Update app.py

app.py

@@ -6,7 +6,6 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 from matplotlib.pyplot import figure
 from matplotlib.offsetbox import OffsetImage, AnnotationBbox
-from scipy import stats
 import matplotlib.lines as mlines
 import matplotlib.transforms as mtransforms
 import numpy as np
@@ -31,13 +30,7 @@ plt.style.use('fivethirtyeight')
 sns.set_context("notebook")
 import warnings
 warnings.filterwarnings('ignore')
-# import yfpy
-# from yfpy.query import YahooFantasySportsQuery
-# import yahoo_oauth
-import json
-#import openpyxl
-# from sklearn import preprocessing
-from datetime import timedelta
+
 # import dataframe_image as dfi
 # from google.colab import drive
 def percentile(n):
@@ -62,31 +55,49 @@ import math
 # import matplotlib.colors as mcolors
 # from  matplotlib.ticker import FuncFormatter
 # from matplotlib.font_manager import FontProperties
-
+import matplotlib.ticker as mtick
 import numpy as np
 # import matplotlib.pyplot as plt
 import matplotlib.colors
 
-# import undetected_chromedriver as uc
-# from selenium import webdriver
-# from seleniumbase import Driver
-
-# driver = Driver(uc=True)
-# driver.get('https://www.naturalstattrick.com')
-
-#x,y,c = zip(*np.random.rand(30,3)*4-2)
-
-#norm=plt.Normalize(-2,2)
-co = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#ffffff","#F0E442"])
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+from matplotlib import cm
+from datetime import date
+
+def show_values(axs, orient="v", space=-1,stat = [],rank_n=[]):
+    def _single(ax):
+        if orient == "v":
+            i = 0
+            for p in ax.patches:
+
+                _x = p.get_x() + p.get_width() / 2
+                _y = p.get_y() + p.get_height() + (p.get_height()*0.01)
+                value = '{:.0f}'.format(p.get_height())
+                if isinstance(stat[i], float):
+                    value_stat = '{:.2f}'.format(stat[i])
+                else:
+                    value_stat = stat[i]
+                rank = rank_n[i]
+                ax.text(_x, -3.8, value_stat, ha="center",fontstyle='italic')    
+                ax.text(_x, -4.2, rank, ha="center",fontstyle='italic')  
+                i = i+1
+        elif orient == "h":
+            for p in ax.patches:
+                _x = p.get_x() + p.get_width() + float(space)
+                _y = p.get_y() + p.get_height() - (p.get_height()*0.5)
+                value = '{:.0f}'.format(p.get_width())
+                ax.text(_x, _y, value, ha="left")
+
+    if isinstance(axs, np.ndarray):
+        for idx, ax in np.ndenumerate(axs):
+            _single(ax)
+    else:
+        _single(axs)
 
 
-try: 
-    data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/427.l.public;out=settings/players;position=ALL;start=0;count=3000;sort=average_pick;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
-    key_check = data_r['fantasy_content']['league']['players']
 
-except KeyError:
-    data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/427.l.public;out=settings/players;position=ALL;start=0;count=1151;sort=average_pick;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
-    print('key_checked')
+data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/427.l.public;out=settings/players;position=ALL;start=0;count=1000;sort=rank_season;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
 
 total_list = []
 
@@ -108,95 +119,33 @@ for x in data_r['fantasy_content']['league']['players']:
         single_list.append(0)
     total_list.append(single_list)
 
+df_2023 = pd.DataFrame(data=total_list,columns=['player_id','rank_value','full','first','last','average_pick', 'average_cost','display_position','editorial_team_abbr','percent_owned'])
 
-yahoo_df = pd.DataFrame(total_list,columns = ['player_id','rank_value','full','first','last','average_pick','average_auction_cost','display_position','editorial_team_abbr','percent_owned'])
-yahoo_df_2 = yahoo_df.copy()
-
-# # Write your code here.
-# response = requests.get("https://www.naturalstattrick.com/playerlist.php?fromseason=20232024&thruseason=20232024&stype=2&sit=all&stdoi=oi&rate=n")
-# soup = BeautifulSoup(response.text, 'html.parser')
-# table_rows = soup.findAll('tr')
-# table_rows = table_rows[1:-1]
-# table_rows[0].findAll('td')
-
-# player_name = []
-# player_position = []
-# player_team = []
-# player_id = []
-
-# for i in range(0,len(table_rows)-1):
-#     player_name.append(str(table_rows[i].findAll('td')[0].contents[0]))
-#     player_position.append(table_rows[i].findAll('td')[1].contents[0])
-#     player_team.append(table_rows[i].findAll('td')[2].contents[0])
-#     player_id.append(str(table_rows[i].findAll('td')[3].contents[0])[-76:][:7])
-
-# player_id_df = pd.DataFrame({'Player':player_name,'Player ID':player_id,'Position':player_position,'Team':player_team})
-# #player_id_df.index.name = 'Player Name'
-# player_id_df.head()
-
-# skater_df = player_id_df[player_id_df['Position'] != 'G']
-# goalie_df = player_id_df[player_id_df['Position'] == 'G']
+df_2023['pos_new'] = ['D' if "D" in x else 'F' for x in df_2023['display_position']]
 
-season = 20232024
-seasontype = 2
+player_games = pd.read_csv('Drive/player_games_cards.csv',index_col=[0]).sort_values(by='date')
+summary_2023 = pd.read_csv('Drive/summary_2024.csv',index_col=[0])
+summary_2022 = pd.read_csv('Drive/2022-23/summary_2023.csv',index_col=[0])
+team_games = pd.read_csv('Drive/team_games.csv',index_col=[0])
+nhl_logos = pd.read_csv("NHL Logos - NHL Logos.csv")
+team_games = team_games.merge(right=nhl_logos[['Team Name','Team']],left_on=['team'],right_on=['Team Name'],how='left')
 
-def nat_stat_trick_range_pp_gp(rookie='n',start_date='2022-10-01',end_date=str(pd.to_datetime(datetime.now(pytz.timezone('US/Pacific')).strftime('%Y-%m-%d')).date()),sit='all',gp=1):
-    time.sleep(2)
-    url = f'https://www.naturalstattrick.com/playerteams.php?fromseason={season}&thruseason={season}&stype={seasontype}&sit=pp&score=all&stdoi=std&rate=y&team=ALL&pos=S&loc=B&toi=0&gpfilt=gpteam&fd=&td=&tgp='+str(gp)+'&lines=single&draftteam=ALL'
+yahoo_to_nhl_df = pd.read_csv('yahoo_to_nhl.csv')
 
-    player_list_all = []
-    response = requests.get(url)
-    soup = BeautifulSoup(response.text, 'html.parser')
-    table_rows = soup.findAll('tr')
-    table_rows = table_rows[1:]
+yahoo_to_nhl_df = yahoo_to_nhl_df.merge(df_2023)
+summary_2023 = summary_2023.merge(yahoo_to_nhl_df,left_on='player_id',right_on='nhl_id',suffixes=['','_yahoo'],how='left')
 
-    for j in range(0,len(table_rows)):
-      p_string = [str(x).strip('<td>').strip('</') for x  in list(table_rows[j].findAll('td')) if "<td>" in str(x)]
-      player_list_all.append([p_string[0]]+[str(table_rows[j].findAll('td')[1]).split('>')[2].split('<')[0]]+p_string[1:]+[str(table_rows[j].findAll('td')[1])[98:105].strip('</a></td>')])
-    #table_rows[0].findAll('td')
+summary_2023 = summary_2023.merge(right=player_games.drop_duplicates(subset=['player_id'],keep='last')[['player_id','Position','Team']],left_on=['player_id'],right_on=['player_id'],how='left')
 
-    if soup != "":
-      columns_list = [str(x).split('>')[1].split('<')[0] for x in soup.findAll('th')]+['player_id']
-      df_url = pd.DataFrame(data=player_list_all,columns=columns_list)
+summary_2023.loc[summary_2023.display_position.isna(),'display_position'] = summary_2023.loc[summary_2023.display_position.isna(),'Position']
+summary_2023.display_position = summary_2023.display_position.replace({'L':'LW','R':'RW'})
+summary_2023.percent_owned = summary_2023.percent_owned.fillna(0)
 
