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| import requests | |
| import pandas as pd | |
| 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 | |
| #import plotly.express as px | |
| #!pip install chart_studio | |
| # import chart_studio.tools as tls | |
| #from bs4 import BeautifulSoup | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import matplotlib.font_manager as font_manager | |
| from datetime import datetime | |
| import pytz | |
| from datetime import date | |
| datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y') | |
| # Configure Notebook | |
| #%matplotlib inline | |
| plt.style.use('fivethirtyeight') | |
| sns.set_context("notebook") | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| #from urllib.request import urlopen | |
| import json | |
| from datetime import date, timedelta | |
| #import dataframe_image as dfi | |
| #from os import listdir | |
| #from os.path import isfile, join | |
| import datetime | |
| import seaborn as sns | |
| import os | |
| import calendar | |
| #from IPython.display import display, HTML | |
| import matplotlib.image as mpimg | |
| #from skimage import io | |
| #import difflib | |
| from datetime import datetime | |
| import pytz | |
| datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y') | |
| # Configure Notebook | |
| #%matplotlib inline | |
| 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 PIL import Image | |
| import logging | |
| import matplotlib.patches as patches | |
| from matplotlib.patches import Rectangle | |
| from matplotlib.font_manager import FontProperties | |
| from matplotlib.offsetbox import OffsetImage, AnnotationBbox | |
| import requests | |
| #import pickle | |
| 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() | |
| # 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']))]) | |
| # 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() | |
| team_abv = pd.read_csv('team_abv.csv') | |
| # 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('2024_schedule_href.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' | |
| yahoo_weeks = pd.read_csv('yahoo_weeks.csv') | |
| #yahoo_weeks['Number'] = yahoo_weeks['Number'].astype(int) | |
| yahoo_weeks['Start'] = pd.to_datetime(yahoo_weeks['Start']) | |
| yahoo_weeks['End'] = pd.to_datetime(yahoo_weeks['End']) | |
| yahoo_weeks.head(5) | |
| def highlight_cols(s): | |
| color = '#C2FEE9' | |
| return 'background-color: %s' % color | |
| def highlight_cells(val): | |
| color = 'white' if val == ' ' else '' | |
| return 'background-color: {}'.format(color) | |
| import matplotlib.pyplot as plt | |
| import matplotlib.colors | |
| cmap_total = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#56B4E9","#FFFFFF","#F0E442"]) | |
| cmap_off = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"]) | |
| 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' | |
| #if not os.path.isfile('standings/standings_'+str(date.today())+'.csv'): | |
| #standings_df_old = pd.read_html('https://www.hockey-reference.com/leagues/NHL_2024_standings.html')[0].append(pd.read_html('https://www.hockey-reference.com/leagues/NHL_2024_standings.html')[1]) | |
| standings_df_old = pd.read_csv('2024_standings_href.csv') | |
| # standings_df_old.to_csv('standings/standings_'+str(date.today())+'.csv') | |
| #standings_df_old = pd.read_csv('standings/standings_'+str(date.today())+'.csv',index_col=[0]) | |
| standings_df = standings_df_old[standings_df_old['Unnamed: 0'].str[-8:] != 'Division'].sort_values('Unnamed: 0').reset_index(drop=True).rename(columns={'Unnamed: 0':'Team'})#.drop(columns='Unnamed: 0') | |
| #standings_df = standings_df.replace('St. Louis Blues','St Louis Blues') | |
| standings_df['GF/GP'] = standings_df['GF'].astype(int)/standings_df['GP'].astype(int) | |
| standings_df['GA/GP'] = standings_df['GA'].astype(int)/standings_df['GP'].astype(int) | |
| standings_df['GF_Rank'] = standings_df['GF/GP'].rank(ascending=True,method='first')/10-1.65 | |
| standings_df['GA_Rank'] = standings_df['GA/GP'].rank(ascending=False,method='first')/10-1.65 | |
| 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['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']) | |
| schedule_stack['team_opponent'] = list(schedule_df['team_name_home'])+list(schedule_df['team_name']) | |
| schedule_stack['team_abv_home'] = list(schedule_df['team_abv_home'])+list(schedule_df['team_abv']) | |
| schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GF_Rank']],left_on='team_abv',right_on='team_abv',how='inner',suffixes=("",'_y')) | |
| schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GA_Rank']],left_on='team_abv_home',right_on='team_abv',how='inner',suffixes=("",'_y')) | |
| schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GF_Rank']],left_on='team_abv',right_on='team_abv',how='inner',suffixes=("",'_y')) | |
| schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GA_Rank']],left_on='team_abv_home',right_on='team_abv',how='inner',suffixes=("",'_y')) | |
| list_o = schedule_stack.sort_values(['team','date'],ascending=[True,True]).reset_index(drop=True) | |
| new_list = [x - y for x, y in zip(list_o['date'][1:], list_o['date'])] | |
| b2b_list = [0] + [x.days for x in new_list] | |
| b2b_list = [1 if x==1 else 0 for x in b2b_list] | |
| test = list(schedule_stack.groupby(by='date').count()['team']) | |
| offnight = [1 if x<15 else 0 for x in test] | |
| offnight_df = pd.DataFrame({'date':schedule_stack.sort_values('date').date.unique(),'offnight':offnight}).sort_values('date').reset_index(drop=True) | |
| schedule_stack = schedule_stack.merge(right=offnight_df,left_on='date',right_on='date',how='right') | |
| schedule_stack = schedule_stack.sort_values(['team','date'],ascending=[True,True]).reset_index(drop=True) | |
| schedule_stack['b2b'] = b2b_list | |
| schedule_stack.date = pd.to_datetime(schedule_stack.date) | |
| away_b2b = [] | |
| home_b2b = [] | |
| for i in range(0,len(schedule_stack)): | |
| away_b2b.append(schedule_stack[(schedule_stack.date[i]==schedule_stack.date)&(schedule_stack.team_opponent[i]==schedule_stack.team)].reset_index(drop=True)['b2b'][0]) | |
