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ff1.py
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# Check the captain points by joining the data frames...
import pandas as pd
import json
import requests
from pandas.io.json import json_normalize
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)
from sqlalchemy import create_engine
import pandas as pd
import os
DATABASE_URL = os.environ['DATABASE_URL']
engine = create_engine(DATABASE_URL)
import datetime
now = datetime.datetime.now()
print("I be starting")
print (now.strftime("%Y-%m-%d %H:%M"))
# Kick off by grabbing the bootstrap data - gameweek & player details are in here
# ['assists', 'bonus', 'bps', 'chance_of_playing_next_round', 'chance_of_playing_this_round', 'clean_sheets',
#'code', 'cost_change_event', 'cost_change_event_fall', 'cost_change_start', 'cost_change_start_fall',
#'creativity', 'dreamteam_count', 'ea_index', 'element_type', 'ep_next', 'ep_this', 'event_points',
#'first_name', 'form', 'goals_conceded', 'goals_scored', 'ict_index', 'id', 'in_dreamteam', 'influence',
#'loaned_in', 'loaned_out', 'loans_in', 'loans_out', 'minutes', 'news', 'news_added', 'now_cost', 'own_goals',
#'penalties_missed', 'penalties_saved', 'photo', 'points_per_game', 'red_cards', 'saves', 'second_name',
#'selected_by_percent', 'special', 'squad_number', 'status', 'team', 'team_code', 'threat', 'total_points',
#'transfers_in', 'transfers_in_event', 'transfers_out', 'transfers_out_event', 'value_form', 'value_season', 'web_name', 'yellow_cards']
json_bootstrap = json.loads(requests.get('https://fantasy.premierleague.com/drf/bootstrap').text)
df_elements = json_normalize(json_bootstrap['elements'])
latest_gameweek= json_bootstrap['current-event']
df_events = json_normalize(json_bootstrap['events'])
df_events.to_sql('df_events',engine,if_exists='replace')
df_elements.to_sql('df_elements',engine,if_exists='replace')
# Then get the league data
# ['entry', 'entry_name', 'event_total', 'id', 'last_rank', 'league', 'movement', 'own_entry',
# 'player_name', 'rank', 'rank_sort', 'start_event', 'stop_event', 'total']
json_league_standings = json.loads(requests.get('https://fantasy.premierleague.com/drf/leagues-classic-standings/231600').text)
# results is within standings
df_league_standings = json_normalize(json_league_standings['standings'], 'results')
df_league_standings.to_sql('df_league_standings',engine,if_exists='replace')
df_league_details = json_normalize(json_league_standings['league'])
df_league_details.to_sql('df_league_details',engine,if_exists='replace')
# Now loop through league standings and concatenate a dataframe with entry history in
# ['bank', 'entry', 'event', 'event_transfers', 'event_transfers_cost', 'id', 'movement', 'overall_rank',
# 'points', 'points_on_bench', 'rank', 'rank_sort', 'total_points', 'value']
#
# And a chip data frame
# ['chip', 'entry', 'event', 'name', 'played_time_formatted', 'status', 'time']
df_manager_history = pd.DataFrame()
df_manager_chips = pd.DataFrame()
for ls_index,ls_row in (df_league_standings.iterrows()):
# Get history
json_entry_history = \
json.loads(requests.get('https://fantasy.premierleague.com/drf/entry/' + str(ls_row['entry']) + '/history').text)
if df_manager_history.empty:
df_manager_history = json_normalize(json_entry_history['history'])
else:
df_tmp = json_normalize(json_entry_history['history'])
df_manager_history = pd.concat([df_manager_history, df_tmp], ignore_index=True)
# Get chips
if df_manager_chips.empty:
df_manager_chips = json_normalize(json_entry_history['chips'])
else:
df_tmp = json_normalize(json_entry_history['chips'])
df_manager_chips = pd.concat([df_manager_chips, df_tmp], ignore_index=True)
df_manager_history.to_sql('df_manager_history',engine,if_exists='replace')
