CODEWITHSUNDEEP
pythonbeautifulsoupscreen-scraping
Steve
Steve

Reputation: 4668

extracting data from espn using python on the tag data-reactid

I am looking to scrape some data from an espn site using python.

http://www.espn.co.uk/rugby/playerstats?gameId=293905&league=289234

I am using beautiful soup to extract the content from the page.

 item = soup.findAll('span', attrs={'data-reactid': '136'})[0].text

Will only show me the column heading. Inside that link are data-reactid links that are not shown in the urls. How does one navigate data-react links ? The url stays the same when you click on defending or attacking link ?

Upvotes: 0

Views: 301

Answers (1)

keithpjolley
keithpjolley

Reputation: 2263

The beautifulsoup path looks difficult. I think this may work for you:

import requests
import json
import re

url = "http://www.espn.co.uk/rugby/playerstats?gameId=293905&league=289234"
html_doc = requests.get(url)
# not the best regex but it works. there's a lot of data.
stats = json.loads(re.search(r"window.__INITIAL_STATE__\s*=\s*({.*});",html_doc.text).group(1))

# show what we have
stats['gamePackage']['matchLineUp'].keys()                                                                                     
# Out[42]: dict_keys(['text', 'home', 'away', 'gameState', 'sport', 'show'])

# no idea what this sport is. a typo?
stats['gamePackage']['matchLineUp']['sport']                                                                                        
# Out[43]: 'rugby'

stats['gamePackage']['matchLineUp']['home']                                                                                        
# {'name': 'ITALY',
#  'logo': 'http://a1.espncdn.com/combiner/i?img=/i/teamlogos/rugby/teams/500/20.png&h=35&w=35',
#  'team': [
#   {'id': '91554',
#    'url': 'http://en.espn.co.uk/sport/rugby/player/91554.html',
#    'name': 'Jayden Hayward',
#    'number': '15',
#    'position': 'FB',
#    'captain': False,
#    'subbed': False,
#    'homeAway': 'home',
#    ...

And you can iterate, or whatever:

for home_player in stats['gamePackage']['matchLineUp']['home']['team']: 
   print("{} - {}".format(home_player['name'], home_player['number'])) 
Jayden Hayward - 15
Tommaso Benvenuti - 14
Michele Campagnaro - 13
Tommaso Castello - 12
Luca Sperandio - 11
Tommaso Allan - 10
Tito Tebaldi - 9
Andrea Lovotti - 1
Leonardo Ghiraldini - 2
Simone Ferrari - 3
Alessandro Zanni - 4
Dean Budd - 5
Sebastian Negri - 6
Jake Polledri - 7
Braam Steyn - 8

There's tons of other info in there but figured this would get you going...

Upvotes: 1

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