Update app.py

app.py

@@ -31,24 +31,30 @@ from datetime import timedelta
 # r = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2022-10-01&endDate=2023-06-01')
 # schedule = r.json()
 
-schedule = json.loads(urlopen('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-07&endDate=2024-04-19').read())
-
-def flatten(t):
-    return [item for sublist in t for item in sublist]
-
-game_id = flatten([[x['gamePk'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
-game_type = flatten([[x['gameType'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
-game_date = flatten([[(pd.to_datetime(x['gameDate']) - timedelta(hours=8)) for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
-game_final = flatten([[x['status']['detailedState'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
-game_home = flatten([[x['teams']['home']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
-game_away = flatten([[x['teams']['away']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
-
-schedule_df = pd.DataFrame(data={'game_id': game_id, 'game_type':game_type,'game_date' : game_date, 'game_home' : game_home, 'game_away' : game_away,'status' : game_final})
-schedule_df = schedule_df[schedule_df.game_type == 'R'].reset_index(drop=True)
-schedule_df = schedule_df[schedule_df.status != 'Postponed']
-schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens')
-
-
+# schedule = json.loads(urlopen('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-07&endDate=2024-04-19').read())
+
+# def flatten(t):
+#     return [item for sublist in t for item in sublist]
+
+# game_id = flatten([[x['gamePk'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+# game_type = flatten([[x['gameType'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+# game_date = flatten([[(pd.to_datetime(x['gameDate']) - timedelta(hours=8)) for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+# game_final = flatten([[x['status']['detailedState'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+# game_home = flatten([[x['teams']['home']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+# game_away = flatten([[x['teams']['away']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+
+# schedule_df = pd.DataFrame(data={'game_id': game_id, 'game_type':game_type,'game_date' : game_date, 'game_home' : game_home, 'game_away' : game_away,'status' : game_final})
+# schedule_df = schedule_df[schedule_df.game_type == 'R'].reset_index(drop=True)
+# schedule_df = schedule_df[schedule_df.status != 'Postponed']
+# schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens')
+
+schedule = pd.read_html('https://www.hockey-reference.com/leagues/NHL_2024_games.html')[0]
+    #schedule.to_csv('schedule/schedule_'+str(date.today())+'.csv')
+#schedule = pd.read_csv('schedule/schedule_'+str(date.today())+'.csv')
+schedule = schedule.replace('St Louis Blues','St. Louis Blues')
+
+schedule_df = schedule.merge(right=team_abv,left_on='Visitor',right_on='team_name',how='inner',suffixes=['','_away']) 
+schedule_df = schedule_df.merge(right=team_abv,left_on='Home',right_on='team_name',how='inner',suffixes=['','_home']) 
 
 schedule_df_merge = schedule_df.merge(right=team_abv,left_on='game_home',right_on='team_name',how='left')
 schedule_df_merge = schedule_df_merge.merge(right=team_abv,left_on='game_away',right_on='team_name',how='left')