print('Running') import time 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 time #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 matplotlib.ticker import MaxNLocator from matplotlib.patches import Ellipse import matplotlib.transforms as transforms from matplotlib.gridspec import GridSpec 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 datetime import timedelta # import dataframe_image as dfi # from google.colab import drive def percentile(n): def percentile_(x): return np.percentile(x, n) percentile_.__name__ = 'percentile_%s' % n return percentile_ # import os # import praw # import matplotlib.pyplot as plt # import matplotlib.colors # import matplotlib.colors as mcolors # cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285f4","#FFFFFF","#F0E442"]) #import pybaseball import math # import matplotlib.ticker as mtick # import matplotlib.ticker as ticker # colour_palette = ['#FFB000','#648FFF','#785EF0', # '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED'] # import matplotlib.colors as mcolors # from matplotlib.ticker import FuncFormatter # from matplotlib.font_manager import FontProperties 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"]) co = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#56B4E9","#FFFFFF","#F0E442"]) #co = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#56B4E9"]) # 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']['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) yahoo_df = pd.read_csv('df_2023_small.csv',index_col=[0]) yahoo_df.percent_owned = yahoo_df.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 = 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 = pd.read_csv('df_2023_small.csv',index_col=[0],usecols=range(12)) # yahoo_df.columns = ['rank_value','player_id','full','first','last','average_pick', 'average_cost','display_position','projected_auction_value','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'] season = 20232024 seasontype = 2 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' player_list_all = [] response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') table_rows = soup.findAll('tr') table_rows = table_rows[1:] for j in range(0,len(table_rows)): p_string = [str(x).strip('').strip('" 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('')]) #table_rows[0].findAll('td') 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) 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_old = pd.read_csv('player_games_cards.csv',index_col=[0]).sort_values(by='date',ascending=False) team_games_df = pd.read_csv('team_games.csv',index_col=[0])#.sort_values(by='date',ascending=False) player_games_df_2 = player_games_df_old.copy() 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','')}) df['Faceoffs %'] = df['Faceoffs Won']/(df['Faceoffs Won']+df['Faceoffs Lost']) return df from shiny import ui, render, App import matplotlib.image as mpimg app_ui = ui.page_fluid( #ui.panel_title("Simulate a normal distribution"), ui.layout_sidebar( 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("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') ) ) ), ) from urllib.request import Request, urlopen from shiny import App, reactive, ui from shiny.ui import h2, tags # importing OpenCV(cv2) module #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' @output @render.table def pp_roundup(): 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%'] 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%') start_date ='2023-09-01' end_date = '2024-05-01' player_games_df = player_games_df_2.copy() player_games_df = player_games_df.merge(right=team_games_df,left_on=['Team','date'],right_on=['abv','date']) df_pp_1 = player_games_df.groupby('player_id').head(n_1) df_pp_1 = df_pp_1[df_pp_1.game_count > df_pp_1.max_games-n_1] df_pp_2 = df_pp_2 = player_games_df.groupby('player_id').head(n_2) df_pp_2[df_pp_2.game_count > df_pp_2.max_games-n_2] df_pp_3 = df_pp_3 = player_games_df.groupby('player_id').head(n_3) df_pp_3[df_pp_3.game_count > df_pp_3.max_games-n_3] 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) 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() 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")) 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")) df_final = df_final.merge(team_final,left_on='Team_1',right_on='abv') 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) test = test.merge(right=yahoo_df,left_on='player_id',right_on='nhl_id',suffixes=['','_y'],how='left') # if len(test[test.player_id == 8478427]) > 0: # return test.style #print(test[test.nhl_id == 8478427]) 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) print('Column List') print(test.columns) 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.copy()[(test.Team==input.team_id())].sort_values(by=['L'+str(n_1)+' PP%'],ascending=False).reset_index(drop=True) print(top_d_score) 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)) #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%'],vmin=0,vmax=1).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.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().background_gradient(cmap=co,vmin=0.0,vmax=0.7, subset=[x for x in cols if x.endswith('PP%')]) return df_style_bang # test = test.fillna(0) #test['PP TOI'] = ["%d:%02d" % (int(x),(x*60)%60) if x>0 else '0:00' for x in test['PP TOI']] app = App(app_ui, server) #time.sleep(60)