| home_b2b.append(schedule_stack[(schedule_stack.date[i]==schedule_stack.date)&(schedule_stack.team[i]==schedule_stack.team)].reset_index(drop=True)['b2b'][0]) | |
| schedule_stack['away_b2b'] = away_b2b | |
| schedule_stack['home_b2b'] = home_b2b | |
| schedule_stack['away_b2b'] = schedule_stack['away_b2b'].replace(1,' 😴') | |
| schedule_stack['away_b2b'] = schedule_stack['away_b2b'].replace(0,'') | |
| schedule_stack['offnight_score'] = schedule_stack.groupby('date').team.transform('count') | |
| print('we made it',schedule_stack.offnight_score.max()) | |
| schedule_stack.offnight_score = schedule_stack.offnight_score - 16 | |
| #schedule_stack.loc[schedule_stack['offnight_score'] > 0,'offnight_score'] = 0 | |
| schedule_stack.offnight_score = -1*schedule_stack.offnight_score | |
| #print(schedule_stack.offnight_score.max()) | |
| schedule_stack.head() | |
| FontProperties(fname='/System/Library/Fonts/Apple Color Emoji.ttc') | |
| # 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=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() | |
| # 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=400;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() | |
| # print('key_checked') | |
| # total_list = [] | |
| # for x in data_r['fantasy_content']['league']['players']: | |
| # single_list = [] | |
| # single_list.append(int(x['player']['player_id'])) | |
| # single_list.append(int(x['player']['player_ranks'][0]['player_rank']['rank_value'])) | |
| # single_list.append(x['player']['name']['full']) | |
| # single_list.append(x['player']['name']['first']) | |
| # single_list.append(x['player']['name']['last']) | |
| # single_list.append(x['player']['draft_analysis']['average_pick']) | |
| # single_list.append(x['player']['average_auction_cost']) | |
| # single_list.append(x['player']['projected_auction_value']) | |
| # single_list.append(x['player']['display_position']) | |
| # single_list.append(x['player']['editorial_team_abbr']) | |
| # if 'value' in x['player']['percent_owned']: | |
| # single_list.append(x['player']['percent_owned']['value']/100) | |
| # else: | |
| # single_list.append(0) | |
| # total_list.append(single_list) | |
| def get_all_teams(): | |
| url = "https://statsapi.web.nhl.com/api/v1/teams" | |
| response = requests.get(url) | |
| data = response.json() | |
| # Extract team information from the response | |
| teams = [] | |
| for team in data["teams"]: | |
| team_id = team["id"] | |
| team_name = team["name"] | |
| team_abbreviation = team["abbreviation"] | |
| team_city = team["locationName"] | |
| teams.append({"team_id": team_id, "team_name": team_name, "team_abb": team_abbreviation, "team_city": team_city}) | |
| return teams | |
| def get_all_players_teams(): | |
| url = "https://statsapi.web.nhl.com/api/v1/teams?expand=team.roster" | |
| response = requests.get(url) | |
| data = response.json() | |
| # Extract player information from the response | |
| players_teams = [] | |
| for team in data["teams"]: | |
| team_name = team["id"] | |
| #full_name = team["fullName"] | |
| for player in team["roster"]["roster"]: | |
| player_id = player["person"]["id"] | |
| full_name = player["person"]["fullName"] | |
| players_teams.append({"player_id": player_id,"full_name":full_name,"team_id": team_name}) | |
| return players_teams | |
| #df_2023 = pd.DataFrame(data=total_list,columns=['player_id','rank_value','full','first','last','average_pick', 'average_cost','display_position','projected_auction_value','editorial_team_abbr','percent_owned']) | |
| df_2023 = pd.read_csv('df_2023_small.csv',index_col=[0]) | |
| df_2023.percent_owned = df_2023.percent_owned.astype(float) | |
| df_2023.columns = ['player_id','rank_value','full','first','last','average_pick', 'average_cost','display_position','projected_auction_value','editorial_team_abbr','percent_owned','status'] | |
| df_2023.status = df_2023.status.astype(str) | |
| df_2023 = df_2023[df_2023.status.isin(['True','DTD'])] | |
| df_2023.percent_owned = df_2023.percent_owned.astype(float) | |
| print('Jack Hughes',df_2023[df_2023.full == 'Jack Hughes']) | |
| week_dict = yahoo_weeks.set_index('Number')['Week'].sort_index().to_dict() | |
| player_games = pd.read_csv('data/player_games_cards.csv',index_col=[0]).sort_values(by='date').drop_duplicates(subset=['player_id','date']) | |
| team_games = pd.read_csv('data/team_games.csv',index_col=[0]) | |
| team_last = player_games.drop_duplicates(subset=['player_id'],keep='last')[['player_id','Team']].set_index('player_id')['Team'].to_dict() | |
| player_games['Team'] = player_games['player_id'].map(team_last) | |
| # players_df = pd.DataFrame(get_all_players_teams()) | |
| # teams_df = pd.DataFrame(get_all_teams()) | |
| # teams_df.team_name = teams_df.team_name.replace({'Montréal Canadiens':'Montreal Canadiens'}) | |
| # teams_df.team_abb = teams_df.team_name.map(team_abv.set_index('team_name').team_abv.to_dict()) | |
| teams_df = pd.read_csv('teams.csv',index_col=[0]) | |
| team_games = team_games.replace('St. Louis Blues','St Louis Blues') | |
| team_games = team_games.replace('St Louis Blues','St. Louis Blues') | |
| # team_games = team_games.replace('Montréal Canadiens','Montreal Canadiens') | |
| # team_games = team_games.replace('Montreal Canadiens','Montréal Canadiens') | |
| team_games = team_games.merge(teams_df,left_on='team',right_on='team_name') | |
| team_games.team_abv = team_games.team.map(team_abv.set_index('team_name').team_abv.to_dict()) | |
| # players_df = players_df.merge(teams_df) | |
| team_games.team_abb = team_games.team_abv.replace({'LAK':'L.A','SJS':'S.J','NJD':'N.J','TBL':'T.B'}) | |
| yahoo_to_nhl = pd.read_csv("data/yahoo_to_nhl.csv",index_col=[0], encoding='unicode_escape') | |
| #yahoo_df_scrape = pd.read_csv('df_2023_small.csv',index_col=[0]) | |
| yahoo_df_scrape = pd.read_csv('df_2023_small.csv',index_col=[0]) | |
| yahoo_df_scrape.percent_owned = yahoo_df_scrape.percent_owned.astype(float) | |
