def exclude_players(df):
import pandas as pd
import numpy as np
import FF_scoring
import easygui as eg
players = pd.unique(df.Name.ravel()) #getting a list of teams for the week
question = "Select players to exclude"
title = "Select players"
listOfOptions = players
choice = eg.multchoicebox(question , title, listOfOptions)
print choice
cont = raw_input("Exclude these players (if none are showing, there are none selected)? (y/n)")
cont = str(cont)
if cont == 'y':
df = df[~df.Name.isin(choice)] #removing the teams
else:
df = FF_scoring.exclude_players(df)
return df
def which_scores(df):
import pandas as pd
import numpy as np
import FF_scoring
import easygui as eg
scores = df.columns.values
players = pd.unique(df.Name.ravel()) #getting a list of teams for the week
question = "Select scores to include"
title = "Select scores"
listOfOptions = scores
choice = eg.multchoicebox(question , title, listOfOptions)
print choice
cont = raw_input("Include these scores (if none are showing, there are none selected)? (y/n)")
cont = str(cont)
if cont == 'y':
score_list = list(choice) #removing the teams
else:
df = FF_scoring.which_scores(df)
return score_list
for i in range(0, row_num):
# cleaning favorite from "At" and "(London)"
f1 = df_1["player"][i].split(" ")
f = len(f1) - 1
f2 = f1[f]
f3 = f1[0]
df_1["pos"][i] = f2
df_1["player"][i] = f3
i += 1
df_1["Def"] = np.where(df_1["pos"] == pos, 1, 0)
df_1 = df_1[df_1.Def == 1]
df = df_1[["player", "fan_pts"]]
df.to_csv(csv_output)
df = FF_scoring.get_player(csv_output, dk_file, 75)
df = df[~df.dk_name.isin(["NaN"])]
df["fftoday"] = df["fan_pts"]
df["nfl"] = df["fan_pts"]
df["cbs"] = df["fan_pts"]
df["fleaflicker"] = df["fan_pts"]
df["espn"] = df["fan_pts"]
df["fox"] = df["fan_pts"]
df["fire"] = df["fan_pts"]
df["Name"] = df["dk_name"]
df.to_csv(csv_output_dk)
if fd =='1':
#kick_sal = 5000
kick_sal = raw_input("Enter kicker salary: ")
kick_sal = int(kick_sal)
salary = 60000-kick_sal
data_set = "weeks/week-" +week + "/aggregate-week" +week +'fd.csv'
excel_out = "weeks/week-" +week + "/lineups-week" + week + spec +'fd.xlsx'
else:
data_set = "weeks/week-" +week + "/aggregate-week" +week +'dk.csv'
excel_out = "weeks/week-" +week + "/lineups-week" + week + spec + 'dk.xlsx'
#getting the data
df = pd.read_csv(data_set)
df = FF_scoring.exclude_teams(df)
df = FF_scoring.exclude_players(df)
#need a list here
#score_list = ['average']
score_list = FF_scoring.which_scores(df)
#print score_list
#---------
#cont = raw_input("Continue (y/n)?") # for ESPN, this is for the csv_output (auto-refreshes projections)
#cont = str(cont)
#if cont == 'n':
# sys.exit(0)
df = pd.read_csv(csv_output)
i = 0
row_num = len(df)
for i in range(0,row_num):
f1 = df['player'][i].split(',') # split() only once
new_player = f1[0]
df['player'][i] = new_player
i = i+ 1
df.to_csv(csv_output)
#-------------------------------
df_dk = FF_scoring.scoring('final',csv_output)
df_fd = FF_scoring.fd_scoring('final',csv_output)
df_dk_2 = df_dk[(df_dk['final'] > 1)] # cleaning low scores
df_fd_2 = df_fd[(df_fd['final'] > 1)] # cleaning low scores
df_dk_2.to_csv(csv_output_dk)
df_fd_2.to_csv(csv_output_fd)
df_dk = FF_scoring.get_player(csv_output_dk,dk_file,86)
df_fd = FF_scoring.get_player(csv_output_fd,fd_file,86)
df_dk.to_csv(csv_output_dk)
df_fd.to_csv(csv_output_fd)
cols = [ele.text.strip() for ele in cols]
data.append([ele for ele in cols if ele]) # Get rid of empty values
b = open(csv_output, 'w')
a = csv.writer(b)
a.writerows(data),
b.close()
page_num =page_num+1
df = pd.read_csv(csv_output)
df['rec']=0
df['punt_td']=0
df['kick_td']=0
df.to_csv(csv_output)
#--------------------------------------------------
df = FF_scoring.clean(csv_output)
df.replace('-',0, inplace=True)
df['rec']=df['rec_yards']/10 #nfl doesn't do receptions, so I assume a 10 yards/rec
i = 0
row_num = len(df)
for i in range(0,row_num):
f1 = df['player'][i].split(' ') # split() only once
first_name = f1[0]
last_name = f1[1]
new_player = first_name + " " + last_name
df['player'][i] = new_player
i = i+ 1