-    df_url = df_url.fillna(0)
-    df_url['Shots+Hits+Blocks/60'] = df_url['Shots/60'].astype(float)+df_url['Hits/60'].astype(float)+df_url['Shots Blocked/60'].astype(float)
-    df_url['Shots+Hits/60'] = df_url['Shots/60'].astype(float)+df_url['Hits/60'].astype(float)
-    #print(url)
-    return df_url
-
-team_abv = pd.read_csv('team_abv.csv')
-team_dict = team_abv.set_index('team_abv').to_dict()['team_name']
-
-yahoo_nhl_df = pd.read_csv('yahoo_to_nhl.csv', encoding='unicode_escape')
-
-
-player_games_df = pd.read_csv('player_games_cards.csv',index_col=[0])
-team_games_df = pd.read_csv('team_games.csv',index_col=[0])
-
-team_games_df['game_count'] = team_games_df.groupby('team')['team'].cumcount()+1
-team_games_df['max_games'] = team_games_df.groupby('team').game_count.transform('max')
-team_games_df['abv'] = team_games_df.team.map(team_abv.set_index('team_name')['team_abv'].to_dict())
-
-team_games_df = team_games_df.sort_values(by='game_count',ascending=False)
-
-#team_abv = pd.read_csv('team_abv.csv')
-
-
-
-def nat_stat_convert(df):
-    for i in range(0,len(df.columns)):
-        if df.columns[i][-3:]=='/60':
-            if 'ix' not in df.columns[i]:
-                df[df.columns[i]] = np.round(df[df.columns[i]].astype(float)*df['TOI'].astype(float)/60,0)
-                df = df.rename(columns={df.columns[i]: df.columns[i].replace('/60','')})
-            else:
-                df[df.columns[i]] = df[df.columns[i]].astype(float)*df['TOI'].astype(float)/60
-                df = df.rename(columns={df.columns[i]: df.columns[i].replace('/60','')})
+player_games['game'] = player_games.groupby(['player_id']).cumcount() + 1 
 
-    df['Faceoffs %'] = df['Faceoffs Won']/(df['Faceoffs Won']+df['Faceoffs Lost'])
-
-    return df
+skater_dict = summary_2023.set_index('player_id').sort_values(by='Player')
+skater_dict['skater_team'] = skater_dict.Player + ' - ' + skater_dict.Team
+skater_dict = skater_dict['skater_team'].to_dict()
 
 from shiny import ui, render, App
 import matplotlib.image as mpimg
@@ -207,24 +156,12 @@ app_ui = ui.page_fluid(
 
       ui.panel_sidebar(
         #ui.input_date_range("date_range_id", "Date range input",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),
-        ui.input_select("team_id", "Select Team",team_dict,width=1,size=1,selected='ANA',),
-        ui.input_numeric("n_1", "Last Games x", value=1),
-        ui.input_numeric("n_2", "Last Games y", value=0),
-        ui.input_numeric("n_3", "Last Games z", value=0),
-        ui.input_numeric("top_n", "Show top 'n'", value=10),
-        ui.input_switch("x", "Drop N/A"),
+        ui.input_select("id", "Select Skater",skater_dict,width=1),
+        ui.input_numeric("last_game_id", "Select Last 'n' Games",value=1,width=1),
         #ui.input_select("ignore_id", "Remove Columns",['Position','Roster%'],multiple=True,selectize=True),
-      ),
-
-        ui.panel_main(ui.tags.h3(""),
-                      ui.div({"style": "font-size:2.7em;"},ui.output_text("txt_title")),
-                      #ui.tags.h2("Fantasy Hockey Schedule Summary"),
-                      ui.tags.h5("Created By: @TJStats,     Data: Natural Stat Trick, Yahoo Fantasy"),
-                      ui.div({"style": "font-size:1.6em;"},ui.output_text("txt")),
-                      ui.output_table("pp_roundup"),
-                      #ui.tags.h5('Legend'),
-                     #ui.tags.h6('An Off Night is defined as a day in which less than half the teams in the NHL are playing'),
-                      #ui.tags.h6('The scores are determined by using games played, off-nights, B2B, and strength of opponents') )
+      width=2),
+      ui.panel_main(
+        ui.output_plot("plot",height = "1350px",width="2400px")
         )
     ),
 )
@@ -243,199 +180,763 @@ from shiny.ui import h2, tags
 #print(app_ui)
 def server(input, output, session):
 
-
-    @output
-    @render.text
-    def txt():
-        return   f'{team_dict[input.team_id()]} Last Games PP Summary'
-      
     @output
-    @render.text
-    def txt_title():
-    
-        return f'Team Last Games PP% Leaders'
+    @render.plot(alt="A histogram")
+    def plot():
+        # from matplotlib import rc, font_manager
+        # rc('text', usetex=True)
+        # rc('font',**{'family':'sans-serif','sans-serif':['Century Gothic']})
+        #fig.set_facecolor('white')
+        sns.set_theme(style="whitegrid", palette="pastel")
+        cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])
 
+        player_id = int(input.id())
 
+        last_games = input.last_game_id()
 
 
 