df_manager_chips.to_sql('df_manager_chips',engine,if_exists='replace')
print (now.strftime("%Y-%m-%d %H:%M"))
# We can use the last loop to set up the player picks for each week
# element is_captain is_vice_captain multiplier position Entry round
df_manager_history_picks = pd.DataFrame()
for ls_index,ls_row in (df_manager_history.iterrows()):
#print (ls_row['entry'],ls_row['event'])
json_manager_history_picks = \
json.loads(requests.get('https://fantasy.premierleague.com/drf/entry/' \
+ str(ls_row['entry']) \
+ '/event/' \
+ str(ls_row['event']) \
+ '/picks').text)
if df_manager_history_picks.empty:
df_manager_history_picks = json_normalize(json_manager_history_picks['picks'])
df_manager_history_picks['entry']=ls_row['entry']
df_manager_history_picks['round']=ls_row['event']
else:
df_tmp = json_normalize(json_manager_history_picks['picks'])
df_tmp['entry']=ls_row['entry']
df_tmp['round']=ls_row['event']
df_manager_history_picks = pd.concat([df_manager_history_picks, df_tmp], ignore_index=True)
#df_manager_history_picks.to_sql('df_manager_history_picks',engine,if_exists='replace')
now = datetime.datetime.now()
print (now.strftime("%Y-%m-%d %H:%M"))
# For each player and the round they played in - we need their points
# This will be keyed by entry and event
# Avoind picking up the player and gameweek more than once - no nned for that
#['assists', 'attempted_passes', 'big_chances_created', 'big_chances_missed', 'bonus', 'bps', 'clean_sheets',
#'clearances_blocks_interceptions', 'completed_passes', 'creativity', 'dribbles', 'ea_index', 'element',
#'errors_leading_to_goal', 'errors_leading_to_goal_attempt', 'fixture', 'fouls', 'goals_conceded', 'goals_scored',
#'ict_index', 'id', 'influence', 'key_passes', 'kickoff_time', 'kickoff_time_formatted', 'loaned_in', 'loaned_out',
#'minutes', 'offside', 'open_play_crosses', 'opponent_team', 'own_goals', 'penalties_conceded', 'penalties_missed',
#'penalties_saved', 'recoveries', 'red_cards', 'round', 'saves', 'selected', 'tackled', 'tackles', 'target_missed',
#'team_a_score', 'team_h_score', 'threat', 'total_points', 'transfers_balance', 'transfers_in', 'transfers_out',
#'value', 'was_home', 'winning_goals', 'yellow_cards']
df_manager_history_picks_players=pd.DataFrame()
list_of_unique_players=df_manager_history_picks.element.unique()
for element in list_of_unique_players:
json_manager_history_picks_players = \
json.loads(requests.get('https://fantasy.premierleague.com/drf/element-summary/' + str(element) ).text)
if df_manager_history_picks.empty:
df_manager_history_picks_players = json_normalize(json_manager_history_picks_players['history'])
else:
df_tmp = json_normalize(json_manager_history_picks_players['history'])
df_manager_history_picks_players = pd.concat([df_manager_history_picks_players, df_tmp], ignore_index=True )
#df_manager_history_picks_players.to_sql('df_manager_history_picks_players',engine,if_exists='replace')
now = datetime.datetime.now()
print (now.strftime("%Y-%m-%d %H:%M"))
# df_manager_history_with_name
# THis is combo of entry details (name and things) and the history
#['bank', 'entry', 'event', 'event_transfers', 'event_transfers_cost', 'id_x', 'movement_x',
# 'overall_rank', 'points', 'points_on_bench', 'rank_x', 'rank_sort_x', 'total_points', 'value', 'entry_name',
# 'event_total', 'id_y', 'last_rank', 'league', 'movement_y', 'own_entry', 'player_name', 'rank_y',
# 'rank_sort_y', 'start_event', 'stop_event', 'total']
df_manager_history_with_name=pd.merge(df_manager_history, df_league_standings, on='entry')
df_manager_history_with_name.to_sql('df_manager_history_with_name',engine,if_exists='replace')
# df_manager_history_picks_with_names
# Can work out captain points from this = basically each week#s picks for each game week for each manager
#['element', 'is_captain', 'is_vice_captain', 'multiplier', 'position', 'entry',
