I am pulling in betting data from an API and I would like to get it into a DataFrame. I would like the DataFrame to have the following columns: [away_team, home_team, spread, overUnder] I am using the following code:
import cfbd
configuration = cfbd.Configuration()
configuration.api_key['Authorization'] = 'XXX'
configuration.api_key_prefix['Authorization'] = 'Bearer'
from __future__ import print_function
import time
import cfbd
from cfbd.rest import ApiException
from pprint import pprint
# create an instance of the API class
api_instance = cfbd.BettingApi(cfbd.ApiClient(configuration))
game_id = 56 # int | Game id filter (optional)
year = 56 # int | Year/season filter for games (optional)
week = 56 # int | Week filter (optional)
season_type = 'regular' # str | Season type filter (regular or postseason) (optional) (default to regular)
team = 'team_example' # str | Team (optional)
home = 'home_example' # str | Home team filter (optional)
away = 'away_example' # str | Away team filter (optional)
conference = 'conference_example' # str | Conference abbreviation filter (optional)
try:
# Betting lines
api_response = api_instance.get_lines(year=2021, week=7, season_type='regular', conference='SEC')
pprint(api_response)
except ApiException as e:
print("Exception when calling BettingApi->get_lines: %s\n" % e)
API Response:
[{'away_conference': 'SEC',
'away_score': None,
'away_team': 'Auburn',
'home_conference': 'SEC',
'home_score': None,
'home_team': 'Arkansas',
'id': 401282104,
'lines': [{'awayMoneyline': 155,
'formattedSpread': 'Arkansas -3.5',
'homeMoneyline': -180,
'overUnder': '53.5',
'overUnderOpen': '53.0',
'provider': 'Bovada',
'spread': '-3.5',
'spreadOpen': '-3.5'}],
'season': 2021,
'season_type': 'regular',
'start_date': None,
'week': 7},
{'away_conference': 'SEC',
'away_score': None,
'away_team': 'Kentucky',
'home_conference': 'SEC',
'home_score': None,
'home_team': 'Georgia',
'id': 401282105,
'lines': [{'awayMoneyline': 1000,
'formattedSpread': 'Georgia -23.5',
'homeMoneyline': -2200,
'overUnder': '44.5',
'overUnderOpen': '44.5',
'provider': 'Bovada',
'spread': '-23.5',
'spreadOpen': '-23.5'}],
'season': 2021,
'season_type': 'regular',
'start_date': None,
'week': 7},
{'away_conference': 'SEC',
'away_score': None,
'away_team': 'Florida',
'home_conference': 'SEC',
'home_score': None,
'home_team': 'LSU',
'id': 401282106,
'lines': [{'awayMoneyline': -370,
'formattedSpread': 'Florida -10.0',
'homeMoneyline': 285,
'overUnder': '58.5',
'overUnderOpen': '58.0',
'provider': 'Bovada',
'spread': '10.0',
'spreadOpen': '10.0'}],
'season': 2021,
'season_type': 'regular',
'start_date': None,
'week': 7},
{'away_conference': 'SEC',
'away_score': None,
'away_team': 'Alabama',
'home_conference': 'SEC',
'home_score': None,
'home_team': 'Mississippi State',
'id': 401282107,
'lines': [{'awayMoneyline': -950,
'formattedSpread': 'Alabama -17.5',
'homeMoneyline': 600,
'overUnder': '57.5',
'overUnderOpen': '59.0',
'provider': 'Bovada',
'spread': '17.5',
'spreadOpen': '17.0'}],
'season': 2021,
'season_type': 'regular',
'start_date': None,
'week': 7},
{'away_conference': 'SEC',
'away_score': None,
'away_team': 'Texas A&M',
'home_conference': 'SEC',
'home_score': None,
'home_team': 'Missouri',
'id': 401282108,
'lines': [{'awayMoneyline': -310,
'formattedSpread': 'Texas A&M -9.0',
'homeMoneyline': 255,
'overUnder': '60.5',
'overUnderOpen': '61.0',
'provider': 'Bovada',
'spread': '9.0',
'spreadOpen': '9.0'}],
'season': 2021,
'season_type': 'regular',
'start_date': None,
'week': 7},
{'away_conference': 'SEC',
'away_score': None,
'away_team': 'Vanderbilt',
'home_conference': 'SEC',
'home_score': None,
'home_team': 'South Carolina',
'id': 401282109,
'lines': [{'awayMoneyline': 750,
'formattedSpread': 'South Carolina -18.5',
'homeMoneyline': -1400,
'overUnder': '51.0',
'overUnderOpen': '51.0',
'provider': 'Bovada',
'spread': '-18.5',
'spreadOpen': '-20.0'}],
'season': 2021,
'season_type': 'regular',
'start_date': None,
'week': 7},
{'away_conference': 'SEC',
'away_score': None,
'away_team': 'Ole Miss',
'home_conference': 'SEC',
'home_score': None,
'home_team': 'Tennessee',
'id': 401282110,
'lines': [{'awayMoneyline': -150,
'formattedSpread': 'Ole Miss -3.0',
'homeMoneyline': 130,
'overUnder': '80.5',
'overUnderOpen': '78.0',
'provider': 'Bovada',
'spread': '3.0',
'spreadOpen': '3.0'}],
'season': 2021,
'season_type': 'regular',
'start_date': None,
'week': 7}]
I need help getting this output into a DataFrame. Thank you in advance.
CodePudding user response：
You could iterate over the json data, extracting the information that you need and creating a new structure to hold this data. After iterating over all your data, you can create a dataframe from what you extracted. I made an example using a dataclass to store the data you need:
import json
import pandas as pd
from dataclasses import dataclass
@dataclass
class BettingData:
away_team: str
home_team: str
spread: str
overUnder: str
json_data = json.loads(open('sample_data.json', 'r').read())
content = []
for entry in json_data:
for line in entry['lines']:
data = BettingData(away_team=entry['away_team'],
home_team=entry['home_team'],
spread=line['spread'],
overUnder=line['overUnder'])
content.append(data)
df = pd.DataFrame(content)
print(df)
And the output is:
away_team home_team spread overUnder
0 Auburn Arkansas -3.5 53.5
1 Kentucky Georgia -23.5 44.5
2 Florida LSU 10.0 58.5
3 Alabama Mississippi State 17.5 57.5
4 Texas A&M Missouri 9.0 60.5
5 Vanderbilt South Carolina -18.5 51.0
6 Ole Miss Tennessee 3.0 80.5