-    @output
-    @render.table
-    def pp_roundup():
+        player_games_one = player_games[player_games['player_id']==player_id].sort_values(by='game',ascending=False)
+        
+        print(player_games_one)
+        summary_2023_one = summary_2023[summary_2023['player_id']==player_id]
+        summary_2022_one = summary_2022[summary_2022['player_id']==player_id]
+
+        last_games = min(last_games,summary_2023_one.GP.max())
+
+        name = player_games_one.Player.values[0]
+
+        df_last_games = pd.DataFrame(data={'Games':[last_games]})
+        df_last_games['TOI'] = player_games_one['TOI'][:last_games].sum()
+        df_last_games['TOI/G'] = player_games_one['TOI'][:last_games].sum()/df_last_games.Games
+        df_last_games['PP TOI/G'] = player_games_one['TOI_pp'][:last_games].sum()/df_last_games.Games
+        df_last_games['Points/60'] = player_games_one['Total Points'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['Shots/60'] = player_games_one['Shots'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['Hits/60'] = player_games_one['Hits'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['Blocks/60'] = player_games_one['Shots Blocked'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['Goals/60'] = player_games_one['Goals'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['ixG/60'] = player_games_one['ixG'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['G-ixG/60'] = df_last_games['Goals/60'] - df_last_games['ixG/60'] 
+        df_last_games['iCF/60'] = player_games_one['iCF'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['iSCF/60'] = player_games_one['iSCF'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['iHDCF/60'] = player_games_one['iHDCF'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['GF/60'] = player_games_one['GF'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['xGF/60'] = player_games_one['xGF'][:last_games].sum()/df_last_games.TOI*60
+        df_last_games['IPP'] = player_games_one['Total Points'][:last_games].sum()/player_games_one['GF'][:last_games].sum()
+        df_last_games['S%'] = player_games_one['Goals'][:last_games].sum()/player_games_one['Shots'][:last_games].sum()
+        df_last_games['xS%'] = player_games_one['ixG'][:last_games].sum()/player_games_one['Shots'][:last_games].sum()
+        df_last_games['1st Assist%'] = player_games_one['First Assists'][:last_games].sum()/player_games_one['Total Assists'][:last_games].sum()
+        df_last_games['Off. Zone Start%'] = player_games_one['Off.\xa0Zone Starts'][:last_games].sum()/(player_games_one['Off.\xa0Zone Starts'][:last_games].sum()+player_games_one['Def.\xa0Zone Starts'][:last_games].sum())
+        df_last_games['oiSH%'] = player_games_one['GF'][:last_games].sum()/player_games_one['SF'][:last_games].sum()
+        df_season = pd.DataFrame(data={'Games':[len(player_games_one)]})
+        df_season['TOI'] = player_games_one['TOI'][:len(player_games_one)].sum()
+        df_season['TOI/G'] = player_games_one['TOI'][:len(player_games_one)].sum()/df_season.Games
+        df_season['PP TOI/G'] = player_games_one['TOI_pp'][:len(player_games_one)].sum()/df_season.Games
+        df_season['Points/60'] = player_games_one['Total Points'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['Shots/60'] = player_games_one['Shots'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['Hits/60'] = player_games_one['Hits'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['Blocks/60'] = player_games_one['Shots Blocked'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['Goals/60'] = player_games_one['Goals'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['ixG/60'] = player_games_one['ixG'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['G-ixG/60'] = df_season['Goals/60'] - df_season['ixG/60'] 
+        df_season['iCF/60'] = player_games_one['iCF'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['iSCF/60'] = player_games_one['iSCF'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['iHDCF/60'] = player_games_one['iHDCF'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['GF/60'] = player_games_one['GF'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['xGF/60'] = player_games_one['xGF'][:len(player_games_one)].sum()/df_season.TOI*60
+        df_season['IPP'] = player_games_one['Total Points'][:len(player_games_one)].sum()/player_games_one['GF'][:len(player_games_one)].sum()
+        df_season['S%'] = player_games_one['Goals'][:len(player_games_one)].sum()/player_games_one['Shots'][:len(player_games_one)].sum()
+        df_season['xS%'] = player_games_one['ixG'][:len(player_games_one)].sum()/player_games_one['Shots'][:len(player_games_one)].sum()
+        df_season['1st Assist%'] = player_games_one['First Assists'][:len(player_games_one)].sum()/player_games_one['Total Assists'][:len(player_games_one)].sum()
+        df_season['Off. Zone Start%'] = player_games_one['Off.\xa0Zone Starts'][:len(player_games_one)].sum()/(player_games_one['Off.\xa0Zone Starts'][:len(player_games_one)].sum()+player_games_one['Def.\xa0Zone Starts'][:len(player_games_one)].sum())
+        df_season['oiSH%'] = player_games_one['GF'][:len(player_games_one)].sum()/player_games_one['SF'][:len(player_games_one)].sum()
+        df_last_games.rename(index={0:'Last '+str(df_last_games.Games[0])+' GP'},inplace=True)
+        df_season.rename(index={0:'2023-24 ('+str(df_season.Games[0])+'GP)'},inplace=True)
+
+        # cols_to_use = summary_all.columns.difference(summary_all_on_ice.columns)
+
+
+        # df_career_total = summary_all.merge(summary_all_on_ice, left_index=True, right_index=True,
+        #                  how='outer', suffixes=('', '_y'))
+
+        # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)
+
+        # df_career_total = df_career_total.merge(summary_pp, left_index=True, right_index=True,
+        #                  how='outer', suffixes=('', '_y'))
+
+        # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)
+
+        # df_career_total = df_career_total.merge(summary_pp_on_ice, left_index=True, right_index=True,
+        #                  how='outer', suffixes=('', '_y'))
+
+        # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)
+        df_career_total = summary_2022_one.sort_index(ascending=False)
+        df_career_total.columns = [c.replace(u"\xa0", u" ") for c in list(df_career_total.columns)]
+        df_career = pd.DataFrame(data={'Games':[df_career_total.GP.sum()]})
+        df_career['TOI'] = df_career_total['TOI'].sum()
+        df_career['TOI/G'] = df_career_total['TOI'].sum()/df_career.Games
+        df_career['PP TOI/G'] = df_career_total['TOI_pp'].sum()/df_career.Games
+        df_career['Points/60'] = df_career_total['Total_Points'].sum()/df_career_total.TOI.sum()*60
+        df_career['Shots/60'] = df_career_total['Shots'].sum()/df_career_total.TOI.sum()*60
+        df_career['Hits/60'] = df_career_total['Hits'].sum()/df_career_total.TOI.sum()*60
+        df_career['Blocks/60'] = df_career_total['Shots_Blocked'].sum()/df_career_total.TOI.sum()*60
+        df_career['Goals/60'] = df_career_total['Goals'].sum()/df_career_total.TOI.sum()*60
+        df_career['ixG/60'] = df_career_total['ixG'].sum()/df_career_total.TOI.sum()*60
+        df_career['G-ixG/60'] = df_career['Goals/60'] - df_career['ixG/60'] 
+        df_career['iCF/60'] = df_career_total['iCF'].sum()/df_career_total.TOI.sum()*60
+        df_career['iSCF/60'] = df_career_total['iSCF'].sum()/df_career_total.TOI.sum()*60
+        df_career['iHDCF/60'] = df_career_total['iHDCF'].sum()/df_career_total.TOI.sum()*60
+        df_career['GF/60'] = df_career_total['GF'].sum()/df_career_total.TOI.sum()*60
+        df_career['xGF/60'] = df_career_total['xGF'].sum()/df_career_total.TOI.sum()*60
+        df_career['IPP'] = df_career_total['Total_Points'].sum()/df_career_total['GF'].sum()
+        df_career['S%'] = df_career_total['Goals'].sum()/df_career_total['Shots'].sum()
+        df_career['xS%'] = df_career_total['ixG'].sum()/df_career_total['Shots'].sum()
+        df_career['1st Assist%'] = df_career_total['First_Assists'].sum()/df_career_total['Assists'].sum()
+        df_career['Off. Zone Start%'] = df_career_total['OZ Start%'].sum()
+        df_career['oiSH%'] = df_career_total['GF'].sum()/df_career_total['SF'].sum()
+        df_career.rename(index={0:'2022-23 ('+str(df_career.Games[0])+'GP)'},inplace=True)
+        df_combined = df_last_games.append([df_season,df_career])
+        df_combined_t = round(df_combined,3).transpose()
+        df_combined.style.format(formatter={"IPP": "{:.1%}","S%": "{:.1%}","1st Assist%": "{:.1%}","Off. Zone Start%": "{:.1%}","oiSH%": "{:.1%}"}).set_precision(2)
+        rows = [idx for idx in df_combined_t.index if '/' not in idx]
+
+
+        df_combined_t = df_combined_t.astype(float).fillna(0)
+        # df_combined_t_style = df_combined_t.style.set_table_styles([{
+        #     'selector': 'caption',
+        #     'props': [
+        #         ('color', ''),
+        #         ('fontname', 'Century Gothic'),
+        #         ('font-size', '12px'),
+        #         ('font-style', 'italic'),
+        #         ('font-weight', ''),
+        #         ('text-align', 'centre'),
+        #         ]
+
+        #         },{'selector' :'th', 'props':[('text-align', 'center'),('Height','8px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '10px')]}],overwrite=False).set_properties(
+        #             **{'background-color':'White','index':'White','min-width':'60px'},overwrite=False).set_table_styles(
+        #     [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+        #     [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+        #     [{'selector': 'tr', 'props': [('line-height', '18px')]}],overwrite=False).set_properties(
+        #     **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).apply(
+
+        #     lambda x: ["background: #FEEFC1" if (i < 1 and v > x.iloc[1]*1.05)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #FEEFC1" if (i == 1 and v > x.iloc[2]*1.05)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #D0E1FD" if (i < 1 and v < x.iloc[1]*0.95)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #D0E1FD" if (i == 1 and v < x.iloc[2]*0.95)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #FDDE82" if (i < 1 and v > x.iloc[1]*1.1)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #FDDE82" if (i == 1 and v > x.iloc[2]*1.1)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #A1C2FA" if (i < 1 and v < x.iloc[1]*0.9)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #A1C2FA" if (i == 1 and v < x.iloc[2]*0.9)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #FCCD43" if (i < 1 and v > x.iloc[1]*1.15)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #FCCD43" if (i == 1 and v > x.iloc[2]*1.15)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #72A4F7" if (i < 1 and v < x.iloc[1]*0.85)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #72A4F7" if (i == 1 and v < x.iloc[2]*0.85)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #FBBC04" if (i < 1 and v > x.iloc[1]*1.2)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #FBBC04" if (i == 1 and v > x.iloc[2]*1.2)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["color: #ffffff" if (i < 1 and v < x.iloc[1]*0.8)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["color: #ffffff" if (i == 1 and v < x.iloc[2]*0.8)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #4285F4" if (i == 0 and v < x.iloc[1]*0.8)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #4285F4" if (i == 1 and v < x.iloc[2]*0.8) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #ffffff" if (i == 0 and v == x.iloc[1])  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["background: #ffffff" if (i == 1 and v == x.iloc[2]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["color: #000000" if (i < 1 and v == x.iloc[1])  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
+        #     lambda x: ["color: #000000" if (i == 1 and v == x.iloc[2]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).format(
+        #         "{:.1%}",subset=(rows[2:],df_combined_t.columns)).format(
+        #         '{:.0f}',subset=(rows[0],df_combined_t.columns)).set_precision(2).set_properties(
+        #             **{'index':'White','min-width':'60px'},overwrite=False)
+                
+                
+                
+        #         #.set_table_styles([index_names, headers])
+        
+        # df_combined_t_style = df_combined_t_style.format(
+        #     {df_combined_t_style.columns[0]: '{:,.1%}'.format,
+        #      df_combined_t_style.columns[1]: '{:,.1%}'.format,
+        #      df_combined_t_style.columns[2]: '{:,.1%}'.format},subset=(rows[2:],df_combined_t.columns)
+        # )  
+        # df_combined_t_style = df_combined_t_style.format(
+        #         '{:.0f}',subset=(rows[0],df_combined_t.columns))
 
-        top_n = input.top_n()
-        n_1 = input.n_1()
-        n_2 = input.n_2()
-        n_3 = input.n_3()
 
-        list_of_columns =  ['Player', 'Team', 'display_position','percent_owned','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI',
-                                 'L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
+        # df_combined_t_style
 
-        list_of_columns_name =  ['Player', 'Team', 'Position','Roster%','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI',
-                                 'L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
 