| yahoo_df_scrape.columns = ['yahoo_id','idx','full','first','last','average_pick','average_auction_cost','projected_auction_value','position','team','percent_owned','status'] | |
| yahoo_df_scrape.status = yahoo_df_scrape.status.astype(str) | |
| yahoo_df_scrape = yahoo_df_scrape[yahoo_df_scrape.status.isin(['True','DTD'])] | |
| #percent_roster_dict = yahoo_df_scrape. | |
| #yahoo_df_scrape = pd.DataFrame(total_list,columns=['yahoo_id','idx','full','first','last','average_pick','average_auction_cost','projected_auction_value','position','team','percent_owned']) | |
| yahoo_final_df = yahoo_df_scrape.merge(right=yahoo_to_nhl,left_on='yahoo_id',right_index=True,how='left',suffixes=['','_x']) | |
| #yahoo_final_df = yahoo_final_df.dropna(subset=['status']) | |
| player_games_dd = player_games.drop_duplicates(['player_id']).reset_index(drop=True) | |
| player_games = player_games.merge(right=yahoo_to_nhl.reset_index(),left_on='player_id',right_on='nhl_id',how='left') | |
| player_games = player_games.merge(yahoo_final_df,left_on='player_id_yahoo',right_on='yahoo_id',how='left') | |
| player_games.loc[player_games.position.isna(),'position'] = player_games.loc[player_games.position.isna(),'Position'] | |
| player_games.position = player_games.position.replace({'L':'LW','R':'RW'}) | |
| print('Jack Hughes',player_games[player_games.Player == 'Jack Hughes'].percent_owned) | |
| player_games.percent_owned = player_games.percent_owned.fillna(0) | |
| player_games = player_games.dropna(subset=['status']) | |
| player_games['S_H_B'] = player_games['Shots'] + player_games['Hits'] + player_games['Shots Blocked'] | |
| player_games = player_games.merge(right=team_games,left_on=['date','Team'],right_on=['date','team_abb'],how='left') | |
| player_games = player_games.fillna(0) | |
| player_games = player_games.drop_duplicates(subset=['player_id','date']) | |
| #player_games = player_games.replace([np.inf, -np.inf], 0, inplace=True) | |
| from shiny import ui, render, App | |
| import matplotlib.image as mpimg | |
| # app_ui = ui.page_fluid( | |
| # # ui.output_plot("plot"), | |
| # #ui.h2('MLB Batter Launch Angle vs Exit Velocity'), | |
| # ui.layout_sidebar( | |
| # ui.panel_sidebar( | |
| # ui.input_select("id", "Select Batter",batter_dict), | |
| # ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'}))) | |
| # , | |
| # ui.panel_main(ui.output_plot("plot",height = "750px",width="1250px")), | |
| # #ui.download_button('test','Download'), | |
| # ) | |
| #import shinyswatch | |
| app_ui = ui.page_fluid( | |
| #shinyswatch.theme.cosmo(), | |
| ui.layout_sidebar( | |
| # Available themes: | |
| # cerulean, cosmo, cyborg, darkly, flatly, journal, litera, lumen, lux, | |
| # materia, minty, morph, pulse, quartz, sandstone, simplex, sketchy, slate, | |
| # solar, spacelab, superhero, united, vapor, yeti, zephyr | |
| ui.panel_sidebar( | |
| ui.input_select("week_id", "Select Week (Set as Season for Custom Date Range)",week_dict,width=1), | |
| ui.input_select("sort_id", "Sort Column",['Score','Team','Total','Off-Night','B2B'],width=1), | |
| ui.input_switch("a_d_id", "Ascending?"), | |
| #ui.input_select("date_id", "Select Date",yahoo_weeks['Week'],width=1), | |
| ui.input_date_range("date_range_id", "Date range input",start = datetime.today().date(), end = datetime.today().date() + timedelta(days=6)), | |
| ui.input_date_range("scorer_date_id", "Streamer Stats Date Range",start = max(datetime.today().date()- timedelta(days=21),pd.to_datetime('2023-10-10')), end = datetime.today().date()), | |
| ui.input_numeric('min_games','Min. Games (Streamers)',value=3), | |
| ui.input_numeric('max_head','Number of Rows (Streamers)',value=15), | |
| ui.input_numeric('roster_p','Max Roster Percent% (Streamers)',value=50), | |
| ui.output_table("result"),width=3), | |
| ui.panel_main( | |
| ui.navset_tab( | |
| ui.nav("Schedule", | |
| ui.tags.h3(""), | |
| ui.div({"style": "font-size:2em;"},ui.output_text("txt_title")), | |
| #ui.tags.h2("Fantasy Hockey Schedule Summary"), | |
| ui.tags.h5("Created By: @TJStats, Data: NHL"), | |
| ui.div({"style": "font-size:1.2em;"},ui.output_text("txt")), | |
| ui.output_table("schedule_result"), | |
| ui.tags.h5('Legend'), | |
| ui.output_table("schedule_result_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') ), | |
| ui.nav("Scorers Streamers", | |
| ui.tags.h3(""), | |
| ui.div({"style": "font-size:2.7em;"},ui.output_text("txt_title_streamers")), | |
| ui.tags.h5("Created By: @TJStats, Data: NHL, Natural Stat Trick, Yahoo Fantasy"), | |
| ui.div({"style": "font-size:2em;"},ui.output_text("txt_title_streamers_roster_f")), | |
| ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_f")), | |
| ui.output_table("scorer_streamers_f"), | |
| ui.tags.h3(""), | |
| ui.div({"style": "font-size:2em;"},ui.output_text("txt_title_streamers_roster_d")), | |
| ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_d")), | |
| ui.output_table("scorer_streamers_d"), | |
| ), | |
| ui.nav("Bangers Streamers", | |
| ui.tags.h3(""), | |
| ui.div({"style": "font-size:2.7em;"},ui.output_text("txt_title_streamers_bang")), | |
| ui.tags.h5("Created By: @TJStats, Data: NHL, Natural Stat Trick, Yahoo Fantasy"), | |
| ui.div({"style": "font-size:2em;"},ui.output_text("txt_title_streamers_roster_f_bang")), | |
| ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_f_bang")), | |
| ui.output_table("banger_streamers_f"), | |
| ui.tags.h3(""), | |
| ui.div({"style": "font-size:2em;"},ui.output_text("txt_title_streamers_roster_d_bang")), | |
| ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_d_bang")), | |
| ui.output_table("banger_streamers_d"), | |
| ) | |
| )))) | |
| # ui.row( | |
| # ui.column( | |
| # 3, | |
| # ui.input_date("x", "Date input"),), | |
| # ui.column( | |
| # 1, | |
| # ui.input_select("level_id", "Select Level",level_dict,width=1)), | |
| # ui.column( | |
| # 3, | |
| # ui.input_select("stat_id", "Select Stat",plot_dict_small,width=1)), | |
| # ui.column( | |
| # 2, | |
| # ui.input_numeric("n", "Rolling Window Size", value=50)), | |
| # ), | |
| # ui.output_table("result_batters")), | |
