Данные Under и Over выглядят так, как будто они есть. Он находится внутри клавиши odds и определяется betType. betType = 18 равно Under, а betType = 19 равно Over.
import pandas as pd
import json
from pandas.io.json import json_normalize
import requests
import numpy as np
headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36'}
# Get Game IDs
url = 'https://betbuilder.digitalsportstech.com/api/latestGames'
payload = {
'leagueId': '123',
'order': 'asc',
'sb': 'betonline',
'sort': 'date',
'status': 'in,1,2,3',
'tz': '0'}
jsonData = requests.get(url, headers=headers, params=payload).json()
gameIds = []
for game in jsonData['data']:
gameIds.append(str(game['id']))
gameIds = ','.join(gameIds)
# Get Player Data
url = 'https://betbuilder.digitalsportstech.com/api/feed'
payload = {
'betType': 'in,18,19',
'gameId': 'in,%s' %gameIds,
'isActive': '1',
'limit': '9999',
'sb': 'betonline',
'tz': '-8'}
jsonData = requests.get(url, headers=headers, params=payload).json()
if len(jsonData['data']) == 0:
raise ValueError("O/U Markets for this league will be available as soon as possible\nCheck back soon")
def flatten_df(data):
finalOutput = pd.DataFrame()
try:
data = json_normalize(data)
print ('Parsing the data...')
except:
data=data
def unpack(temp_df, item):
try:
temp_df = json_normalize(item)
return temp_df
except:
pass
def iterrate(temp_df, row):
for col, item in row.iteritems():
if type(item) != list and type(item) != dict or col == 'statistic.phraseTitle':
if len([ x for x in temp_df.columns if col.startswith(x) ]) > 0:
col = col + '_%s' %(len([ x for x in temp_df.columns if col.startswith(x) ]))
try:
temp_df.loc[0,col] = item
except:
temp_df.loc[0,col] = ', '.join(item)
temp_df = temp_df.ffill()
elif type(item) == list or type(item) == dict:
temp_dfa = unpack(temp_df, item)
temp_dfa.columns = col + '_' + temp_dfa.columns
idxList = list(temp_df.index.values)
alpha = temp_dfa.copy()
merge_df = pd.DataFrame()
for idx in idxList:
alpha.index = [idx]*len(alpha)
merge_df = merge_df.append(alpha, sort=False)
temp_df = pd.merge(temp_df, merge_df, how='outer', left_index=True, right_index=True)
temp_df = temp_df.reset_index(drop=True)
return temp_df
for idx, row in data.iterrows():
temp_df = pd.DataFrame()
finalOutput = finalOutput.append(iterrate(temp_df, row),sort=False).reset_index(drop=True)
continueUnpacking = False
for idx, row in finalOutput.iterrows():
if len([ x for x, y in row.iteritems() if type(y) == list or type(y) == dict ]) > 0:
print ('Still untangling...')
continueUnpacking = True
break
if continueUnpacking == True:
finalOutput = flatten_df(finalOutput)
return finalOutput
results = flatten_df(jsonData['data'])
playersData = {}
for idx, row in results.iterrows():
if row['player1.name'] not in list(playersData.keys()):
playersData[row['player1.name']] = {}
if row['statistic.title'] not in list(playersData[row['player1.name']].keys()):
playersData[row['player1.name']][row['statistic.title']] = {'Line': row['markets_value']}
# Under
if row['betType'] == 18:
playersData[row['player1.name']][row['statistic.title']].update({'Under': row['markets_odds']})
# Over
if row['betType'] == 19:
playersData[row['player1.name']][row['statistic.title']].update({'Over': row['markets_odds']})
df = pd.DataFrame()
for player, v in playersData.items():
temp_df = pd.DataFrame(v).T.reset_index().rename(columns={'index':'statistic.title'})
temp_df['Name'] = player
temp_df = temp_df[['Name', 'statistic.title', 'Under', 'Line', 'Over']]
df = df.append(temp_df, sort=False).reset_index(drop=True)
Вывод:
Вот сайт и соответствующие выходные данные:
print (df)
Name statistic.title Under Line Over
0 Devonte' Graham Pts + Reb + Ast 1.90 28.0 1.86
1 Devonte' Graham Three Point Field Goals Made 1.67 2.5 2.15
2 Devonte' Graham Points 1.86 17.5 1.90
3 Devonte' Graham Assists 1.65 6.5 2.18
4 Miles Bridges Pts + Reb + Ast 1.88 24.0 1.88
5 Miles Bridges Three Point Field Goals Made 1.85 1.5 1.91
6 Miles Bridges Total Rebounds 1.88 6.0 1.88
7 Miles Bridges Points 1.81 15.5 1.96
8 Trae Young Assists 1.70 9.5 2.10
9 Trae Young Three Point Field Goals Made 2.15 3.5 1.67
10 Trae Young Pts + Reb + Ast 1.87 41.5 1.89
11 Trae Young Points 1.89 27.5 1.87
12 Kevin Huerter Points 1.92 13.5 1.84
13 Kevin Huerter Three Point Field Goals Made 2.04 2.5 1.74
14 Kevin Huerter Pts + Reb + Ast 1.85 21.5 1.91
15 Terry Rozier Points 1.95 19.5 1.81
16 Terry Rozier Pts + Reb + Ast 1.88 27.5 1.88
17 Terry Rozier Total Rebounds 2.24 4.5 1.62
18 Terry Rozier Three Point Field Goals Made 1.95 2.5 1.81
19 Cody Zeller Points 1.77 10.5 2.00
20 Cody Zeller Total Rebounds 1.74 6.5 2.05
21 Cody Zeller Pts + Reb + Ast 1.82 19.5 1.94
22 John Collins Total Rebounds 1.77 9.5 2.00
23 John Collins Three Point Field Goals Made 2.12 1.5 1.69
24 John Collins Pts + Reb + Ast 1.85 34.5 1.91
25 John Collins Points 1.86 22.5 1.90
26 P.J. Washington Three Point Field Goals Made 1.88 1.5 1.88
27 P.J. Washington Total Rebounds 1.91 5.5 1.85
28 P.J. Washington Pts + Reb + Ast 1.80 21.5 1.97
29 P.J. Washington Points 1.84 13.5 1.92