-        if type(n_1) is not int:
-            n_1 = 1
             
-        if (n_2 == 0) or (n_2 == n_1) or (n_2 == None):
-            list_of_columns.remove(f'L{str(n_2)} PP TOI')
-            list_of_columns.remove(f'L{str(n_2)} PP%')
-            list_of_columns_name.remove(f'L{str(n_2)} PP TOI')
-            list_of_columns_name.remove(f'L{str(n_2)} PP%')
-
-        if (n_3 == 0) or (n_3 == n_1) or (n_3 == n_2) or (n_3 == None):
-            list_of_columns.remove(f'L{str(n_3)} PP TOI')
-            list_of_columns.remove(f'L{str(n_3)} PP%')
-            list_of_columns_name.remove(f'L{str(n_3)} PP TOI')
-            list_of_columns_name.remove(f'L{str(n_3)} PP%')
 
+        # dfi.export(df_combined_t_style, 'players/'+name+'_'+str(last_games)+'.png',fontsize=9,dpi=600,table_conversion='chrome')
+
+
+        percent_owned = summary_2023_one['percent_owned'].reset_index(drop=True)[0]
+        yahoo_position = summary_2023_one['display_position'].reset_index(drop=True)[0]
+
+
+
+        # image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
+        # logo = nhl_logos[nhl_logos.Team==list(player_games_one['Team'])[0]].reset_index().URL[0]
+        # fig, ax = plt.subplots(figsize=(10,16))
+        # fig.set_facecolor('white')
+
+        # # img = mpimg.imread('players/'+name+'_'+str(last_games)+'.png')
+        # # ax.imshow(img)
+        # # ax.axis('off')
+        # # fig.tight_layout()
+
+        # ax.axis('off')
+        # im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
+        # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
+        # ax.imshow(im)
+        # ax.axis('off')
+        # fig.tight_layout()
+
+        # fig.text(x=0.5,y=-0.05,s='Note: Last Games compares to 2023-24. 2023-24 compares to 2022-23.',horizontalalignment='center',fontsize=16,fontname='Century Gothic')
+        # # fig.text(x=0.05,y=-0.1,s='Created By: @TJStats',horizontalalignment='left',fontsize=24,fontname='Century Gothic')
+        # # fig.text(x=0.95,y=-0.1,s='Data: Natural Stat Trick',horizontalalignment='right',fontsize=24,fontname='Century Gothic')
+        # fig.text(x=0.5,y=1.13,s='NHL Player Summary',horizontalalignment='center',fontsize=52, fontweight='bold')
+        # fig.text(x=0.5,y=1.08,s='2023-24 Season',horizontalalignment='center',fontsize=36,fontname='Century Gothic', fontstyle='italic')
+        # fig.text(x=0.12,y=1.03,s='Player',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
+        # fig.text(x=0.12,y=1.0,s='Team',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
+        # fig.text(x=0.12,y=0.97,s='Position',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
+        # #fig.text(x=0.12,y=0.94,s='Age',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
+        # # fig.text(x=0.12,y=0.91,s='Cap Hit',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
+        # #fig.text(x=0.12,y=0.88,s='Roster%',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
+
+        # fig.text(x=0.25,y=1.03,s=name,horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+        # fig.text(x=0.25,y=1.0,s=list(player_games_one['Team'])[0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+        # fig.text(x=0.25,y=0.97,s=yahoo_position,horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+        # #fig.text(x=0.25,y=0.94,s=str(int(summary_2023_one.reset_index().AGE[0])),horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+        # # fig.text(x=0.25,y=0.91,s=summary_2023_one.loc[summary_2023_one['player_id']==player_id].reset_index()['sheets'][0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+        # #fig.text(x=0.25,y=0.88,s=str(int(percent_owned*100))+'%',horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+
+
+        # im = plt.imread(image)
+        # newax = fig.add_axes([0.5,0.8,0.22,0.22], anchor='NW', zorder=1)
+        # newax.imshow(im)
+
+        # imr = plt.imread(logo)
+        # newerax = fig.add_axes([0.75,0.8,0.22,0.22], anchor='NW', zorder=1)
+        # newerax.imshow(imr)
+
+        # newax.axis('off')
+        # newerax.axis('off')
+
+        # plt.savefig('players/'+name+"_"+str(last_games)+' int.png',bbox_inches="tight")
+        # plt.close()
+
+        #test.loc[name == test.Player].reset_index().TOI[0]/1.5
+        min_time = min(math.floor(summary_2023.loc[player_id == summary_2023.player_id].reset_index().TOI[0]/100)*100,summary_2023.GP.max()*10)
+        # min_time = min(math.floor(test.loc[name == test.Player].reset_index().TOI[0]/100)*100,test.GP.max()*10)
+        test_filter = summary_2023[(summary_2023.TOI >= min_time)&(summary_2023.pos == summary_2023.loc[name == summary_2023.Player].reset_index().pos[0])]
+        test_filter['Goals/GP'] = test_filter['Goals']/test_filter['GP']
+        test_filter['Total Assists/GP'] = test_filter['Assists']/test_filter['GP']
+        test_filter['Total Points/GP'] = test_filter['Total_Points']/test_filter['GP']
+        test_filter['PP Points/GP'] = test_filter['Total_Points_pp']/test_filter['GP']
+        test_filter['Shots/GP'] = test_filter['Shots']/test_filter['GP']
+        test_filter['Hits/GP'] = test_filter['Hits']/test_filter['GP']
+        test_filter['Shots Blocked/GP'] = test_filter['Shots_Blocked']/test_filter['GP']
+        test_filter['ixG/60'] = test_filter['ixG']/test_filter['TOI']*60
+        test_filter['Goals/60'] = test_filter['Goals']/test_filter['TOI']*60
+        test_filter['G-xG/60'] = test_filter['G-ixG']/test_filter['TOI']*60
+        test_filter['iCF/60'] = test_filter['iCF']/test_filter['TOI']*60
+        test_filter['iSCF/60'] = test_filter['iSCF']/test_filter['TOI']*60
+        test_filter['First Assists/60'] = test_filter['First_Assists']/test_filter['TOI']*60
+        test_filter['Total Points/60'] = test_filter['Total_Points']/test_filter['TOI']*60
+
+
+        test_filter['G-xG/60'] = test_filter['Goals/60'] - test_filter['ixG/60']
+        test_filter['Goals Z-Score'] = (test_filter['Goals/GP']-test_filter['Goals/GP'].mean())/test_filter['Goals/GP'].std()
+        test_filter['Assists Z-Score'] = (test_filter['Total Assists/GP']-test_filter['Total Assists/GP'].mean())/test_filter['Total Assists/GP'].std()
+        test_filter['Points Z-Score'] = (test_filter['Total Points/GP']-test_filter['Total Points/GP'].mean())/test_filter['Total Points/GP'].std()
+        test_filter['PP Points Z-Score'] = (test_filter['PP Points/GP']-test_filter['PP Points/GP'].mean())/test_filter['PP Points/GP'].std()
+        test_filter['Shots Z-Score'] = (test_filter['Shots/GP']-test_filter['Shots/GP'].mean())/test_filter['Shots/GP'].std()
+        test_filter['Hits Z-Score'] = (test_filter['Hits/GP']-test_filter['Hits/GP'].mean())/test_filter['Hits/GP'].std()
+        test_filter['Blocks Z-Score'] = (test_filter['Shots Blocked/GP']-test_filter['Shots Blocked/GP'].mean())/test_filter['Shots Blocked/GP'].std()
+
+
+        test_filter['ixG Z-Score'] = (test_filter['ixG/60']-test_filter['ixG/60'].mean())/test_filter['ixG/60'].std()
+        test_filter['G Z-Score'] = (test_filter['Goals/60']-test_filter['Goals/60'].mean())/test_filter['Goals/60'].std()
+        test_filter['G-xG Z-Score'] = (test_filter['G-xG/60']-test_filter['G-xG/60'].mean())/test_filter['G-xG/60'].std()
+        test_filter['iCF Z-Score'] = (test_filter['iCF/60']-test_filter['iCF/60'].mean())/test_filter['iCF/60'].std()
+        test_filter['iSCF Z-Score'] = (test_filter['iSCF/60']-test_filter['iSCF/60'].mean())/test_filter['iSCF/60'].std()
+        test_filter['First Assists Z-Score'] = (test_filter['First Assists/60']-test_filter['First Assists/60'].mean())/test_filter['First Assists/60'].std()
+        test_filter['P Z-Score'] = (test_filter['Total Points/60']-test_filter['Total Points/60'].mean())/test_filter['Total Points/60'].std()
+        values_1 = test_filter.loc[name == test_filter.Player][['Goals/GP','Total Assists/GP','Total Points/GP','PP Points/GP','Shots/GP','Hits/GP','Shots Blocked/GP']].reset_index(drop=True).loc[0]
+        values_2 = test_filter.loc[name == test_filter.Player][['ixG/60','Goals/60','G-xG/60','iCF/60','iSCF/60','First Assists/60','Total Points/60']].reset_index(drop=True).loc[0]
+        categories = [i[:-11]+'/GP' for i in test_filter.columns[-14:-7]]
+
+        #categories = [*categories]
+
+        player = list(np.around(test_filter.loc[name == test_filter.Player][test_filter.columns[-14:-7]].values.flatten().tolist()))
+
+        #player = [*player, player[0]]
+
+
+        label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(player))
+
+        players_stats_all_on_filter = test_filter.copy()
+        players_stats_all_on_filter['GF/60'] = (players_stats_all_on_filter['GF'])/players_stats_all_on_filter['TOI']*60
+        players_stats_all_on_filter['xGF/60'] = (players_stats_all_on_filter['xGF'])/players_stats_all_on_filter['TOI']*60
+        players_stats_all_on_filter['CF/60'] = (players_stats_all_on_filter['CF'])/players_stats_all_on_filter['TOI']*60
+        # players_stats_all_on_filter['oiSH%'] = (players_stats_all_on_filter['On-Ice SH%']-players_stats_all_on_filter['On-Ice SH%'].mean())/players_stats_all_on_filter['On-Ice SH%'].std()
+        #players_stats_all_on_filter['OZ Start%'] = (players_stats_all_on_filter['CF']-players_stats_all_on_filter['CF'])/players_stats_all_on_filter['CF']
+        players_stats_all_on_filter['GF Z-Score'] = (players_stats_all_on_filter['GF/60']-players_stats_all_on_filter['GF/60'].mean())/players_stats_all_on_filter['GF/60'].std()
+        players_stats_all_on_filter['xGF Z-Score'] = (players_stats_all_on_filter['xGF/60']-players_stats_all_on_filter['xGF/60'].mean())/players_stats_all_on_filter['xGF/60'].std()
+        players_stats_all_on_filter['CF Z-Score'] = (players_stats_all_on_filter['CF/60']-players_stats_all_on_filter['CF/60'].mean())/players_stats_all_on_filter['CF/60'].std()
+        players_stats_all_on_filter['oiSH% Z-Score'] = (players_stats_all_on_filter['oiSH']-players_stats_all_on_filter['oiSH'].mean())/players_stats_all_on_filter['oiSH'].std()
+        players_stats_all_on_filter['OZ Start% Z-Score'] = (players_stats_all_on_filter['OZ Start%']-players_stats_all_on_filter['OZ Start%'].mean())/players_stats_all_on_filter['OZ Start%'].std()
+        categories_on = [i[:-8]+'/60' for i in players_stats_all_on_filter.columns[-5:]]
+        categories_on = ['GF/60', 'xGF/60', 'CF/60', 'oiSH', 'OZ Start%']
+        player_on = list(np.around(players_stats_all_on_filter.loc[name == test_filter.Player][players_stats_all_on_filter.columns[-5:]].values.flatten().tolist(),2))
+        categories_1 = [i[:-8]+'/GP' for i in test_filter.columns[-14:-7]]
+        categories_2 = [i[:-8]+'/60' for i in test_filter.columns[-7:]]
+        categories = categories_1+categories_2
+        categories[12] = '1A/60'
+        player = list(np.around(test_filter.loc[name == test_filter.Player][test_filter.columns[-14:]].values.flatten().tolist(),2))
+
+        # color_scheme = []
+        # for i in player:
+        #     if i <= -2:
+        #         color_scheme.append('#4285F4')
+        #     if i > -2 and i <= -1:
+        #         color_scheme.append('#A1C2FA')
+        #     if i > -1 and i <= 0:
+        #         color_scheme.append('#D0E1FD')
+        #     if i > 0 and i <= 1:
+        #         color_scheme.append('#FEEFC1')
+        #     if i > 1 and i <= 2:
+        #         color_scheme.append('#FDDE82')
+        #     if i > 2:
+        #         color_scheme.append('#FBBC04')
             
