Update app.py

app.py

@@ -68,21 +68,31 @@ import requests
 import pandas as pd
 
 # # Loop over the counter and format the API call
-r = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-01&endDate=2024-06-01')
-schedule = r.json()
+# r = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-01&endDate=2024-06-01')
+# schedule = r.json()
 
-def flatten(t):
-    return [item for sublist in t for item in sublist]
+# 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_date = flatten([[x['gameDate'] 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']))])
+# game_id = flatten([[x['gamePk'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+# game_date = flatten([[x['gameDate'] 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_date' : game_date, 'game_home' : game_home, 'game_away' : game_away})
-schedule_df.game_date = pd.to_datetime(schedule_df['game_date']).dt.tz_convert(tz='US/Eastern').dt.date
-schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens')
-schedule_df.head()
+# schedule_df = pd.DataFrame(data={'game_id': game_id, 'game_date' : game_date, 'game_home' : game_home, 'game_away' : game_away})
+# schedule_df.game_date = pd.to_datetime(schedule_df['game_date']).dt.tz_convert(tz='US/Eastern').dt.date
+# schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens')
+# schedule_df.head()
+
+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['away_sym'] = '@'
+schedule_df['home_sym'] = 'vs'
 
 team_abv = pd.read_csv('team_abv.csv')
 yahoo_weeks = pd.read_csv('yahoo_weeks.csv')
@@ -105,10 +115,11 @@ cmap_off = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#
 cmap_back = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#56B4E9"])
 cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
 
-schedule_df = schedule_df.merge(right=team_abv,left_on='game_away',right_on='team_name',how='inner',suffixes=['','_away']) 
-schedule_df = schedule_df.merge(right=team_abv,left_on='game_home',right_on='team_name',how='inner',suffixes=['','_home']) 
-schedule_df['away_sym'] = '@'
-schedule_df['home_sym'] = 'vs'
+# schedule_df = schedule_df.merge(right=team_abv,left_on='game_away',right_on='team_name',how='inner',suffixes=['','_away']) 
+# schedule_df = schedule_df.merge(right=team_abv,left_on='game_home',right_on='team_name',how='inner',suffixes=['','_home']) 
+# schedule_df['away_sym'] = '@'
+# schedule_df['home_sym'] = 'vs'
+
 
 
 #if not os.path.isfile('standings/standings_'+str(date.today())+'.csv'):   
@@ -126,7 +137,8 @@ standings_df.Team = standings_df.Team.str.strip('*')
 standings_df = standings_df.merge(right=team_abv,left_on='Team',right_on='team_name')
 
 schedule_stack = pd.DataFrame()
-schedule_stack['date'] = pd.to_datetime(list(schedule_df['game_date'])+list(schedule_df['game_date']))
+# schedule_stack['date'] = pd.to_datetime(list(schedule_df['game_date'])+list(schedule_df['game_date']))
+schedule_stack['date'] = pd.to_datetime(list(schedule_df['Date'])+list(schedule_df['Date']))
 schedule_stack['team'] = list(schedule_df['team_name'])+list(schedule_df['team_name_home'])
 schedule_stack['team_abv'] = list(schedule_df['team_abv'])+list(schedule_df['team_abv_home'])
 schedule_stack['symbol'] = list(schedule_df['away_sym'])+list(schedule_df['home_sym'])