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 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