Stubhub API

A Python wrapper for the stubhub inventory search API. You can see the full tutorial in the Stubhub API.ipynb

Getting Started

from stubhub_scraper import St

import pandas as pd
import numpy as np

## Enter user's API key, secret, and Stubhub login
app_token = '7131e534-bbec-374f-b1e4-1bdf6909a8ee'
consumer_key = 'jC475_MWRt6VV0aRz6nhA4Kpfloa'
consumer_secret = 'U7bW44Spj64CDYwUQSofJaMh1zka'
stubhub_username = ''
stubhub_password = ''
    
st = St(app_token,consumer_key,consumer_secret,stubhub_username,stubhub_password)

b'{"access_token":"254gce9e-3abd-3274-9d1f-13b7c3bf96b2","refresh_token":"1fdrf277-ac54-3d8d-b59b-58aa1482f7f4","scope":"default","token_type":"Bearer","expires_in":14846462}'

If authentication is succesfull you will see a message similar to above with your access token.

Single event

# San Antonio at Golden State game on March 19th 2018 
listings = st.get_listings(103138286,pages=True)
listings[0]

{'currentPrice': 46.9,
 'dirtyTicketInd': False,
 'listingId': 1314761532,
 'listingPrice': 37.0,
 'quantity': 2,
 'retrieveTime': '2018-03-17 20:21:08',
 'row': '14',
 'score': 0.0,
 'seatNumbers': '1;2',
 'sectionId': 127168,
 'sectionName': 'Balcony Corner 229',
 'sellerSectionName': '229',
 'zoneId': 7453,
 'zoneName': 'Balcony Corner'}

# Number of listings
len(listings)

298

listings is a list of dictionaries one of which you can see above. You can convert it to a dataframe and save as a csv.

listings = pd.DataFrame(listings)
listings.head()
listings.to_csv('SpursvsWarriors 2018-3-19.csv')

Multiple events

To download listings for multiple events use get_listings_by_event function that takes the events parameter which is a pandas dataframe with event name and id in the following format.

events = pd.read_csv('flyers events 2018.csv')
events

Let's get listings for the rest of Philadelphia Flyers home games in 2017-2018 season.

flyers = st.get_listings_by_event(events)

Event: New York Rangers 4/7/2018: : 5it [00:22,  4.52s/it]    

Done getting listings by event.

flyers.head()

pd.unique(flyers['Event'])

array(['Washington Capitals 3/18/2018', 'New York Rangers 3/22/2018',
       'Boston Bruins 4/1/2018', 'Carolina Hurricanes 4/5/2018',
       'New York Rangers 4/7/2018'], dtype=object)

Let's also look at the number of listings.

Below is a good visual to see how spread out the prices by event are.

Reference

get_listings(eventid, pages=False) - Get listings using Stubhub API.

Parameters:

• eventid (int) - eventid taken from the Stubhub event url.
• pages (bool) - if True paginate to get all listings. If False get 200 listings.

get_listings_by_event(events) - Given the list of events and event ids retrieve all the listings for each event .

Parameters:

• events (pandas DataFrame) - a pandas dataframe of events and event ids taken from stubhub.

Resources

• https://developer.stubhub.com/docs/StubHub+API+Developers+Guide.html
• http://ozzieliu.com/2016/06/21/scraping-ticket-data-with-stubhub-api/
• https://stubhubapi.zendesk.com/hc/en-us/articles/220922687-Inventory-Search