 
-        start_date ='2023-09-01'
-        end_date = '2024-05-01'
+        goals_above_expected = test_filter.loc[name == test_filter.Player]['Goals'] - test_filter.loc[name == test_filter.Player]['ixG']
+        goals_above_expected = test_filter.loc[name == test_filter.Player]['Goals'] - test_filter.loc[name == test_filter.Player]['ixG']
+
+        # color_scheme_on= []
+        # for i in player_on:
+        #     if i <= -2:
+        #         color_scheme_on.append('#4285F4')
+        #     if i > -2 and i <= -1:
+        #         color_scheme_on.append('#A1C2FA')
+        #     if i > -1 and i <= 0:
+        #         color_scheme_on.append('#D0E1FD')
+        #     if i > 0 and i <= 1:
+        #         color_scheme_on.append('#FEEFC1')
+        #     if i > 1 and i <= 2:
+        #         color_scheme_on.append('#FDDE82')
+        #     if i > 2:
+        #         color_scheme_on.append('#FBBC04')
+
+        fig = plt.figure(figsize=(24, 13.5),dpi=300)
+        fig.set_facecolor('white')
+
+        cmap_new = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white",'#FBBC04'])
+        colormap_new = plt.get_cmap(cmap_new)
+        norm_new = Normalize(vmin=-3, vmax=3)
+
+        color_scheme = [colormap_new(norm_new(x)) for x in player]
+        color_scheme_on = [colormap_new(norm_new(x)) for x in player_on]
+            
+        players_stats_all_on_filter = players_stats_all_on_filter.rename(columns={'On-Ice SH%':'oiSH%','Off.\xa0Zone Start %':'OZ Start%'})
+        values_on = players_stats_all_on_filter.loc[name == test_filter.Player][['GF/60', 'xGF/60', 'CF/60', 'oiSH', 'OZ Start%']].reset_index(drop=True).loc[0]
+        position_player = summary_2023_one['pos'].reset_index().pos[0]
+        rank_1 = list(test_filter[values_1.index].rank(method='min',ascending=False).loc[name == test_filter.Player].reset_index(drop=True).astype(int).iloc[0])
+        rank_2 = list(test_filter[values_2.index].rank(method='min',ascending=False).loc[name == test_filter.Player].reset_index(drop=True).astype(int).iloc[0])
+        rank_3 = list(players_stats_all_on_filter[values_on.index].rank(method='min',ascending=False).loc[name == test_filter.Player].reset_index(drop=True).astype(int).iloc[0][categories_on])
+        #rank_3 = rank_3 + list(players_stats_all_on_filter[values_on.index].rank(method='first',ascending=True).loc[name == test.Player].reset_index(drop=False).astype(int).iloc[0][categories_on[3:5]])
 
-        df_pp_1 = player_games_df.groupby('Player').head(n_1)
-        df_pp_2 = player_games_df.groupby('Player').head(n_2)
-        df_pp_3 = player_games_df.groupby('Player').head(n_3)
+        values_on = [float(x) for x in values_on]
 
+        player_games_one = player_games_one.merge(right=team_games[['Team','pp_toi','date']],left_on=['Team','date'],right_on=['Team','date'],how='left').fillna(0)
+        y=(player_games_one.rolling(last_games).sum()['TOI_pp']/player_games_one.rolling(last_games).sum()['pp_toi'])[last_games:]
+        player_games_one = player_games_one.sort_values(by='date').reset_index(drop=True)
 
-        team_games_df_1 = team_games_df.groupby('team').head(n_1)
-        team_games_df_2 = team_games_df.groupby('team').head(n_2)
-        team_games_df_3 = team_games_df.groupby('team').head(n_3)
+        # fig, ([ax1,ax2],[ax3,ax4])= plt.subplots(nrows=2, ncols=2,figsize=(16,10))
 
-        df_all_pp_1 = df_pp_1.copy()
-        df_all_pp_2 = df_pp_2.copy()
-        df_all_pp_3 = df_pp_3.copy()
 
-        df_all_pp_1_final = df_all_pp_1.groupby(['player_id','Player','Team','Position']).sum()[['TOI_pp']].reset_index()
-        df_all_pp_2_final = df_all_pp_2.groupby(['player_id','Player','Team','Position']).sum()[['TOI_pp']].reset_index()
-        df_all_pp_3_final = df_all_pp_3.groupby(['player_id','Player','Team','Position']).sum()[['TOI_pp']].reset_index()
 
-        team_games_df_1_final = team_games_df_1.groupby(['abv']).sum()[['pp_toi']].reset_index()
-        team_games_df_2_final = team_games_df_2.groupby(['abv']).sum()[['pp_toi']].reset_index()
-        team_games_df_3_final = team_games_df_3.groupby(['abv']).sum()[['pp_toi']].reset_index()
+        colormap = plt.get_cmap(cmap)
 
