semvec/pypi-packages huggingface dataset:
flask-oidc-pp 1.4.2
OpenID Connect extension for Flask

flask-oidc
==========

`OpenID Connect <https://openid.net/connect/>`_ support for `Flask <http://flask.pocoo.org/>`_.

.. image:: https://img.shields.io/pypi/v/flask-oidc.svg?style=flat
  :target: https://pypi.python.org/pypi/flask-oidc

.. image:: https://img.shields.io/pypi/dm/flask-oidc.svg?style=flat
  :target: https://pypi.python.org/pypi/flask-oidc

.. image:: https://readthedocs.org/projects/flask-oidc/badge/?version=latest
   :target: http://flask-oidc.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status

.. image:: https://img.shields.io/travis/puiterwijk/flask-oidc.svg?style=flat
  :target: https://travis-ci.org/puiterwijk/flask-oidc

This library should work with any standards compliant OpenID Connect provider.

It has been tested with:

* `Google+ Login <https://developers.google.com/accounts/docs/OAuth2Login>`_
* `Ipsilon <https://ipsilon-project.org/>`_


Project status
==============

This project is in active development.
ScrapeData 0.0.6
Python module to get table data from any webpage and converts it into either Json or Csv

# ScrapeData
Python module to get table data from any webpage and converts it into either Json or Csv

## Installation
Supported python version is 3.0+
```
pip3 install ScrapeData
```

## Usage
There are 4 functions exposed which will do all the work for you available in **TableParser**
```
1. toJsonByClass(URL, tagClass, orientation, tagId = "table")
2. toJsonByIndex(URL, tagIndex, orientation, tagId = "table")
3. toCsvByClass(URL, tagClass, fileName, tagId = "table")
4. toCsvByIndex(URL, tagIndex, fileName, tagId = "table")
```

1. **URL** :- The url from where table needs to fetched
2. **tagClass** :- if there is any class associated with tag
3. **tagIndex** :- the occurance index of the table you want to fetch.
4. **tagId** :- the html tag which is by default table (optional value)
5. **orientation** :-
```
‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
‘records’ : list like [{column -> value}, … , {column -> value}]
‘index’ : dict like {index -> {column -> value}}
‘columns’ : dict like {column -> {index -> value}}
‘values’ : just the values array
‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}} describing the data, and the data component is like orient='records'.
```
There are other functions available in **Utils** if you want to customize the implementation.
```
1. getDataFromUrl(url)
2. parseDataUsingHtmlParser(page)
3. findTableByClass(soup, tagId, tagClass)
4. findTableByIndex(soup, tagId, tagIndex)
5. convertTableToList(table)
6. convertListToJson(dataList, orientation = 'none')
7. convertListToCsv(dataList, fileName)
```

## Example Code

```
from ScrapeData import TableParser as parser
from ScrapeData import Utils

URL = 'https://www.w3schools.com/python/module_requests.asp'
tableClass = 'w3-table-all notranslate'

print(parser.toJsonByClass(URL, tableClass, 'records'))
print(parser.toJsonByIndex(URL, 0, 'records'))
print(parser.toCsvByIndex(URL, 0, 'records.csv'))
```