为何某段Pandas代码在测试环境正常，嵌入主代码后更新不完全？

我在PyCharm环境中工作，测试环境运行以下示例代码时，DNB和U_4_5列能正常更新；但将这段代码嵌入主代码后，这两列仅部分行得到更新，其余行未受影响，逻辑一致却无法定位原因。

import pandas as pd

df = pd.read_csv("predicted_no_sel.csv")

df = df.loc[:, ['total_score', 'away_score', 'home_score', 'Win', 'DNB', 'O_1_5', 'U_4_5', 'predicted_score_difference', 'predicted_total_score', 'result', 'predicted_result', 'result_match', 'selection', 'selection_match']]


predicted = df

predicted['Win'] = predicted.apply(lambda x: x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection in ["W", "W & O 1.5"] and x.result != x.predicted_result and x.result != 'Draw' else(x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection == "W & O 1.5" and x.result == 'Draw' else(x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection == "W" and x.result != 'Draw' else(x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection == "W" and x.result == 'Draw' else x.Win))), axis=1)
predicted['DNB'] = predicted.apply(lambda x: x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection == "DNB"else (x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection == "W & O 1.5" and x.result != x.predicted_result and x.result != 'Draw'else (x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection == "W" and x.result != x.predicted_result and x.result != 'Draw'else (x.predicted_score_difference + 0.02 if x.selection_match == "No Match" and x.selection == "DNB" and x.result != x.predicted_result and x.result != 'Draw' else x.DNB))), axis=1)
predicted['O_1_5'] = predicted.apply(lambda x: x.predicted_total_score + 0.02 if x.selection_match == "No Match" and (x.selection == "O 1.5" or x.selection == "W & O 1.5") and x.total_score < 2 else x['O_1_5'], axis=1)
predicted['U_4_5'] = predicted.apply(lambda x: x.predicted_total_score - 0.02 if (x.total_score > 4) and (x.selection == "U 4.5") else x['U_4_5'], axis=1)


# 创建选择函数
def selection(row):
    if row["predicted_score_difference"] > row["Win"] and row["predicted_total_score"] > row["O_1_5"]:
        return "W & O 1.5"
    if row["predicted_score_difference"] > row["Win"]:
        return "W"
    if row["predicted_total_score"] > row["O_1_5"]:
        return "O 1.5"
    if row["predicted_score_difference"] > row["DNB"] and row["predicted_score_difference"] < row["Win"] and row[
        "predicted_total_score"] > row["O_1_5"]:
        return "O 1.5 or DNB"
    if row["predicted_score_difference"] > row["DNB"] and row["predicted_score_difference"] < row["Win"]:
        return "DNB"
    if row["predicted_score_difference"] > row["Win"] and row["predicted_total_score"] < row["U_4_5"]:
        return "W & U 4.5"
    if row["predicted_total_score"] < row["U_4_5"]:
        return "U 4.5"
    if row["predicted_score_difference"] < row["DNB"]:
        return "N"


def selection_match(row):
    if row["selection"] == "N":
        return "No Sel."
    elif (row["home_score"] + row["away_score"]) < 5 and row["selection"] == "U 4.5":
        return "Match"
    elif row["result"] == row["predicted_result"] and row["selection"] == "W":
        return "Match"
    elif row["result"] == row["predicted_result"] and row["total_score"] > 1 and row["selection"] == "W & O 1.5":
        return "Match"
    elif row["total_score"] > 1 and row["selection"] == "O 1.5":
        return "Match"
    elif (row["result"] == row["predicted_result"] or row["result"] == 'Draw') and row["selection"] == "DNB":
        return "Match"
    elif pd.isna(row["home_score"]):  # 已修复
        return "NA"
    else:
        return "No Match"


predicted['selection'] = predicted.apply(selection, axis=1)
predicted['selection_match'] = predicted.apply(selection_match, axis=1)

内容的提问来源于stack exchange，提问作者PyNoob