-        df_final =  df_all_pp_1_final.merge( df_all_pp_2_final,how='outer',left_on=['player_id'],right_on=['player_id'],suffixes=("","_2"))
-        df_final =  df_final.merge( df_all_pp_3_final,how='outer',left_on=['player_id'],right_on=['player_id'],suffixes=("_1","_3"))
+        value = 1
+        # Normalize the value
+        norm = Normalize(vmin=0.8, vmax=1.2)
+        normalized_value = norm(value)
 
-        team_final =  team_games_df_1_final.merge( team_games_df_2_final,how='outer',left_on=['abv'],right_on=['abv'],suffixes=("","_2"))
-        team_final =  team_final.merge( team_games_df_3_final,how='outer',left_on=['abv'],right_on=['abv'],suffixes=("_1","_3"))
+        col_3_colour = ['white']*len(df_combined_t)
+        col_2_colour = [colormap(norm(x)) for x in list(((df_combined_t[df_combined_t.columns[1]].values) / (df_combined_t[df_combined_t.columns[2]].values)))]
+        col_1_colour = [colormap(norm(x)) for x in list(((df_combined_t[df_combined_t.columns[0]].values) / (df_combined_t[df_combined_t.columns[1]].values)))]
+        colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour]).T.values
 
+        colour_df[[0],[0]] = 'white'
+        colour_df[[1],[0]] = 'white'
+        colour_df[[0],[1]] = 'white'
+        colour_df[[1],[1]] = 'white'
+        if df_combined_t.values[[10],[0]] < 0:
+            if df_combined_t.values[[10],[1]] < 0:
+                #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
+                norm = Normalize(vmin=-1.2, vmax=-0.8)
+                colour_df[[10],[0]] = tuple(colormap(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))
 
-        df_final = df_final.merge(team_final,left_on='Team_1',right_on='abv')
+        if df_combined_t.values[[10],[1]] < 0:
+            if df_combined_t.values[[10],[2]] < 0:
+                #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
+                norm = Normalize(vmin=-1.2, vmax=-0.8)
+                colour_df[[10],[1]] = tuple(colormap(norm(-df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))
 
-        test = df_final[['player_id','Player_1','Team_1','Position_1','TOI_pp_1','TOI_pp_2','TOI_pp_3','pp_toi_1','pp_toi_2','pp_toi_3']]
-        test.columns = ['player_id','Player','Team','Position','TOI_1','TOI_2','TOI_3','pp_toi_1','pp_toi_2','pp_toi_3']
-        test = test.fillna('0')
 
 
-        test['PP%_1'] = test['TOI_1'].astype(float)/ test['pp_toi_1'].astype(float)
-        test['PP%_2'] = test['TOI_2'].astype(float)/ test['pp_toi_2'].astype(float)
-        test['PP%_3'] = test['TOI_3'].astype(float)/ test['pp_toi_3'].astype(float)
-        # test = test.fillna(0)
-        test['TOI_1'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_1'].astype(float)]
-        test['TOI_2'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_2'].astype(float)]
-        test['TOI_3'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_3'].astype(float)]
-        test = test.drop(['pp_toi_1','pp_toi_2','pp_toi_3'],axis=1)
-        test.columns = ['player_id','Player','Team','Position','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI','L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
 
 
-        yahoo_df = yahoo_df_2.merge(yahoo_nhl_df,left_on = 'player_id',right_on='player_id_yahoo',suffixes=['','_y'])
-        yahoo_df.nhl_id = yahoo_df.nhl_id.astype(float)
-        test.player_id = test.player_id.astype(float)
+        ax1 = plt.subplot(1,3,1)
+        ax2 = plt.subplot(3,3,2)
+        ax3 = plt.subplot(3,3,5)
+        ax4 = plt.subplot(3,3,8)
+        ax5 = plt.subplot(3,3,3)
+        ax6 = plt.subplot(3,3,6)
+        ax7 = plt.subplot(3,3,9)
+        #axbot = plt.subplot(3,1,1)
+        axes = [ax1, ax2, ax3, ax4, ax5,ax6,ax7]
+        # ax[0][0].axis('off')
+        # im = plt.imread('players/'+name+'_'+str(last_games)+' int.png')
+        # ax[0][0] = fig.add_axes([0,0,1,1], anchor='W', zorder=1)
+        # ax[0][0].imshow(im)
 
-        test = test.merge(right=yahoo_df,left_on='player_id',right_on='nhl_id',suffixes=['','_y'],how='left')
+        
+        # ax[0][0].axis('off')
+        # ax[1][0].axis('off')
+        image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
+        logo = nhl_logos[nhl_logos.Team==list(player_games_one['Team'])[0]].reset_index().URL[0]
+        #im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
+        #ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
+        #ax.imshow(im)
 
 
-        test.loc[test.display_position.isna(),'display_position'] = test.loc[test.display_position.isna(),'Position']
-        test.display_position = test.display_position.replace({'L':'LW','R':'RW'})
-        test.percent_owned = test.percent_owned.fillna(0)
+        im = plt.imread(image)
+        im = OffsetImage(im, zoom=.75)
+        ab = AnnotationBbox(im, (0.69, 0.765), xycoords='axes fraction', box_alignment=(0.0,0.0),bboxprops={'edgecolor':'white'})
+        ax1.add_artist(ab)
+        # axim = fig.add_axes([0.25,0.76,0.12,0.12], anchor='NW', zorder=1)
+        # axim.imshow(im)
+        # axim.axis('off')
 
-        
+        imr = plt.imread(logo)
+        imr = OffsetImage(imr, zoom=.1)
+        ab = AnnotationBbox(imr, (0.45, 0.765), xycoords='axes fraction', box_alignment=(0.0,0.0),bboxprops={'edgecolor':'white'})
+        ax1.add_artist(ab)
+        # axim = fig.add_axes([0.18,0.76,0.75,0.075], anchor='NW', zorder=1)
+        # axim.imshow(imr)
+        # axim.axis('off')
 
+        sub_value  = 0.16
+        ax1.text(x=0.5,y=1.13-sub_value,s='NHL Player Summary',horizontalalignment='center',fontsize=36, fontweight='bold')
+        ax1.text(x=0.5,y=1.08-sub_value,s='2022-23 Season',horizontalalignment='center',fontsize=28,fontname='Century Gothic', fontstyle='italic')
+        ax1.text(x=0.05,y=1.04-sub_value,s='Player',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
+        ax1.text(x=0.05,y=1.005-sub_value,s='Team',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
+        ax1.text(x=0.05,y=0.97-sub_value,s='Position',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
+        #ax1.text(x=0.1,y=0.94-sub_value,s='Age',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
+        #ax1.text(x=0.12,y=0.91-sub_value,s='Cap Hit',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
+        ax1.text(x=0.05,y=0.935-sub_value,s='Roster%',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
 
-        print('Column List')
-        print(test.columns)
+        ax1.text(x=0.175,y=1.04-sub_value,s=name,horizontalalignment='left',fontsize=18,fontname='Century Gothic')
+        ax1.text(x=0.175,y=1.005-sub_value,s=list(player_games_one['Team'])[0],horizontalalignment='left',fontsize=18,fontname='Century Gothic')
+        ax1.text(x=0.175,y=0.97-sub_value,s=yahoo_position,horizontalalignment='left',fontsize=18,fontname='Century Gothic')
+        #ax1.text(x=0.25,y=0.94-sub_value,s=str(summary_2023_one.reset_index().AGE[0]),horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+        #ax1.text(x=0.25,y=0.91-sub_value,s=summary_2023_one.loc[summary_2023_one['player_id']==player_id].reset_index()['sheets'][0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
+        ax1.text(x=0.175,y=0.935-sub_value,s=str(int(percent_owned*100))+'%',horizontalalignment='left',fontsize=18,fontname='Century Gothic')
 
 
-        print(list_of_columns)
-        test = test[list_of_columns]
-        test = test.rename(columns={'percent_owned':'Roster%'})
-        test = test.rename(columns={'display_position':'Position'})
-        
 
-        top_d_score = test[(test.Team==input.team_id())].sort_values(by=['L'+str(n_1)+' PP%'],ascending=False).reset_index(drop=True)
-        
-        if input.x():
-            top_d_score = top_d_score.dropna(axis='columns')
-        
-        top_d_score = top_d_score.head(min(len(top_d_score),top_n))
 
+        ax1.axis("off")
 
-        #top_d_score.columns = list_of_columns_name
 
-        
 
-        top_d_score['Deployment'] = "PP2"
-        top_d_score['Deployment'][0:5] = "PP1"
 