# 'round', 'assists', 'attempted_passes', 'big_chances_created', 'big_chances_missed', 'bonus',
# 'bps', 'clean_sheets', 'clearances_blocks_interceptions', 'completed_passes', 'creativity',
# 'dribbles', 'ea_index', 'errors_leading_to_goal', 'errors_leading_to_goal_attempt', 'fixture',
# 'fouls', 'goals_conceded', 'goals_scored', 'ict_index', 'id_x', 'influence', 'key_passes', 'kickoff_time',
# 'kickoff_time_formatted', 'loaned_in', 'loaned_out', 'minutes', 'offside', 'open_play_crosses', 'opponent_team',
# 'own_goals', 'penalties_conceded', 'penalties_missed', 'penalties_saved', 'recoveries', 'red_cards', 'saves',
# 'selected', 'tackled', 'tackles', 'target_missed', 'team_a_score', 'team_h_score', 'threat', 'total_points',
# 'transfers_balance', 'transfers_in', 'transfers_out', 'value', 'was_home', 'winning_goals', 'yellow_cards',
# 'entry_name', 'event_total', 'id_y', 'last_rank', 'league', 'movement', 'own_entry', 'player_name', 'rank',
# 'rank_sort', 'start_event', 'stop_event', 'total']
df_tmp=pd.merge(df_manager_history_picks,df_manager_history_picks_players,on=['element','round'])
df_manager_history_picks_with_names=pd.merge(df_tmp,df_league_standings,on='entry')
#df_manager_history_picks_with_names.to_sql('df_manager_history_picks_with_names',engine,if_exists='replace')
# Captains as above but just the captain picks and points doubled = should be tripled for TC chip!
df_captains=(df_manager_history_picks_with_names[df_manager_history_picks_with_names.is_captain==True])
# This merge tells us when the chips were played and factors in 3x chip for captain points.
df_captains = pd.merge(df_captains,df_manager_chips,left_on=['entry','round'],right_on=['entry','event'] ,how='outer')
df_captains['total_captain_point_multiplier'] = np.where(df_captains['name']=='3xc',3,2)
df_captains['total_captain_points'] = np.where(1==1,df_captains['total_points'] * df_captains['total_captain_point_multiplier'],9999)
df_captains.to_sql('df_captains',engine,if_exists='replace')
now = datetime.datetime.now()
print (now.strftime("%Y-%m-%d %H:%M"))
#['chip', 'entry', 'event', 'name', 'played_time_formatted', 'status', 'time',
#'entry_name', 'event_total', 'id', 'last_rank', 'league',
#'movement', 'own_entry', 'player_name', 'rank', 'rank_sort', 'start_event', 'stop_event', 'total']
df_manager_chips_names=pd.merge(df_manager_chips,df_league_standings, on='entry')
df_manager_chips_names.to_sql('df_manager_chips_names',engine,if_exists='replace')
#
# reports
report_transfer_costs=df_manager_history_with_name['event_transfers_cost'].groupby(df_manager_history_with_name['player_name']).sum().reset_index().sort_values(by='event_transfers_cost',ascending=False)
report_transfer_costs.to_sql('report_transfer_costs',engine,if_exists='replace')
report_point_burner=df_manager_history_with_name['points_on_bench'].groupby(df_manager_history_with_name['player_name']).sum().reset_index().sort_values(by='points_on_bench',ascending=False)
report_point_burner.to_sql('report_point_burner',engine,if_exists='replace')
report_captain_points=df_captains['total_points'].groupby(df_captains['player_name']).sum().reset_index().sort_values(by='total_points',ascending=False)
report_captain_points.to_sql('report_captain_points',engine,if_exists='replace')
report_chips_played=df_manager_chips_names[['player_name','name','played_time_formatted','event']].sort_values(by='event')
report_chips_played.to_sql('report_chips_played',engine,if_exists='replace')
#report_point_burner.plot(kind='barh',x='player_name', y='points_on_bench',legend=False,ax=ax1)
#ax1.set_title('Hit Burners\n',fontsize=20)
#ax1.set_ylabel('Manager',fontsize=14)
#ax1.set_xlabel('Points Burned',fontsize=14)
#plt.savefig('/app/hello/static/point_burners.png')
# Run some SQL?