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)
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)
#-----------------------
#accumulating with aggregate scores
df_agg_dk = pd.read_csv(agg_dk)
df_agg_fd = pd.read_csv(agg_fd)
df_dk = pd.merge(df_agg_dk, df_dk, how='left', left_on='Name', right_on = 'dk_name')
df_fd = pd.merge(df_agg_fd, df_fd, how='left', left_on='Name', right_on = 'dk_name')
