# Topsis
This is a python library which implements topsis.Topsis is a technique which is used for Multiple Criteria Decision Making(MCDM). It takes weights and impacts of criteria does the decision making by calculating the similarity to ideal solution.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the package
```
pip install Topsis_Keshav_Garg_102017064
```
## Import
```
from Topsis_Keshav_Garg_102017064 import topsis
```
## Usage
topsis module has a function names get_score which takes 3 arguements as:
1. dataframe - It is a pandas dataframe which has atleast 3 columns(including the first column with names). It should only have numerical values. Any non-numerical value should be encoded before passing it to function.
2. weights - It is a string of comma(,) separated numbers which tell the weight of each criteria.
3. impacts - It is a string of comma(,) separated + and - sign showing the impact of criteria on decision making.
The function return the original pandas dataframe with 2 more columns added, which are Topsis Score and Rank.
```
topsis.get_score(dataframe,weights,impacts)
```
## Example
test.csv (Input):
|mobile|ram|memo|display|battery|price|
|------|---|----|-------|-------|-----|
|a|4|128|6.5|3500|15000|
|b|6|64|6.4|3800|16000|
|c|6|128|6.8|4200|19000|
|d|8|256|7|5000|25000|
|e|3|64|6.2|4000|14000|
```
from Topsis_Keshav_Garg_102017064 import topsis
import pandas as pd
df = pd.read_csv('./test.csv')
weights = "+,+,+,+,-"
impacts = "1,1,1,1,1"
print(topsis.get_score(df,weights,impacts))
```
Output:
| |mobile| ram| memo| display| battery| price| Topsis Score| Rank|
|--|------|----|--------|--------|-------|-------|----------|-------|
|0 |a |4 |128 |6.5 |3500| 15000| 0.379477| 3|
|1 |b |6 |64 |6.4 |3800| 16000| 0.341963| 4|
|2 |c |6 |128 |6.8 |4200| 19000| 0.439078| 2|
|3 |d |8 |256 |7.0 |5000| 25000| 0.729791| 1|
|4 |e |3 |64 |6.2 |4000| 14000| 0.276912| 5|
Raw data
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"description": "# Topsis\nThis is a python library which implements topsis.Topsis is a technique which is used for Multiple Criteria Decision Making(MCDM). It takes weights and impacts of criteria does the decision making by calculating the similarity to ideal solution.\n\n\n## Installation\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install the package\n```\npip install Topsis_Keshav_Garg_102017064\n```\n\n\n## Import\n\n```\nfrom Topsis_Keshav_Garg_102017064 import topsis\n```\n\n## Usage\ntopsis module has a function names get_score which takes 3 arguements as:\n1. dataframe - It is a pandas dataframe which has atleast 3 columns(including the first column with names). It should only have numerical values. Any non-numerical value should be encoded before passing it to function.\n2. weights - It is a string of comma(,) separated numbers which tell the weight of each criteria.\n3. impacts - It is a string of comma(,) separated + and - sign showing the impact of criteria on decision making.\n\nThe function return the original pandas dataframe with 2 more columns added, which are Topsis Score and Rank.\n```\ntopsis.get_score(dataframe,weights,impacts)\n```\n\n\n## Example\n\ntest.csv (Input):\n\n|mobile|ram|memo|display|battery|price|\n|------|---|----|-------|-------|-----|\n|a|4|128|6.5|3500|15000|\n|b|6|64|6.4|3800|16000|\n|c|6|128|6.8|4200|19000|\n|d|8|256|7|5000|25000|\n|e|3|64|6.2|4000|14000|\n\n\n```\nfrom Topsis_Keshav_Garg_102017064 import topsis\nimport pandas as pd\ndf = pd.read_csv('./test.csv')\nweights = \"+,+,+,+,-\"\nimpacts = \"1,1,1,1,1\"\nprint(topsis.get_score(df,weights,impacts))\n```\n\nOutput:\n| |mobile| ram| memo| display| battery| price| Topsis Score| Rank|\n|--|------|----|--------|--------|-------|-------|----------|-------|\n|0 |a |4 |128 |6.5 |3500| 15000| 0.379477| 3|\n|1 |b |6 |64 |6.4 |3800| 16000| 0.341963| 4|\n|2 |c |6 |128 |6.8 |4200| 19000| 0.439078| 2|\n|3 |d |8 |256 |7.0 |5000| 25000| 0.729791| 1|\n|4 |e |3 |64 |6.2 |4000| 14000| 0.276912| 5|\n\n",
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