semvec/pypi-packages huggingface dataset:
Topsis-ParneetKaur-101917044 1.0.4
A package that calculates Topsis Score and Rank them accordingly
License: MIT

topsis-python
Package Description :
Python package for TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) ALGORITHM.

Motivation :
This is a part of project - I made for UCS633 - Data analytics and visualization at TIET.
@Author : Sourav Kumar
@Roll no. : 101883068

Algorithm :
STEP 1 :
Create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria.
Apply any preprocessing if required.

STEP 2 :
The matrix is then normalised using the norm.

STEP 3 :
Calculate the weighted normalised decision matrix.

STEP 4 :
Determine the worst alternative and the best alternative.

STEP 5 :
Calculate the L2-distance between the target alternative i and the worst condition.

STEP 6 :
Calculate the similarity to the worst condition.

STEP 7 :
Rank the alternatives according to final performance scores.

Getting started Locally :
Run On Terminal
python -m topsis.topsis <filename.csv> <weights> <impacts>
ex. python python -m topsis.topsis topsis.csv 0.25,0.25,0.25,0.25 -,+,+,+

Run In IDLE
from topsis import topsis
t = topsis.topsis('filepath', [list of weights], [list of impacts])
t.topsis_main()

Run on Jupyter
Open terminal (cmd)
jupyter notebook
Create a new python3 file.
If file <filename.csv> doesn't exists, then make sure to upload the file to jupyter env.
from topsis import topsis
t = topsis.topsis('filepath', [list of weights], [list of impacts])
t.topsis_main()

topsis_main() has been specifically designed to inhibit leakeage of inbuilt functions.
topsis_main(debug=True) use this to display all the intermediate matrices.
Make sure that filename.csv is in same directory where package is installed.
PAPER :
Find the research paper at arxiv.

OUTPUT :
Prints out overall ml models ranked and the best model / alternative.

output result on jupyter output result on idle output result on cmd

TESTING :
The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.
sdknotion 0.1
An unofficial Notion SDK for Python
License: MIT
Keywords: notion

# NotionSDK for Python

![Allow what you think is necessary](images/logo-notionsdk.png)

NotionSDK is an open source Python package that allows you to communicate with the [Notion API](https://developers.notion.com/reference/intro).

## Installation
Run the following command to install:
`pip install notionsdk`

## How to get your Notion auth token
Follow this tutorial: [Get your Notion auth token](docs/get_notion_token.md)

## Basic Example
```python
import notionsdk as Notion # Import notionsdk

# Create an notion client instance
client = Notion.client("auth_token")

# Find a Database
# If found: Returns a Database instance
# If not found: Returns None
found_database = Notion.Database.find(client, "Database name")

# Get database info
db_title = found_database.title
db_id = found_database.id

# Retrieve database content
content = found_database.retrieve()

## Create a database
# If created: Returns a Database instance
# If didn't create: Returns None

# First, get the page_id (parent)[
page_id = "page-id-example"

# Second, create the model
model = Notion.Model("Database title")
model.add([
    #{"field name": "field type"}
    {"name": "text"},
    {"description": "rich_text"}
])

created_database = Notion.Database.create(client, page_id, model)
```

## Roadmap 🗺️
- 🗃️ Database 1 (v0.1)
    - [x] Retrieve
    - [x] Create

- 🗃️ Database 2 (v0.2)
    - [ ] Update
    - [ ] Query

- 📄 Pages 1 (v0.3)
    - [ ] Retrieve
    - [ ] Create
    - [ ] Delete

- 📄 Pages 2 (v0.4)
    - [ ] Update
    - [ ] Query

- 🔳 Blocks 1 (v0.5)
    - [ ] Retrieve
    - [ ] Create

- 🔳 Blocks 2 (v0.6)
    - [ ] Update
    - [ ] Query

- 👤 Users (v0.7)
    - [ ] Retrieve an user
    - [ ] List

- 🔍️ Search (v0.8)
    - [ ] Query pages

- 🖇️ [Retrieve a page property item](https://developers.notion.com/reference/retrieve-a-page-property) (0.9)
- 🔑 [Retrieve your token's bot user](https://developers.notion.com/reference/get-self) (1.0)
Topsis-Parneet-101917044 1.0.4
A package that calculates Topsis Score and Rank them accordingly
License: MIT

topsis-python
Package Description :
Python package for TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) ALGORITHM.

Motivation :
This is a part of project - I made for UCS633 - Data analytics and visualization at TIET.
@Author : Sourav Kumar
@Roll no. : 101883068

Algorithm :
STEP 1 :
Create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria.
Apply any preprocessing if required.

STEP 2 :
The matrix is then normalised using the norm.

STEP 3 :
Calculate the weighted normalised decision matrix.

STEP 4 :
Determine the worst alternative and the best alternative.

STEP 5 :
Calculate the L2-distance between the target alternative i and the worst condition.

STEP 6 :
Calculate the similarity to the worst condition.

STEP 7 :
Rank the alternatives according to final performance scores.

Getting started Locally :
Run On Terminal
python -m topsis.topsis <filename.csv> <weights> <impacts>
ex. python python -m topsis.topsis topsis.csv 0.25,0.25,0.25,0.25 -,+,+,+

Run In IDLE
from topsis import topsis
t = topsis.topsis('filepath', [list of weights], [list of impacts])
t.topsis_main()

Run on Jupyter
Open terminal (cmd)
jupyter notebook
Create a new python3 file.
If file <filename.csv> doesn't exists, then make sure to upload the file to jupyter env.
from topsis import topsis
t = topsis.topsis('filepath', [list of weights], [list of impacts])
t.topsis_main()

topsis_main() has been specifically designed to inhibit leakeage of inbuilt functions.
topsis_main(debug=True) use this to display all the intermediate matrices.
Make sure that filename.csv is in same directory where package is installed.
PAPER :
Find the research paper at arxiv.

OUTPUT :
Prints out overall ml models ranked and the best model / alternative.

output result on jupyter output result on idle output result on cmd

TESTING :
The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.
Topsis-Naman-101903245 1.5
This package helps to find the topsis score and ranking based on that score for the given input.
License: MIT
Keywords: TOPSIS,RANKS,SCORE

#Topsis-Naman-101903245

Calculate the topsis score and ranking based on it by providing the input excel file along with the weights and impacts for each column and also the name of the output file.

#installing
pip install Topsis-Naman-101903245

#usage
from Topsis_Naman_101903245.topsis import find_topsis

find_topsis("data.xslx",[1,1,2,3],["+","+","-","-"],"output.csv")