-        
-        cols = top_d_score.columns.tolist();
-        print('we made it here',cols)
-
-        # for i in list(input.ignore_id()):
-        #     print('we made it here')
-        #     print(i)
-        #     cols.remove(i)
-
-        # df_style_bang = top_d_score.head(10).style.background_gradient(cmap=co, subset=['L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%','Roster%']).hide_index().set_properties(**{'Height': '12px'},**{'text-align': 'center'}).set_table_styles([{
-        #         'selector': 'caption',
-        #         'props': [
-        #             ('color', ''),
-        #             ('fontname', 'Century Gothic'),
-        #             ('font-size', '20px'),
-        #             ('font-style', 'italic'),
-        #             ('font-weight', ''),
-        #             ('text-align', 'centre'),
-        #             ]
-
-        #             },{'selector' :'th', 'props':[('text-align', 'center'),('Height','5px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('fontname', 'Century Gothic')]}]).format(
-        #                 {'L'+str(n_1)+' PP%': '{:.0%}',
-        #                 'L'+str(n_2)+' PP%': '{:.0%}',
-        #                 'L'+str(n_3)+' PP%': '{:.0%}',
-        #                 'Roster%': '{:.0%}',
-        #                 },)
-
-        df_style_bang = top_d_score[cols].head(input.top_n()).style.background_gradient(cmap=co,vmin=0,vmax=1, subset=[x for x in cols if x.endswith('PP%')]).set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
-            'selector': 'caption',
-            'props': [
-                ('color', ''),
-                ('fontname', 'Century Gothic'),
-                ('font-size', '20px'),
-                ('font-style', 'italic'),
-                ('font-weight', ''),
-                ('text-align', 'centre'),
-                ]
-
-                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(
-                    'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
-                    **{'background-color':'White','index':'White','min-width':'100px'},overwrite=False).set_properties(
-                    **{'background-color':'White','index':'White','min-width':'200px'},overwrite=False,subset=cols[0]).set_table_styles(
-            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
-            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
-            [{'selector': 'tr', 'props': [('line-height', '35px')]}],overwrite=False).set_properties(
-            **{'Height': '35px'},**{'text-align': 'center'},overwrite=False).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(
-                    **{'border': '1px black solid !important'},subset = ((list(top_d_score.index[:]),top_d_score.columns[:]))).set_properties(**{
-                                'color': 'black'},overwrite=False).set_properties(
-                    **{'border': '1px black solid !important'},subset = ((list(top_d_score.index[:]),top_d_score.columns[:]))).format(
-            {
-            'L'+str(n_1)+' PP%': '{:.0%}',
-            'L'+str(n_2)+' PP%': '{:.0%}',
-            'L'+str(n_3)+' PP%': '{:.0%}',
-            'Roster%': '{:.0%}',
-            },).background_gradient(cmap=co, subset=[x for x in cols if x.endswith('PP%')]).hide_index()
-
-        return df_style_bang
+
+        table = ax1.table(cellText=df_combined_t.values, colLabels=df_combined_t.columns,rowLabels=df_combined_t.index
+                        , cellLoc='center',rowLoc='center', bbox=[0.15, 0.05, 0.8, 0.7],cellColours=colour_df)
+        #table.auto_set_font_size(True)
+        table.set_fontsize(20)
+        table.scale(1, 1.5)  
+
+
+
+        format_col = df_combined_t[df_combined_t.columns[0]]
+        n_c = 0
+        for cell in table.get_celld().values():
+            if n_c < 1*3:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
+            elif n_c < 16*3:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
+            else:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+            n_c = n_c + 1 
+
+
+        format_col = df_combined_t[df_combined_t.columns[1]]
+        n_c = 0
+        for cell in table.get_celld().values():
+            if n_c < 1*3:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
+            elif n_c < 16*3:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
+            else:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+            n_c = n_c + 1 
+
+        format_col = df_combined_t[df_combined_t.columns[2]]
+        n_c = 0
+        for cell in table.get_celld().values():
+            if n_c < 1*3:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
+            elif n_c < 16*3:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
+            else:
+                if cell.get_text().get_text() in format_col.astype(str).values:
+                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+            n_c = n_c + 1 
+
+        #ax1.text('')
+
+
+
+        # ax1.axis('off')
+        # img = mpimg.imread('players/'+name+'_'+str(last_games)+' int.png')
+
+        # im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
+        # ax1 = fig.add_axes([0.03,0.,1,1], anchor='SW')
+        # ax1.imshow(im)
+
+        # ax1.imshow(img)
+        # ax1.axis('off')
+        # fig.tight_layout()
+
+
+
+        categories[0:7] = ['G/GP','A/GP','P/GP','PPP/GP','S/GP','Hits/GP','Blk/GP']
+        _ = sns.barplot(data=test_filter[test_filter.columns[56:63]],x=categories[0:7],y=player[0:7],palette=color_scheme[0:7],edgecolor='black',ax=ax2)
+
+        ax2.set_title("Individual Per Game Z-Score (vs "+position_player+", min. "+str(min_time)+' TOI)',fontsize=14,fontname='Century Gothic')
+
+
+        #plt.rcParams['xtick.color']='#333F4B'
+        #ax[0][1].set_prop_cycle(ytick.color='#333F4B')
+
+
+        ax2.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
+        #plt.ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
+
+        #plt.xlabel('Percentile', fontsize=12, color = '#000000',fontname='Century Gothic')
+        #plt.tick_params(axis='x', which='both', labelsize=10,bottom=False,top=False)
+        ax2.set_ylim([-3, 3])
+        plt.style.use('classic')
+        show_values(ax2,stat = values_1,rank_n=rank_1)
+        ax2.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
+
+        ax2.set_axisbelow(True)
+        #ax[1].hlines(y=0,xmin=0,xmax=len(player),color='black')
+
+
+        #plt.savefig('players/'+name+"_"+str(last_games)+'_Z.png')#,bbox_inches="tight")
+        # ax[1][0] = sns.barplot(data=test,x=categories,y=player,palette=color_scheme,edgecolor='black')
+        # show_values(ax2,stat = values)
+        # data_input = df_shots[(df_shots.shooterName==name) & (df_shots.event!='MISS')]
+        # sns.kdeplot(data=data_input, x=data_input["yCordAdjusted"]*-1, y=data_input["xCordAdjusted"],fill=True,thresh=0.4,cmap=cmap,ax=ax3)
+        # #sns.scatterplot(data=data_input, x=data_input["yCordAdjusted"]*-1, y=data_input["xCordAdjusted"], alpha=1,zorder=25,cmap='Blues',s=400,hue='shotType',palette="Set2")
+        # rink = NHLRink(rotation=270)
+        # #x, y = rink.convert_xy(x, y)
+        # rink.draw(ax=ax3,display_range='dzone')
+        # ax3.set_title(name+" Shooting Heat Map",fontsize=16,fontname='Century Gothic')
+        #plt.suptitle('NHL Shot Locations ', fontsize=32, y = 0.91)
+        #plt.title('All Shots', fontsize=16, y=1.02)
+        sns.barplot(x=categories[7:14],y=player[7:14],palette=color_scheme[7:14],edgecolor='black',ax=ax3)
+        ax3.set_title("Individual Rate Z-Score (vs "+position_player+", min. "+str(min_time)+' TOI)',fontsize=14,fontname='Century Gothic')
+        ax3.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
+        ax3.set_ylim([-3, 3])
+        ax3.set_axisbelow(True)
+        ax3.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
+        #plt.rcParams['xtick.color']='#333F4B'
+        #ax[0][1].set_prop_cycle(ytick.color='#333F4B')
+
+
+        sns.barplot(x=categories_on,y=player_on,palette=color_scheme_on,edgecolor='black',ax=ax4)
+        ax4.set_ylim([-3, 3])
+        ax4.set_title("On-Ice All Situations Rate Z-Score (vs "+position_player+", min. "+str(min_time)+' TOI)',fontsize=14,fontname='Century Gothic')
+        ax4.set_axisbelow(True)
+        ax4.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
+        ax4.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
+
+        ax2.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
+        show_values(ax3,stat = values_2,rank_n=rank_2)
+        values_on[3] = str(round(values_on[3]*100,1))+'%'
+        values_on[4] = str(round(values_on[4]*100,1))+'%'
+        show_values(ax4,stat = values_on,rank_n=rank_3)
+
+
+        ax2.text(-0.5, -3.8, '2023-24', ha="right",fontstyle='italic',zorder=1) 
+        ax3.text(-0.5, -3.8, '2023-24', ha="right",fontstyle='italic') 
+        ax4.text(-0.5, -3.8, '2023-24', ha="right",fontstyle='italic') 
+
+
+        ax2.text(-0.5, -4.2, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100) 
+        ax3.text(-0.5, -4.2, 'Rank(of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100) 
