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')