| # ui.nav( | |
| # "Pitchers", | |
| # ui.row( | |
| # ui.column( | |
| # 3, | |
| # ui.input_select("id_pitch", "Select Pitcher",pitcher_dict,width=1,selected=675911), | |
| # ), | |
| # ui.column( | |
| # 1, | |
| # ui.input_select("level_id_pitch", "Select Level",level_dict,width=1)), | |
| # ui.column( | |
| # 3, | |
| # ui.input_select("stat_id_pitch", "Select Stat",plot_dict_small_pitch,width=1)), | |
| # ui.column( | |
| # 2, | |
| # ui.input_numeric("n_pitch", "Rolling Window Size", value=50)), | |
| # ), | |
| # ui.output_table("result_pitchers")), | |
| # ) | |
| # ) | |
| # ) | |
| from urllib.request import Request, urlopen | |
| # importing OpenCV(cv2) module | |
| def server(input, output, session): | |
| def txt(): | |
| week_set = int(input.week_id()) | |
| if week_set != 0: | |
| if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['Start'].values[0]).year != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['End'].values[0]).year: | |
| return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d, %Y")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}' | |
| else: | |
| if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).month != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).month: | |
| return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}' | |
| else: | |
| return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%d, %Y")}' | |
| else: | |
| if input.date_range_id()[0].year != input.date_range_id()[1].year: | |
| return f'{input.date_range_id()[0].strftime("%B %d, %Y")} to {input.date_range_id()[1].strftime("%B %d, %Y")}' | |
| else: | |
| if input.date_range_id()[0].month != input.date_range_id()[1].month: | |
| return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%B %d, %Y")}' | |
| else: | |
| return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%d, %Y")}' | |
| def txt_title(): | |
| week_set = int(input.week_id()) | |
| if week_set != 0: | |
| return f'Fantasy Hockey Schedule Summary - Yahoo - Week {input.week_id()}' | |
| else: | |
| return f'Fantasy Hockey Schedule Summary' | |
| def txt_title_streamers(): | |
| week_set = int(input.week_id()) | |
| if week_set != 0: | |
| return f'Fantasy Hockey Scorers Targets - Week {input.week_id()}' | |
| else: | |
| return f'Fantasy Hockey Scorers Targets' | |
| def txt_title_streamers_bang(): | |
| week_set = int(input.week_id()) | |
| if week_set != 0: | |
| return f'Fantasy Hockey Bangers Targets - Week {input.week_id()}' | |
| else: | |
| return f'Fantasy Hockey Bangers Targets' | |
| def txt_title_streamers_dates_f(): | |
| return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)' | |
| def txt_title_streamers_roster_f(): | |
| return f'Forwards Scorers Targets (<{input.roster_p()/100:.0%} Rostered)' | |
| def txt_title_streamers_roster_d(): | |
| return f'Defense Scorers Targets (<{input.roster_p()/100:.0%} Rostered)' | |
| def txt_title_streamers_roster_f_bang(): | |
| return f'Forwards Bangers Targets (<{input.roster_p()/100:.0%} Rostered)' | |
| def txt_title_streamers_roster_d_bang(): | |
| return f'Defense Bangers Targets (<{input.roster_p()/100:.0%} Rostered)' | |
| def txt_title_streamers_dates_d(): | |
| return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)' | |
| def txt_title_streamers_bang(): | |
| week_set = int(input.week_id()) | |
| if week_set != 0: | |
| return f'Fantasy Hockey Scorers Streamers - Week {input.week_id()}' | |
| else: | |
| return f'Fantasy Hockey Scorers Streamers' | |
| def txt_title_streamers_dates_f_bang(): | |
| return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)' | |
| def txt_title_streamers_dates_d_bang(): | |
| return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)' | |
| def result(): | |
| #print(yahoo_weeks) | |
| return yahoo_weeks[['Week','Start','End']] | |
| def schedule_result(): | |
| week_set = int(input.week_id()) | |
| print(week_set) | |
| if week_set == 0: | |
| start_point = input.date_range_id()[0] | |
| end_point = input.date_range_id()[1] | |
| else: | |
| start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0] | |
| end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0] | |
| sort_value='Score' | |
| ascend=False | |
| weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)] | |
| date_list = pd.date_range(start_point,end_point,freq='d') | |
| test_list = [[]] * len(date_list) | |
| for i in range(0,len(date_list)): | |
| test_list[i] = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left') | |
| test_list[i] = test_list[i].fillna("") | |
| test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b'] | |
| test_df = pd.DataFrame() | |
| test_df['Team'] = list(team_abv['team_abv']) | |
| test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv'] | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score'] | |
| test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b'] | |
| gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank']) | |
| ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank']) | |
| #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))]) | |
| games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b']) | |
| print('test_df') | |
| print(test_df) | |
| #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| test_df['Score'] = team_score | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight'] | |
| cols = test_df.columns.tolist(); | |
| L = len(cols) | |
| test_df = test_df[cols[4:]+cols[0:4]] | |
| #return test_df#[cols[4:]+cols[0:4]] | |
| test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend) | |
| for i in range(0,len(date_list)): | |
| test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month).zfill(2)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text'] | |
| row = ['']*L | |
| for x in test_df[test_df.columns[L:]]: | |
| row.append(int(sum(test_df[x]!=" ")/2)) | |
| test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id()) | |
| test_df.loc[32] = row | |
| #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) | |
| offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]] | |
