Topsis-Dhairya-101916016 1.5.0
TOPSIS Score calculation for MDM
License: MIT

### **Package Description:**

**Python package for TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) Algorithm.**

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### **Steps to install and use the package:**

**STEP 1 :**
Download Python package from PyPi using pip install , Package name - 'Topsis-Dhairya-101916016'

**STEP 2 :**

###### _**For CMD -**_

_**python -m Topsis-Dhairya-101916016.main inputdata.csv 0.25,0.25,0.25,0.25 -,+,+,+ outputdata.csv**_

###### **_For Jupyter/Colab/Python IDE -_**

**_topsis = \_\_import\_\_("Topsis-Dhairya-101916016")_**

**_topsis.topsis("inputdata.csv","0.25,0.25,0.25,0.25","-,+,+,+","outputdata.csv")_**


After running Step 2 , output file (with mentioned name) will be created in same working directory which will create the csv with original dataframe , TOPSIS score and Rank.

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### **Main Function in Package:**

## **topsis**(_inputcsv,_weights,_impacts,_resultfilename)

Takes 4 arguments -

_inputcsv = The name of input csv which have data

_weights = Weights associated with each column in str format. eg - "1,2,1,1"

_impacts = Impacts associated with each column in str format. eg - "+,+,+,+"

_resultfilename = The name of output csv which have TOPSIS score and rank

**This function will perform TOPSIS calculation and store the resultant csv in the working directory with provided name.**

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##Below Functions are used by topsis function for calculation of Topsis Score and Rank

### **checkfornumerical**(_df)

Takes 1 argument -

_df = DataFrame without the first column (which is usually str)

**This function will return True if all the values in _df is numerical**

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### **normalize**(_evaldf)

Takes 1 argument -

_evaldf = DataFrame without the first column (which is usually str)

**This function will return the normalised DataFrame. Normalisation is done on basis of each value divided by root of sum of squares of column.**

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### **addingweights**(_evaldf,_weights)

Takes 2 arguments -

_evaldf = DataFrame without the first column (which is usually str)

_weights = Weights associated with each column in str format. eg - "1,2,1,1"

**This function will return the weighted DataFrame. Adding weights is done by multiplying weight to value for each column.**

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### **idealbestworst**(_evaldf,_impacts)

Takes 2 arguments -

_evaldf = DataFrame without the first column (which is usually str)

_impacts = Impacts associated with each column in str format. eg - "-,+,+,+"

**This function will return the ideal best and ideal worst in DataFrame. 2 new rows will be added in returned Dataframe , first one being Ideal Best and Second one being Ideal Worst.**

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### **euclideandistance**(_evaldf)

Takes 1 argument -

_evaldf = DataFrame without the first column (which is usually str) and 2 more rows including ideal best and worst respectively.

**This function will return the Topsis score as new column in DataFrame provided. 2 new rows will be added in returned Dataframe , It uses Euclidean Distance for calculation of Performance/Topsis Score.**

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### **givingranks**(_evaldf)

Takes 1 argument -

_evaldf = DataFrame without the first column (which is usually str) and 1 more column which is performance score.

**This function will return the Topsis score and Ranking as new column in DataFrame provided. It uses Hashmap for calculation and gives rank 1 to the highest Topsis Score.**

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### **Requirements for Package:**

Pandas

Numpy

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### **Input file handling:**
Input File must have atleast 3 columns and should have Numerical values only except the first column.