def optimize_lineup_fd(score,df,mult,max_sal): import pandas as pd import numpy as np import csv from datetime import datetime import FF_scoring import itertools start_time = datetime.now() df_qb = FF_scoring.segment_df(df,score,['QB']) df_rb = FF_scoring.segment_df(df,score,['RB']) df_wr = FF_scoring.segment_df(df,score,['WR']) df_te = FF_scoring.segment_df(df,score,['TE']) df_dst = FF_scoring.segment_df(df,score,['D']) #df_flex = FF_scoring.segment_df(df,score,['RB','WR','TE']) df_qb = FF_scoring.optimal_players(df_qb,score,1) df_rb = FF_scoring.optimal_players(df_rb,score,2) df_wr = FF_scoring.optimal_players(df_wr,score,3) df_te = FF_scoring.optimal_players(df_te,score,1) df_dst = FF_scoring.optimal_players(df_dst,score,1) #df_flex = FF_scoring.optimal_players(df_flex,score,7) #getting three columns df_qb = df_qb[['Name','Salary',score]] df_rb = df_rb[['Name','Salary',score]] df_wr = df_wr[['Name','Salary',score]] df_te = df_te[['Name','Salary',score]] df_dst = df_dst[['Name','Salary',score]] #df_flex = df_flex[['Name','Salary',score]] #flattening RBs print datetime.now() - start_time #doing the combos (use 2 as parameter) rb_num = len(df_rb) #i can probably get rid of this rb = df_rb.values.tolist() tup_rb = itertools.combinations(rb, 2) list_rb = map(list,tup_rb) df_rb = pd.DataFrame(list_rb) #unique to Rb/WR -- extending list df_rb['RB'] = df_rb[0] + df_rb[1] df_rb = df_rb[['RB']] #flatten list rb = df_rb.values.tolist() rb = list(itertools.chain(*rb)) # adding salaries and points r = len(rb) x=0 for x in range(0,r): sal = [rb[x][1] +rb[x][4]] pts = [rb[x][2] +rb[x][5]] rb[x] = rb[x] +sal +pts x +=1 #shortening list df_rb = pd.DataFrame(rb) df_rb = df_rb[[0,3,6,7]] df_rb.columns = ['RB1','RB2','Salary',score] df_rb = FF_scoring.optimal_players(df_rb,score,1) print datetime.now() - start_time #----------------------------------------------- #flattening WRs #doing the combos (use 2 as parameter) wr_num = len(df_wr) #i can probably get rid of this wr = df_wr.values.tolist() tup_wr = itertools.combinations(wr, 3) list_wr = map(list,tup_wr) df_wr = pd.DataFrame(list_wr) #unique to Rb/WR -- extending list df_wr['WR'] = df_wr[0] + df_wr[1] + df_wr[2] df_wr = df_wr[['WR']] #flatten list wr = df_wr.values.tolist() wr = list(itertools.chain(*wr)) # adding salaries and points r = len(wr) x=0 for x in range(0,r): sal = [wr[x][1] +wr[x][4]+wr[x][7]] pts = [wr[x][2] +wr[x][5]+wr[x][8]] wr[x] = wr[x] +sal +pts x +=1 #shortening list df_wr = pd.DataFrame(wr) df_wr = df_wr[[0,3,6,9,10]] df_wr.columns = ['WR1','WR2','WR3','Salary',score] df_wr = FF_scoring.optimal_players(df_wr,score,1) print datetime.now() - start_time #-------------------------------------------------- #getting length of file qb_num = len(df_qb) rb_num = len(df_rb) wr_num = len(df_wr) te_num = len(df_te) dst_num = len(df_dst) #flex_num = len(df_flex) print qb_num print rb_num print wr_num print te_num print dst_num #turning df into list of lists qb = df_qb.values.tolist() rb = df_rb.values.tolist() wr = df_wr.values.tolist() te = df_te.values.tolist() dst = df_dst.values.tolist() #flex = df_flex.values.tolist() #these are the column headings lu = [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] lu_headings = [['QB','QB_sal','QB_score','RB1','RB2','RB_sal','RB_score','WR1','WR2','WR3','WR_sal','WR_score','TE','TE_sal','TE_score','DST','DST_sal','DST_score','salary','score']] q = 0 #for row numbers r1 = 0 #first rb w1 = 0 #first wr t = 0 d = 0 l = 0 # f = 0 max_score = 0 max_sal = max_sal # an input for the function for q in range(0,qb_num): for r1 in range(0,rb_num): for w1 in range(0,wr_num): for t in range(0,te_num): for d in range(0,dst_num): data = [qb[q][0],qb[q][1],qb[q][2],rb[r1][0],rb[r1][1],rb[r1][2],rb[r1][3],wr[w1][0],wr[w1][1],wr[w1][2],wr[w1][3],wr[w1][4],te[t][0],te[t][1],te[t][2],dst[d][0],dst[d][1],dst[d][2],0,0] total_salary = data[1] + data[5] + data[10] + data[13] + data[16] total_score = data[2] + data[6] + data[11] + data[14] + data[17] data[18]=total_salary data[19]=total_score if total_score >= mult* max_score and total_salary <= max_sal: lu.append(data) if total_score > max_score: max_score = total_score l +=1 d +=1 t +=1 print l print datetime.now() - start_time w1 +=1 r1 +=1 q +=1 df = pd.DataFrame(lu, columns=lu_headings[0]) #print df df.sort_values(by='score', ascending=False, inplace=True) df = df.reset_index(drop=True) df = df[['QB','RB1','RB2','WR1','WR2','WR3','TE','DST','salary','score']] df = df.head(250) df = df.transpose() return df