| test_df.style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list]))) | |
| test_df_style = test_df.style.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':'75px'},overwrite=False).set_properties( | |
| **{'background-color':'White','index':'White','min-width':'100px'},overwrite=False,subset = ((list(test_df.index[:]),test_df.columns[5:]))).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', '20px')]}],overwrite=False).set_properties( | |
| **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).hide_index() | |
| test_df_style = test_df_style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list]))) | |
| test_df_style = test_df_style.applymap(highlight_cells) | |
| test_df_style = test_df_style.background_gradient(cmap=cmap_total,subset = ((list(test_df.index[:-1]),test_df.columns[0]))) | |
| test_df_style = test_df_style.background_gradient(cmap=cmap_total,vmin=0,vmax=np.max(test_df.Total[:len(test_df)-1]),subset = ((list(test_df.index[:-1]),test_df.columns[2]))) | |
| test_df_style = test_df_style.background_gradient(cmap=cmap_off,subset = ((list(test_df.index[:-1]),test_df.columns[3]))) | |
| test_df_style = test_df_style.background_gradient(cmap=cmap_back,subset = ((list(test_df.index[:-1]),test_df.columns[4]))) | |
| test_df_style = test_df_style.background_gradient(cmap=cmap_sum,subset = ((list(test_df.index[-1:]),test_df.columns[L:])),axis=1) | |
| test_df_style = test_df_style.set_properties( | |
| **{'border': '1px black solid !important'},subset = ((list(test_df.index[:-1]),test_df.columns[:]))).set_properties( | |
| **{'min-width':'85px'},subset = ((list(test_df.index[:-1]),test_df.columns[L:])),overwrite=False).set_properties(**{ | |
| 'color': 'black'},overwrite=False).set_properties( | |
| **{'border': '1px black solid !important'},subset = ((list(test_df.index[:]),test_df.columns[L:]))) | |
| test_df_style = test_df_style.format( | |
| '{:.0f}',subset=(test_df.index[:-1],test_df.columns[2:L])) | |
| test_df_style = test_df_style.format( | |
| '{:.1f}',subset=(test_df.index[:-1],test_df.columns[0])) | |
| print('made it to teh end') | |
| return test_df_style | |
| #return exit_velo_df_codes_summ_time_style_set | |
| # @output | |
| # @render.plot(alt="A histogram") | |
| # def plot_pitch(): | |
| # p | |
| def schedule_result_legend(): | |
| off_b2b_df = pd.DataFrame(data={'off':'Off-Night','b2b':'Tired Opp. 😴'},index=[0]) | |
| #off_b2b_df.style.applymap(highlight_cols,subset = ((list(off_b2b_df.index[:-1]),off_b2b_df.columns[0]))) | |
| off_b2b_df_style = off_b2b_df.style.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':'150px'},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', '20px')]}],overwrite=False).set_properties( | |
| **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties( | |
| **{'background-color':'#C2FEE9'},subset=off_b2b_df.columns[0]).set_properties( | |
| **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([ | |
| {'selector': 'thead', 'props': [('display', 'none')]} | |
| ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties( | |
| **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties( | |
| **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{ | |
| 'color': 'black'},overwrite=False).set_properties( | |
| **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))) | |
| return off_b2b_df_style | |
| def scorer_streamers_f(): | |
| week_set = int(input.week_id()) | |
| print(week_set) | |
| if week_set == 0: | |
| start_point = input.date_range_id()[0] | |
| end_point = input.date_range_id()[1] | |
| else: | |
| start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0] | |
| end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0] | |
| sort_value='Score' | |
| ascend=False | |
| weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)] | |
| date_list = pd.date_range(start_point,end_point,freq='d') | |
| test_list = [[]] * len(date_list) | |
| for i in range(0,len(date_list)): | |
| test_list[i] = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left') | |
| test_list[i] = test_list[i].fillna("") | |
| test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b'] | |
| test_df = pd.DataFrame() | |
| test_df['Team'] = list(team_abv['team_abv']) | |
| test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv'] | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score'] | |
| test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b'] | |
| gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank']) | |
| ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank']) | |
| #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))]) | |
| games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b']) | |
| print('test_df') | |
| print(test_df) | |
| #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| test_df['Score'] = team_score | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight'] | |
| cols = test_df.columns.tolist(); | |
| L = len(cols) | |
| test_df = test_df[cols[4:]+cols[0:4]] | |
| #return test_df#[cols[4:]+cols[0:4]] | |
| test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend) | |
| for i in range(0,len(date_list)): | |
| test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text'] | |
| row = ['']*L | |
| for x in test_df[test_df.columns[L:]]: | |
| row.append(int(sum(test_df[x]!=" ")/2)) | |
| test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id()) | |
| test_df.loc[32] = row | |
| #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) | |
| offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]] | |
| start_date = input.scorer_date_id()[0] | |
| end_date = input.scorer_date_id()[1] | |
| gp_min = input.min_games() | |
| df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))] | |
| team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.75]['Team'].values | |
| if(len(team_top)) < 5: | |
| team_top = test_df.Team.values[:5] | |
| df_dated_score_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg( | |
| GP = ('GP','sum'), | |
| TOI = ('TOI','sum'), | |
| Goals = ('Goals','sum'), | |
