semvec/pypi-packages huggingface dataset:
opengrapher 0.4
Utility for parsing the OpenGraph tags
License: MIT
Requires Python: >=3.4
Keywords: opengraph

## Opengrapher

##### utility for parsing the Open Graph tags from url

_(read more about the specification at http://ogp.me/)_

#### Installation

```bash
   $ pip install opengrapher
```

#### Usage

```python

   >>> import opengrapher
   >>> opengrapher.parse('https://www.imdb.com/title/tt0110912')
   {
      "url": "https://www.imdb.com/title/tt0110912",
      "title": "Pulp Fiction (1994) - IMDb",
      "type": "video.movie",
      "image": "https://m.media-amazon.com/images/M/MV5BNGNhMDIzZTUtNTBlZi00MTRlLWFjM2ItYzViMjE3YzI5MjljXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UY1200_CR97,0,630,1200_AL_.jpg",
      "description": """
         Directed by Quentin Tarantino.  With John Travolta, Uma Thurman, 
         Samuel L. Jackson, Bruce Willis. The lives of two mob hitmen, 
         a boxer, a gangster and his wife, and a pair of diner bandits 
         intertwine in four tales of violence and redemption.
      """,
   }
```

---

List of parsing tags is stored in PARSE_TAGS constant
```python

   >>> from opengrapher import PARSE_TAGS
   >>> PARSE_TAGS
   ["url", "title", "type", "image", "description"]
```

You can pass a specific list of tags to `parse` function if you want:
```python

   >>> import opengrapher
   >>> custom_tags = ['url', 'title']
   >>> opengrapher.parse('https://www.imdb.com/title/tt0110912', parse_tags=custom_tags)
   {
      "url": "https://www.imdb.com/title/tt0110912",
      "title": "Pulp Fiction (1994) - IMDb",
   }
```

> Note that all tags will be transformed to "og:{tag}" format, as it stated in opengraph notation
topsis-Manpreet-101803174 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

## What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.


## How to use this package:



TOPSIS-Manpreet-101803174 can be run as in the following example:

### In command prompt:

```bash
>> TOPSIS data.csv "1,1,2,1" "+,-,+,-" result
```




## Sample Dataset

```python



Model	Correlation	  R2	RMSE	Accuracy
M1	      0.79	     0.62	1.25	 60.89
M2	      0.66	     0.44	2.89	 63.07
M3	      0.56	     0.31	1.57     62.87
M4	      0.82	     0.67	2.68	 70.19
M5	      0.75	     0.56	1.3	     80.39

```

## Output

```python
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

```
fin-formula 0.1.1
My first Python library
License: MIT
Requires Python: >=3.6

# fin formula :notebook: &nbsp;[![](https://camo.githubusercontent.com/17fa56d1fbad7bb4082c9711a77b984b85e79446/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f507974686f6e2d332e362d627269676874677265656e2e737667)](https://python.org)

In this project I have developed my first python package. It is simple package that contain investment related formula.

[![](https://camo.githubusercontent.com/2fb0723ef80f8d87a51218680e209c66f213edf8/68747470733a2f2f666f7274686562616467652e636f6d2f696d616765732f6261646765732f6d6164652d776974682d707974686f6e2e737667)](https://python.org)

# How to install in local system? :thinking:
```pip install fin-formula```


## Bug / Feature Request :man_technologist:
If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue [here](https://github.com/yogeshnile/fin-formula/issues/new) by including your search query and the expected result.

If you'd like to request a new function, feel free to do so by opening an issue [here](https://github.com/yogeshnile/fin-formula/issues/new). Please include sample queries and their corresponding results.


## Connect with Author! 🌐
Known on internet as **Yogesh Nile**

[![][I_LinkedIn]][LinkedIn]  [![][I_Github]][Github] [![][I_Twitter]][Twitter] [![][I_Telegram]][Telegram] [![][I_Instagram]][Instagram]  [![][I_Instagram Personal]][Instagram Personal]   [![][I_discord]][discord]

## Email Me :e-mail:

[![][I_Email]][E-mail]


[LinkedIn]: https://bit.ly/2Ky3ho6
[Github]: https://bit.ly/2yoggit
[Twitter]: https://bit.ly/3dbLJLC
[Telegram]: https://t.me/yogeshnile
[Instagram]: https://bit.ly/3b9Qeo4
[Instagram Personal]: https://bit.ly/32SXHV0
[E-mail]: mailto:yogeshnile.work4u@gmail.com
[discord]: https://discord.gg/R2ug3gR

[I_discord]: https://img.icons8.com/bubbles/50/000000/discord-logo.png
[I_LinkedIn]: https://img.icons8.com/bubbles/50/000000/linkedin.png
[I_Github]: https://img.icons8.com/bubbles/50/000000/github.png
[I_Twitter]: https://img.icons8.com/bubbles/50/000000/twitter.png
[I_Telegram]: https://img.icons8.com/bubbles/50/000000/telegram-app.png
[I_Instagram]: https://img.icons8.com/bubbles/50/000000/instagram-new.png
[I_Instagram Personal]: https://img.icons8.com/bubbles/50/000000/instagram.png
[I_Email]: https://img.icons8.com/bubbles/50/000000/secured-letter.png
Topsis-Navpreet-101803293 1.1.3
This package can be used to calculate the topsis score of multiple component data and rank them accordingly
License: MIT

# TOPSIS Package in Python

UCS538 â€“ Data Science Fundamentals
Assignment06 - TOPSIS

Submitted by: Navpreet Singh Kapoor 

Roll no: 101803293

* * *
<hr style = "border:2px solid gray"> </hr>
## Brief About TOPSIS

TOPSIS stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.

* * *

## Installation
#### Before running, make sure you have pandas installed on your system
```
>> pip install TOPSIS-Navpreet-101803293
```

## Usage

```
>> python
>>>from topsis_analysis.topsispackage import topsis
>>>topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")
```
## Sample Input

This input was used to test the module

<table><thead><tr><th>Model</th><th>Corr</th><th>Rseq</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output 


<table><thead><tr><th>Model</th><th align="right">Corr</th><th align="center">Rseq</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212592</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407846</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828267</td><td>1.0</td></tr></tbody></table>


## License

Â© 2020 Navpreet Singh Kapoor

This repository is licensed under the MIT license. See LICENSE for details.