+        ax4.text(-0.5, -4.2, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100) 
+
+
+        ax2.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)  
+        ax3.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)
+        ax4.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)
+
+        if summary_2023_one.GP.values[0] < 2:
+            line_text_value = 1
+        else:
+            line_text_value = last_games+0.5
+
+        if position_player == 'F':
+            ax5.hlines(y=18,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
+            ax5.text(s="1st",y=18,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
+            ax5.hlines(y=16,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
+            ax5.text(s="2nd",y=16,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
+            ax5.hlines(y=14,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
+            ax5.text(s="3rd",y=14,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
+            ax5.hlines(y=12,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
+            ax5.text(s="4th",y=12,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
+
+        if position_player == 'D':
+            ax5.hlines(y=23,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
+            ax5.text(s="1st",y=23,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
+            ax5.hlines(y=20,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
+            ax5.text(s="2nd",y=20,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
+            ax5.hlines(y=17,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
+            ax5.text(s="3rd",y=17,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
+
+
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games,min_periods=last_games).mean()['TOI'],ax=ax5,color='#FFB000',linewidth = 2,label='TOI/GP',legend=False)
+
+
+
+
+        ax5.xaxis.set_major_locator(MaxNLocator(integer=True))
+        ax5.set_xlim([last_games, np.max(player_games_one.game)])
+        axn = ax5.twinx()
+        #sns.lineplot(x=game_log_pp.Game,y=game_log_pp.rolling(last_games).mean()['PP_TOI'],ax=axn,color='#DCAD23',linewidth = 2,label='PP TOI/GP',legend=False)
+        #ax5.set_ylim([min(math.floor(min(player_games_one.TOI)/5)*5,10), math.ceil(max((player_games_one.rolling(last_games).mean().fillna(0)['TOI']))/5)*5])
+        ax5.set_ylim([10,30])
+        ax5.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
+        ax5.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
+        ax5.set_title(str(last_games)+" Game Rolling Average - TOI and PP%",fontsize=14,fontname='Century Gothic')
+        ax5.set(xlabel='Game', ylabel='TOI/GP')
+        axn.set(xlabel='Game', ylabel='PP%')
+        axn.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
+        axn.set_ylim([0, 1])
+
+
+        sns.lineplot(x=player_games_one.game,y=(player_games_one.rolling(last_games).sum()['TOI_pp']/player_games_one.rolling(last_games).sum()['pp_toi']),ax=axn,color='#648FFF',linewidth = 2,label='PP%',legend=False)
+        #
+        handles, labels = [ax5.get_legend_handles_labels()[0]+axn.get_legend_handles_labels()[0]]+[ax5.get_legend_handles_labels()[1]+axn.get_legend_handles_labels()[1]]
+        ax5.legend(handles, labels, fontsize=10,ncol=2,loc=9)
+
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Points'],ax=ax6,color='#FFB000',label='Points',linewidth = 2)
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Points_pp'],ax=ax6,color='#648FFF',label='PP Points',linewidth = 2)
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Goals'],ax=ax6,color='#DC267F',label='Goals',linewidth = 2)
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Assists'],ax=ax6,color='#FE6100',label='Assists',linewidth = 2)
+        ax6.legend(fontsize=10,ncol=4,loc=9)
+        ax6.xaxis.set_major_locator(MaxNLocator(integer=True))
+        ax6.set_xlim([last_games, np.max(player_games_one.game)])
+        ax6.set_ylim([0, round(max((player_games_one.rolling(last_games).mean().fillna(0)['Total Points'])+1)*2)/2])
+        ax6.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
+        ax6.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
+        ax6.set_title(str(last_games)+" Game Rolling Average - Points",fontsize=14)
+        ax6.set(xlabel='Game', ylabel='Value Per Game')
+
+
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Shots'],ax=ax7,color='#FFB000',linewidth = 2,label='Shots')
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['iCF'],ax=ax7,color='#648FFF',linewidth = 2,label='iCF')
+        # sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['iSCF'],ax=ax7,color='#DC267F',linewidth = 2,label='iSCF')
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Hits'],ax=ax7,color='#DC267F',linewidth = 2,label='Hits')
+        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Shots Blocked'],ax=ax7,color="#FE6100",linewidth = 2,label='Blocks')
+
+        ax7.legend(fontsize=10,ncol=5,loc=9)
+        ax7.xaxis.set_major_locator(MaxNLocator(integer=True))
+        ax7.set_xlim([last_games, np.max(player_games_one.game)])
+        ax7.set_ylim([0, max(round(max((player_games_one.rolling(last_games).mean().fillna(0)['iCF'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Shots'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Hits'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Shots Blocked'])+1)*2)/2+1)])
+        ax7.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
+        ax7.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
+        ax7.set_title(str(last_games)+" Game Rolling Average - Shots and Bangers",fontsize=14,fontname='Century Gothic')
+        ax7.set(xlabel='Game', ylabel='Value Per Game')
+        ax2.hlines(y=0,xmin=-0.5, xmax=len(ax2.patches)-0.5, color='black',linewidth=1)
+        ax3.hlines(y=0,xmin=-0.5, xmax=len(ax3.patches)-0.5, color='black',linewidth=1)
+        ax4.hlines(y=0,xmin=-0.5, xmax=len(ax4.patches)-0.5, color='black',linewidth=1)
+
+        ax2.set_xlim([-0.5, len(ax2.patches)-0.5])
+        ax3.set_xlim([-0.5, len(ax3.patches)-0.5])
+        ax4.set_xlim([-0.5, len(ax4.patches)-0.5])
+
+        #plt.tight_layout(pad=5, w_pad=2, h_pad=2)
+
+        #abot = fig.add_axes([0.075,0.025,0.9,0.025], anchor='NW', zorder=1)
+
+        print('here')
+
+        ax1.text(x=0.0,y=-0.025,s='Created By: @TJStats',horizontalalignment='left',fontsize=14)#,fontname='Century Gothic')
+        ax1.text(x=0.0,y=-0.05,s='Data: Natural Stat Trick, CapFriendly, Yahoo Fantasy',horizontalalignment='left',fontsize=14)#,fontname='Century Gothic')
+        ax1.text(x=0.725,y=-0.025,s=f'Generated: {str(date.today())}',horizontalalignment='right',fontsize=14)#,fontname='Century Gothic')
+
+        from matplotlib.font_manager import FontProperties
+        font_properties_label = FontProperties(family='century gothic', size=16)
+        ax5.set_xlabel(xlabel=ax5.get_xlabel(),fontproperties=font_properties_label) 
+        ax6.set_xlabel(xlabel=ax6.get_xlabel(),fontproperties=font_properties_label) 
+        ax7.set_xlabel(xlabel=ax7.get_xlabel(),fontproperties=font_properties_label) 
+        ax5.set_ylabel(ylabel=ax5.get_ylabel(),fontproperties=font_properties_label) 
+        ax6.set_ylabel(ylabel=ax6.get_ylabel(),fontproperties=font_properties_label) 
+        ax7.set_ylabel(ylabel=ax7.get_ylabel(),fontproperties=font_properties_label) 
+
+        font_properties_title = FontProperties(family='century gothic', size=16)
+        ax2.set_title(label=ax2.get_title(),fontproperties=font_properties_title) 
+        ax3.set_title(label=ax3.get_title(),fontproperties=font_properties_title) 
+        ax4.set_title(label=ax4.get_title(),fontproperties=font_properties_title) 
+        ax5.set_title(label=ax5.get_title(),fontproperties=font_properties_title) 
+        ax6.set_title(label=ax6.get_title(),fontproperties=font_properties_title) 
+        ax7.set_title(label=ax7.get_title(),fontproperties=font_properties_title) 
+
+        ax1.text(x=0.43,y=0.025,s='Note: Last Games compares to 2023-24. 2023-24 compares to 2022-23.',horizontalalignment='center',fontsize=12,fontname='Century Gothic')
+        # font_properties_ticks = FontProperties(family='century gothic', size=12)
+        # ax5.set_xticklabels(ax5.get_xticks(),fontproperties=font_properties_label) 
+
+        plt.xticks(fontname = 'century gothic')
+
+
+
+        #abot.axis('off')
+        #ax1.set_zorder(2)
+        fig.tight_layout()
+
+        # for i in range(0,len(df_combined_t.index)):
+        #     ax1.text(s=df_combined_t.index[i],x=10,y=700+(10*i),fontsize=10,bbox=dict(facecolor='red', alpha=1))
+
+
+        # #plt.figure(dpi=300)
+        # if not os.path.exists('player_cards/skaters/'+name): 
+        #     os.makedirs('player_cards/skaters/'+name)
+
+        # dir = 'players/'
+        # for f in os.listdir(dir):
+        #     os.remove(os.path.join(dir, f))
+
+
+        #plt.savefig('player_cards/skaters/'+name+'/'+name+"_"+str(last_games)+'_Znew.png',dpi=600,bbox_inches="tight")
+
+        #matplotlib.rcParams["figure.dpi"] = 600
+
+        #plt.savefig('players/'+name+"_"+str(last_games)+'_Znew.png',dpi=600,bbox_inches="tight")