| ixG = ('ixG','sum'), | |
| Assists = ('Total Assists','sum'), | |
| Points = ('Total Points','sum'), | |
| Shots = ('Shots','sum'), | |
| PP_Points = ('Total Points_pp','sum'), | |
| PP_toi = ('TOI_pp','sum'), | |
| team_pp = ('pp_toi','sum'), | |
| )#.reset_index() | |
| df_dated_score_group = df_dated_score_group[(df_dated_score_group.GP >= gp_min)] | |
| df_dated_score_group[df_dated_score_group.columns[1:]] = df_dated_score_group[df_dated_score_group.columns[1:]].divide(df_dated_score_group.GP,axis=0) | |
| df_dated_score_group['PP_percent'] = df_dated_score_group.PP_toi / df_dated_score_group.team_pp | |
| df_dated_score_group = df_dated_score_group.reset_index() | |
| df_dated_score_group = df_dated_score_group.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team']) | |
| df_dated_score_group = df_dated_score_group[(df_dated_score_group.Team.isin(team_top))&(df_dated_score_group.percent_owned <= input.roster_p()/100 )] | |
| df_dated_score_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_score_group['TOI'].astype(float)] | |
| df_dated_score_group_table = df_dated_score_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI', | |
| 'Goals', 'ixG', 'Assists', 'Points', 'Shots', 'PP_Points', 'PP_percent','Off-Night']].sort_values(['Points','Off-Night','PP_percent','Goals','Shots'],ascending=False) | |
| df_dated_score_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP', | |
| 'Goals/GP', 'ixG/GP','Assists/GP', 'Points/GP', 'Shots/GP', 'PPP/GP', 'PP%','Off-Night'] | |
| return df_dated_score_group_table[df_dated_score_group_table.Position != 'D'].head(input.max_head()).style.background_gradient( | |
| cmap=cmap_off, subset=['Points/GP']).background_gradient(cmap=cmap_total, subset=['Roster%']).background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_score_group_table['Off-Night'].max()).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'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format( | |
| {'Roster%': '{:.0%}','GP': '{:.0f}','Goals/GP': '{:.2f}','ixG/GP': '{:.2f}','Assists/GP': '{:.2f}', | |
| 'Points/GP': '{:.2f}','Shots/GP': '{:.2f}','PPP/GP': '{:.2f}','PP%': '{:.0%}','Off-Night': '{:.0f}'},).set_properties( | |
| **{'border': '1px black solid !important'}).set_properties( | |
| **{'min-width':'175px'},subset = ((df_dated_score_group_table.columns[0])),overwrite=False).set_properties( | |
| **{'min-width':'50px'},subset = ((df_dated_score_group_table.columns[1:])),overwrite=False) | |
| def scorer_streamers_d(): | |
| week_set = int(input.week_id()) | |
| print(week_set) | |
| if week_set == 0: | |
| start_point = input.date_range_id()[0] | |
| end_point = input.date_range_id()[1] | |
| else: | |
| start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0] | |
| end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0] | |
| sort_value='Score' | |
| ascend=False | |
| weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)] | |
| date_list = pd.date_range(start_point,end_point,freq='d') | |
| test_list = [[]] * len(date_list) | |
| for i in range(0,len(date_list)): | |
| test_list[i] = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left') | |
| test_list[i] = test_list[i].fillna("") | |
| test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b'] | |
| test_df = pd.DataFrame() | |
| test_df['Team'] = list(team_abv['team_abv']) | |
| test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv'] | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score'] | |
| test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b'] | |
| gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank']) | |
| ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank']) | |
| #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))]) | |
| games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b']) | |
| print('test_df') | |
| print(test_df) | |
| #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| test_df['Score'] = team_score | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight'] | |
| cols = test_df.columns.tolist(); | |
| L = len(cols) | |
| test_df = test_df[cols[4:]+cols[0:4]] | |
| #return test_df#[cols[4:]+cols[0:4]] | |
| test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend) | |
| for i in range(0,len(date_list)): | |
| test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text'] | |
| row = ['']*L | |
| for x in test_df[test_df.columns[L:]]: | |
| row.append(int(sum(test_df[x]!=" ")/2)) | |
| test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id()) | |
| test_df.loc[32] = row | |
| #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) | |
| offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]] | |
| start_date = input.scorer_date_id()[0] | |
| end_date = input.scorer_date_id()[1] | |
| gp_min = input.min_games() | |
| df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))] | |
| team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.75]['Team'].values | |
| if(len(team_top)) < 5: | |
| team_top = test_df.Team.values[:5] | |
| df_dated_score_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg( | |
| GP = ('GP','sum'), | |
| TOI = ('TOI','sum'), | |
| Goals = ('Goals','sum'), | |
| ixG = ('ixG','sum'), | |
| Assists = ('Total Assists','sum'), | |
| Points = ('Total Points','sum'), | |
| Shots = ('Shots','sum'), | |
| PP_Points = ('Total Points_pp','sum'), | |
| PP_toi = ('TOI_pp','sum'), | |
| team_pp = ('pp_toi','sum'), | |
| )#.reset_index() | |
| df_dated_score_group = df_dated_score_group[(df_dated_score_group.GP >= gp_min)] | |
| df_dated_score_group[df_dated_score_group.columns[1:]] = df_dated_score_group[df_dated_score_group.columns[1:]].divide(df_dated_score_group.GP,axis=0) | |
| df_dated_score_group['PP_percent'] = df_dated_score_group.PP_toi / df_dated_score_group.team_pp | |
| df_dated_score_group = df_dated_score_group.reset_index() | |
| df_dated_score_group = df_dated_score_group.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team']) | |
| df_dated_score_group = df_dated_score_group[(df_dated_score_group.Team.isin(team_top))&(df_dated_score_group.percent_owned <= input.roster_p()/100 )] | |
