def mybookie_nhl_extractor(page):
tp = TableParser()
tp.feed(page)
tables = tp.get_tables()
# Get rid of garbage lines in the table
tables = tables[0][1:]
# Find team names and moneylines
pairs = []
for i in range(len(tables)/2):
name1 = tables[i*2][2].strip().split(" ")
name1 = name1[0] if len(name1) == 1 else " ".join(name1[1:])
name2 = tables[i*2+1][1].strip().split(" ")
name2 = name2[0] if len(name2) == 1 else " ".join(name2[1:])
moneyline1 = str(tables[i*2][-1]).strip()
moneyline2 = str(tables[i*2+1][-1]).strip()
if moneyline1 == '0' or moneyline2 == '0':
continue
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
def bodog_nhl_extractor(page):
tp = TableParser()
tp.feed(strip(page))
tables = tp.get_tables()
# Get rid of garbage rows
rows = [r for t in tables for r in t if len(r) > 3][1:]
# Find team names and moneylines
pairs = []
for i in range(len(rows)/2):
name1 = rows[i*2][2].strip()
name2 = rows[i*2+1][1].strip()
moneyline1 = rows[i*2][4].strip()
moneyline2 = rows[i*2+1][3].strip()
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
def betdsi_nhl_extractor(page):
tp = TableParser()
tp.feed(strip(page))
tables = tp.get_tables()
# Get rid of garbage rows
tables = [t for t in tables if len(t) == 2]
# Find team names and moneylines
pairs = []
for table in tables:
name1 = table[0][1].strip()
name2 = table[1][0]
moneyline1 = table[0][-1].strip()
moneyline2 = table[1][-1].strip()
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
def topbet_nhl_extractor(page):
tp = TableParser()
tp.feed(page)
tables = tp.get_tables()
# Get rid of garbage tables
tables = [t for t in tables if len(t) == 3 and t[0][0] == t[0][1] == 0]
# Find team names and moneylines
pairs = []
for table in tables:
name1 = table[1][1].strip()
name2 = table[2][1].strip()
moneyline1 = table[1][5].strip()
moneyline2 = table[2][5].strip()
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
def betdsi_nhl_extractor(page):
tp = TableParser()
tp.feed(strip(page))
tables = tp.get_tables()
# Get rid of garbage rows
tables = [t for t in tables if len(t) == 2]
# Find team names and moneylines
pairs = []
for table in tables:
name1 = table[0][1].strip()
name2 = table[1][0]
moneyline1 = table[0][-1].strip()
moneyline2 = table[1][-1].strip()
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
def sportsbetting_nhl_extractor(page):
tp = TableParser()
tp.feed(page)
tables = tp.get_tables()
# Clean up tables
tables = tables[3][2:]
tables = [r for r in tables if len(r) > 20]
# Extract names/lines
pairs = []
for i in range(len(tables) / 2):
name1 = tables[i * 2][2].strip()
name2 = tables[i * 2 + 1][1].strip()
moneyline1 = str(tables[i * 2][9]).strip()
moneyline2 = str(tables[i * 2 + 1][8]).strip()
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
def sportsbetting_nhl_extractor(page):
tp = TableParser()
tp.feed(page)
tables = tp.get_tables()
# Clean up tables
tables = tables[3][2:]
tables = [r for r in tables if len(r) > 20]
# Extract names/lines
pairs = []
for i in range(len(tables)/2):
name1 = tables[i*2][2].strip()
name2 = tables[i*2+1][1].strip()
moneyline1 = str(tables[i*2][9]).strip()
moneyline2 = str(tables[i*2+1][8]).strip()
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
def bovada_nhl_extractor(page):
tp = TableParser()
tp.feed(strip(page))
tables = tp.get_tables()
# Get rid of garbage lines in the table
tables = tables[1:]
for i, t in enumerate(tables):
tables[i] = max(t, key=lambda x: len(x))
# Find the team names and moneylines
pairs = []
for i in range(len(tables) / 2):
name1 = tables[i * 2][2].strip()
name2 = tables[i * 2 + 1][1].strip()
moneyline1 = tables[i * 2][4].strip()
moneyline2 = tables[i * 2 + 1][3].strip()
pairs.append(((name1, moneyline1), (name2, moneyline2)))
return pairs
