS simple in python.
Key features of the package are -
- Easy to use
- Numpy Based
- Ideal for Students
Installation
Use the package manager pip to install Topsis-Nandini-102017101
Syntax
Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and output file name with .csv extension
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example:
topsis inputfile.csv “1,1,1,2” “+,+,-,+” result.csv
or vectors can be entered without " "
topsis inputfile.csv 1,1,1,2 +,+,-,+ result.csv
But the second representation does not provide for inadvertent spaces between vector values. So, if the input string contains spaces, make sure to enclose it between double quotes (" ").
Example
Sample input data
Fund Name | P1 | P2 | P3 | P4 | P5 |
---|---|---|---|---|---|
M1 | 0.65 | 0.42 | 5.3 | 43.8 | 12.54 |
M2 | 0.94 | 0.88 | 4 | 61.5 | 16.83 |
M3 | 0.72 | 0.52 | 3.2 | 69.7 | 18.54 |
M4 | 0.89 | 0.79 | 5.4 | 49 | 14.02 |
M5 | 0.75 | 0.56 | 6.9 | 49.4 | 14.4 |
M6 | 0.6 | 0.36 | 4.2 | 68.3 | 18.37 |
M7 | 0.89 | 0.79 | 6.7 | 44.6 | 13.25 |
M8 | 0.79 | 0.62 | 3.8 | 51.7 | 14.23 |
weights vector = [ 1 , 1 , 1 , 1 , 1 ]
impacts vector = [ + , + , + , + , + ]
Sample output data
Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |
---|---|---|---|---|---|---|---|
M1 | 0.65 | 0.42 | 5.3 | 43.8 | 12.54 | 0.287855029 | 8 |
M2 | 0.94 | 0.88 | 4 | 61.5 | 16.83 | 0.631106388 | 2 |
M3 | 0.72 | 0.52 | 3.2 | 69.7 | 18.54 | 0.412672373 | 5 |
M4 | 0.89 | 0.79 | 5.4 | 49 | 14.02 | 0.605503106 | 3 |
M5 | 0.75 | 0.56 | 6.9 | 49.4 | 14.4 | 0.536194294 | 4 |
M6 | 0.6 | 0.36 | 4.2 | 68.3 | 18.37 | 0.36630047 | 7 |
M7 | 0.89 | 0.79 | 6.7 | 44.6 | 13.25 | 0.635938379 | 1 |
M8 | 0.79 | 0.62 | 3.8 | 51.7 | 14.23 | 0.373853804 | 6 |
Please Note:
- Categorical values are not handled
- Enter the path for your input csv file
-Enter the weights vector with each weight separated by commas
-Enter the impact vector with each impact separated by commas
-Enter the name of csv file in which you want to store output dataframe.
License
MIT
Free Software, Hell Yeah!
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