| df_dated_score_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_score_group['TOI'].astype(float)] | |
| df_dated_score_group_table = df_dated_score_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI', | |
| 'Goals', 'ixG', 'Assists', 'Points', 'Shots', 'PP_Points', 'PP_percent','Off-Night']].sort_values(['Points','Off-Night','PP_percent','Goals','Shots'],ascending=False) | |
| df_dated_score_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP', | |
| 'Goals/GP', 'ixG/GP','Assists/GP', 'Points/GP', 'Shots/GP', 'PPP/GP', 'PP%','Off-Night'] | |
| return df_dated_score_group_table[df_dated_score_group_table.Position == 'D'].head(input.max_head()) \ | |
| .style.background_gradient(cmap=cmap_off, subset=['Points/GP']) \ | |
| .background_gradient(cmap=cmap_total, subset=['Roster%']) \ | |
| .background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_score_group_table['Off-Night'].max()) \ | |
| .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'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format( | |
| {'Roster%': '{:.0%}','GP': '{:.0f}','Goals/GP': '{:.2f}','ixG/GP': '{:.2f}','Assists/GP': '{:.2f}', | |
| 'Points/GP': '{:.2f}','Shots/GP': '{:.2f}','PPP/GP': '{:.2f}','PP%': '{:.0%}','Off-Night': '{:.0f}'},).set_properties( | |
| **{'border': '1px black solid !important'}).set_properties( | |
| **{'min-width':'175px'},subset = ((df_dated_score_group_table.columns[0])),overwrite=False).set_properties( | |
| **{'min-width':'50px'},subset = ((df_dated_score_group_table.columns[1:])),overwrite=False) | |
| def banger_streamers_f(): | |
| week_set = int(input.week_id()) | |
| print(week_set) | |
| if week_set == 0: | |
| start_point = input.date_range_id()[0] | |
| end_point = input.date_range_id()[1] | |
| else: | |
| start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0] | |
| end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0] | |
| sort_value='Score' | |
| ascend=False | |
| weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)] | |
| date_list = pd.date_range(start_point,end_point,freq='d') | |
| test_list = [[]] * len(date_list) | |
| for i in range(0,len(date_list)): | |
| test_list[i] = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left') | |
| test_list[i] = test_list[i].fillna("") | |
| test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b'] | |
| test_df = pd.DataFrame() | |
| test_df['Team'] = list(team_abv['team_abv']) | |
| test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv'] | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score'] | |
| test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b'] | |
| gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank']) | |
| ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank']) | |
| #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))]) | |
| games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b']) | |
| print('test_df') | |
| print(test_df) | |
| #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| test_df['Score'] = team_score | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight'] | |
| cols = test_df.columns.tolist(); | |
| L = len(cols) | |
| test_df = test_df[cols[4:]+cols[0:4]] | |
| #return test_df#[cols[4:]+cols[0:4]] | |
| test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend) | |
| for i in range(0,len(date_list)): | |
| test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text'] | |
| row = ['']*L | |
| for x in test_df[test_df.columns[L:]]: | |
| row.append(int(sum(test_df[x]!=" ")/2)) | |
| test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id()) | |
| test_df.loc[32] = row | |
| #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) | |
| offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]] | |
| start_date = input.scorer_date_id()[0] | |
| end_date = input.scorer_date_id()[1] | |
| gp_min = input.min_games() | |
| df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))] | |
| team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.75]['Team'].values | |
| if(len(team_top)) < 5: | |
| team_top = test_df.Team.values[:5] | |
| df_dated_bangers_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg( | |
| GP = ('GP','sum'), | |
| TOI = ('TOI','sum'), | |
| Goals = ('Goals','sum'), | |
| Assists = ('Total Assists','sum'), | |
| Points = ('Total Points','sum'), | |
| Shots = ('Shots','sum'), | |
| Hits = ('Hits','sum'), | |
| Blocks = ('Shots Blocked','sum'), | |
| S_H_B = ('S_H_B','sum'), | |
| )#.reset_index() | |
| df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.GP >= gp_min)] | |
| df_dated_bangers_group[df_dated_bangers_group.columns[1:]] = df_dated_bangers_group[df_dated_bangers_group.columns[1:]].divide(df_dated_bangers_group.GP,axis=0) | |
| #df_dated_bangers_group['PP_percent'] = df_dated_bangers_group.PP_toi / df_dated_bangers_group.team_pp | |
| df_dated_bangers_group = df_dated_bangers_group.reset_index() | |
| df_dated_bangers_group = df_dated_bangers_group.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team']) | |
| df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.Team.isin(team_top))&(df_dated_bangers_group.percent_owned <= input.roster_p()/100 )] | |
| df_dated_bangers_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_bangers_group['TOI'].astype(float)] | |
| df_dated_bangers_group_table = df_dated_bangers_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI', | |
| 'Goals', 'Assists', 'Points', 'Shots', 'Hits', 'Blocks','S_H_B','Off-Night']].sort_values(['S_H_B','Off-Night','Shots','Hits','Blocks','Points'],ascending=False) | |
| df_dated_bangers_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP', | |
| 'Goals/GP', 'Assists/GP', 'Points/GP', 'Shots/GP', 'Hits/GP', 'Blocks/GP','S+H+B/GP','Off-Night'] | |
| #df_dated_bangers_group_table.head(15) | |
| return df_dated_bangers_group_table[df_dated_bangers_group_table.Position != 'D'] \ | |
| .head(input.max_head()).style.background_gradient(cmap=cmap_off, subset=['S+H+B/GP'])\ | |
| .background_gradient(cmap=cmap_total, subset=['Roster%'])\ | |
