semvec/pypi-packages huggingface dataset:
twofourseven 0.1.11
Package to scrape 247Sports website for recruiting data

# TwoFourSeven
Python Package to scrape recruiting data from 247Sports website

Able to scrape transfer portal page as well.

## Installation:
pip install twofourseven <br>

## Classes:
<strong>Recruit</strong> <br>
  getClass(team, year) - Returns df of the recruiting class of a given team from the given year. (does NOT include transfers) <br>
  getAllTime(team) - Returns df of top recruits for a team for recent history. <br>
  getFBPlayerData(year) - Returns df of top 2000 fb recruits for a given year. <br>
  getBBPlayerData(year) - Returns df of top 2000 (or less depending on how many are ranked) bb recruits for a given year. <br>
  getDraft(year) - Returns df of the NFL draft from a given year <br>
<br>
<strong>TransferPortal</strong> <br>
  getFootballData(year) - Returns df of everyone who entered the transferportal for a given year for football <br>
  getBasketballData(year) - Returns df of everyone who entered the transferportal for a given year for basketball <br>
json-scribe 2.0.0
JSON-based logging for the masses

JSON Logging
============
Write your logs as json-lines.

+---------------+-------------------------------------------------+
| Source Code   | `aweber/json-scribe`_                           |
+---------------+-------------------------------------------------+
| Downloads     | json-scribe @ `pypi.org`_                       |
+---------------+-------------------------------------------------+

This library is the result of sending docker container logs to loggly.
Loggly works well with structured Syslog data but if you pass structured
Syslog lines to an aggregator as JSON you end up with an unindexed mess.
This library includes a few classes that make it easier and cleaner to send
log content as JSON objects on single lines *including exceptions*.

Usage
-----

Configuration
^^^^^^^^^^^^^

.. code-block:: json

   {
      "version": 1,
      "filters": {
         "defaultsetter": {
            "()": "jsonscribe.AttributeSetter",
            "add_fields": {
               "correlation_id": "ext://UUID"
            }
         }
      },
      "formatters": {
         "jsonlines": {
            "()": "jsonscribe.JSONFormatter",
            "include_fields": [
               "name",
               "levelname",
               "asctime",
               "message",
               "module",
               "correlation_id",
               "exc_info"
            ],
         }
      },
      "handlers": {
         "loggly": {
            "class": "logging.StreamHandler",
            "formatter": "jsonlines",
            "filters": ["defaultsetter"],
            "stream": "ext://sys.stdout"
         }
      },
      "loggers": {
         "somepackage": {
            "level": "DEBUG",
         }
      },
      "root": {
         "level": "INFO",
         "handlers": ["jsonlines"]
      }
   }

Logging
^^^^^^^
The following snippet is the simplest usage.  It is nothing more than the
textbook usage of the logging module.  It uses the logging configuration from
above and generates a JSON blob.

.. code-block:: python

   import logging.config
   import json

   if __name__ == '__main__':
      config = json.load(open('config.json'))
      logging.config.dictConfig(config)
      logger = logging.getLogger(__package__).getChild('main')
      logger.info('processing request')

The JSON message looks something like the following.  It is reformatted to
make it readable.  The default is to render it as compact JSON.

.. code-block:: json

   {
      "name": "somepackage.main",
      "levelname": "INFO",
      "asctime": "2018-08-09T07:44:54.231",
      "module": "somepackage.entrypoints",
      "correlation_id": "33CA3FB3-D66F-4D32-83A5-D5A8C3D92A6E",
      "message": "processing request",
      "exc_info": null
   }


.. _aweber/json-scribe: https://github.com/aweber/json-scribe
.. _pypi.org: https://pypi.org/project/json-scribe