| .background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_bangers_group_table['Off-Night'].max())\ | |
| .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'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format( | |
| {'Roster%': '{:.0%}','GP': '{:.0f}','Goals/GP': '{:.2f}','Assists/GP': '{:.2f}','Points/GP': '{:.2f}','PIM/GP': '{:.2f}','Shots/GP': '{:.2f}','Hits/GP': '{:.2f}','Blocks/GP': '{:.2f}','S+H+B/GP': '{:.2f}','Off-Night': '{:.0f}'},).set_properties( | |
| **{'border': '1px black solid !important'}).set_properties( | |
| **{'min-width':'175px'},subset = ((df_dated_bangers_group_table.columns[0])),overwrite=False).set_properties( | |
| **{'min-width':'50px'},subset = ((df_dated_bangers_group_table.columns[1:])),overwrite=False) | |
| def banger_streamers_d(): | |
| week_set = int(input.week_id()) | |
| print(week_set) | |
| if week_set == 0: | |
| start_point = input.date_range_id()[0] | |
| end_point = input.date_range_id()[1] | |
| else: | |
| start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0] | |
| end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0] | |
| sort_value='Score' | |
| ascend=False | |
| weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)] | |
| date_list = pd.date_range(start_point,end_point,freq='d') | |
| test_list = [[]] * len(date_list) | |
| for i in range(0,len(date_list)): | |
| test_list[i] = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left') | |
| test_list[i] = test_list[i].fillna("") | |
| test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b'] | |
| test_df = pd.DataFrame() | |
| test_df['Team'] = list(team_abv['team_abv']) | |
| test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv'] | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score'] | |
| test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b'] | |
| gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank']) | |
| ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank']) | |
| #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))]) | |
| games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b']) | |
| print('test_df') | |
| print(test_df) | |
| #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1 | |
| test_df['Score'] = team_score | |
| test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight'] | |
| cols = test_df.columns.tolist(); | |
| L = len(cols) | |
| test_df = test_df[cols[4:]+cols[0:4]] | |
| #return test_df#[cols[4:]+cols[0:4]] | |
| test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend) | |
| for i in range(0,len(date_list)): | |
| test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text'] | |
| row = ['']*L | |
| for x in test_df[test_df.columns[L:]]: | |
| row.append(int(sum(test_df[x]!=" ")/2)) | |
| test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id()) | |
| test_df.loc[32] = row | |
| #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) | |
| offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]] | |
| start_date = input.scorer_date_id()[0] | |
| end_date = input.scorer_date_id()[1] | |
| gp_min = input.min_games() | |
| df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))] | |
| team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.75]['Team'].values | |
| if(len(team_top)) < 5: | |
| team_top = test_df.Team.values[:5] | |
| df_dated_bangers_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg( | |
| GP = ('GP','sum'), | |
| TOI = ('TOI','sum'), | |
| Goals = ('Goals','sum'), | |
| Assists = ('Total Assists','sum'), | |
| Points = ('Total Points','sum'), | |
| Shots = ('Shots','sum'), | |
| Hits = ('Hits','sum'), | |
| Blocks = ('Shots Blocked','sum'), | |
| S_H_B = ('S_H_B','sum'), | |
| )#.reset_index() | |
| df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.GP >= gp_min)] | |
| df_dated_bangers_group[df_dated_bangers_group.columns[1:]] = df_dated_bangers_group[df_dated_bangers_group.columns[1:]].divide(df_dated_bangers_group.GP,axis=0) | |
| #df_dated_bangers_group['PP_percent'] = df_dated_bangers_group.PP_toi / df_dated_bangers_group.team_pp | |
| df_dated_bangers_group = df_dated_bangers_group.reset_index() | |
| df_dated_bangers_group = df_dated_bangers_group.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team']) | |
| df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.Team.isin(team_top))&(df_dated_bangers_group.percent_owned <= input.roster_p()/100 )] | |
| df_dated_bangers_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_bangers_group['TOI'].astype(float)] | |
| df_dated_bangers_group_table = df_dated_bangers_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI', | |
| 'Goals', 'Assists', 'Points', 'Shots', 'Hits', 'Blocks','S_H_B','Off-Night']].sort_values(['S_H_B','Off-Night','Shots','Hits','Blocks','Points'],ascending=False) | |
| df_dated_bangers_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP', | |
| 'Goals/GP', 'Assists/GP', 'Points/GP', 'Shots/GP', 'Hits/GP', 'Blocks/GP','S+H+B/GP','Off-Night'] | |
| #df_dated_bangers_group_table.head(15) | |
| return df_dated_bangers_group_table[df_dated_bangers_group_table.Position == 'D']\ | |
| .head(input.max_head()).style.background_gradient(cmap=cmap_off, subset=['S+H+B/GP'])\ | |
| .background_gradient(cmap=cmap_total, subset=['Roster%'])\ | |
| .background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_bangers_group_table['Off-Night'].max())\ | |
| .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'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format( | |
| {'Roster%': '{:.0%}','GP': '{:.0f}','Goals/GP': '{:.2f}','Assists/GP': '{:.2f}','Points/GP': '{:.2f}','PIM/GP': '{:.2f}','Shots/GP': '{:.2f}','Hits/GP': '{:.2f}','Blocks/GP': '{:.2f}','S+H+B/GP': '{:.2f}','Off-Night': '{:.0f}'},).set_properties( | |
| **{'border': '1px black solid !important'}).set_properties( | |
| **{'min-width':'175px'},subset = ((df_dated_bangers_group_table.columns[0])),overwrite=False).set_properties( | |
| **{'min-width':'50px'},subset = ((df_dated_bangers_group_table.columns[1:])),overwrite=False) | |
| app = App(app_ui, server) |