python 如何将 If-Else 代码转换为 Python？ 返回

如何将 If-Else 代码转换为 Python？


糖果
2025-03-17 08:35:03 (3天前)

我在 Tableau 中编写了一个巨大的 If-Else 代码编译器只需要花费大量时间来执行此代码，因此我想将它移到 Python 上。
我的 df：


  1. match_datetime    country   league         home_team           away_team  predicted_home_score  predicted_away_score  predicted_total_score  predicted_score_difference
    38342  2021-09-15 09:30:00  Australia  FFA Cup         Edge Hill  Gold Coast Knights              1.007927              1.920937               2.928864                    0.913010
    43807  2021-09-21 09:30:00  Australia  FFA Cup  Queensland Lions           Casuarina              3.333684              0.761920               4.095605                    2.571764
    49031  2021-09-26 05:00:00  Australia  FFA Cup    Floreat Athena     Adelaide United              0.688574              2.832026               3.520600                    2.143452
    53094  2021-09-29 10:00:00  Australia  FFA Cup     ECU Joondalup    Adelaide Olympic              2.042965              1.688064               3.731028                    0.354901
    54080  2021-09-29 10:00:00  Australia  FFA Cup     ECU Joondalup    Adelaide Olympic              1.803334              1.554651               3.357985                    0.248683

我有一个 VLOOKUP 表，可以解释这些值以提供输出


  1. df_list:
          Country                   League   Win   DNB  O 1.5  U 4.5
    84  Australia                 A-League  1.45  1.45   3.60    2.2
    85  Australia           A-League Women  1.04  0.65   3.15    2.4
    86  Australia  Brisbane Premier League  1.04  0.65   3.10    2.4
    87  Australia        Capital Territory  1.04  0.65   3.10    2.4
    88  Australia                  FFA Cup  1.49  1.49   3.58    2.4

我的 If-Else 代码简单地说是：


  1. IF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_score_difference”] > df_list[“Win”] AND df[“predicted_total_score”] > df_list[“O 1.5”]
    THEN “W & O 1.5”
    ELSEIF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_score_difference”] > df_list[“Win”]
    THEN “W”
    ELSEIF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_total_score”] > df_list[“O 1.5”]
    THEN “O 1.5”
    ELSEIF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_score_difference”] > df_list[“DNB”] AND df[“predicted_score_difference”] < df_list[“Win”] AND df[“predicted_total_score”] > df_list[“O 1.5”]
    THEN “O 1.5 or DNB”
    ELSEIF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_score_difference”] > df_list[“DNB”] AND df[“predicted_score_difference”] < df_list[“Win”]
    THEN “DNB”
    ELSEIF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_score_difference”] > df_list[“Win”] AND df[“predicted_total_score”] < df_list[“U 4.5”]
    THEN “W & U 4.5”
    ELSEIF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_total_score”] < df_list[“U 4.5”]
    THEN “U 4.5”
    ELSEIF  df[“country”] = df_list[“Country”] AND df[“league”] = df_list[“League”] AND df[“predicted_score_difference”] < df_list[“DNB”]
    THEN “N”

其中 df_output 是结果数据帧
例如对于


  1. match_datetime    country   league         home_team           away_team  predicted_home_score  predicted_away_score  predicted_total_score  predicted_score_difference
    38342  2021-09-15 09:30:00  Australia  FFA Cup         Edge Hill  Gold Coast Knights              1.007927              1.920937               2.928864                    0.913010
    df_output[“result”] 将是 “DNB”

如何在 Python 中编写相同的代码以节省时间？

2 条回复
  1. 1# 糖果 | 2021-12-08 11-58

    像这样？

    1. def func(row):
    2.     if row["predicted_score_difference"] > row["Win"] and row["predicted_total_score"] > row["O 1.5"]:
    3.         return "W & O 1.5" 
    4.     if  row["predicted_score_difference"] > row["Win"]:
    5.         return "W" 
    6.     if  row["predicted_total_score"] > row["O 1.5"]:
    7.         return "O 1.5" 
    8.     if  row["predicted_score_difference"] > row["DNB"] and row["predicted_score_difference"] < row["Win"] and row["predicted_total_score"] > row["O 1.5"]:
    9.         return "O 1.5 or DNB" 
    10.     if  row["predicted_score_difference"] > row["DNB"] and row["predicted_score_difference"] < row["Win"]:
    11.         return "DNB" 
    12.     if  row["predicted_score_difference"] > row["Win"] and row["predicted_total_score"] < row["U 4.5"]:
    13.         return "W & U 4.5" 
    14.     if  row["predicted_total_score"] < row["U 4.5"]:
    15.         return "U 4.5"
    16.     if  row["predicted_score_difference"] < row["DNB"]:
    17.         return "N"
    18. df = df.reset_index().merge(df_list, how="left", left_on=["country", "league"],right_on=["Country", "League"]).set_index('index')
    19. df['result'] = df.apply(func,axis=1)
    20. print(df)

    输出：

    1.       match_datetime    country   league         home_team           away_team  predicted_home_score  predicted_away_score  ...    Country   League   Win   DNB O 1.5  U 4.5     result
    2. index                                                                                                                            ...
    3. 38342 2021-09-15 09:30:00  Australia  FFA Cup         Edge Hill  Gold Coast Knights          1.007927e+06          1.920937e+06  ...  Australia  FFA Cup  1.49  1.49  3.58    2.4      O 1.5
    4. 43807 2021-09-21 09:30:00  Australia  FFA Cup  Queensland Lions           Casuarina          3.333684e+06          7.619200e-01  ...  Australia  FFA Cup  1.49  1.49  3.58    2.4  W & O 1.5
    5. 49031 2021-09-26 05:00:00  Australia  FFA Cup    Floreat Athena     Adelaide United          6.885740e-01          2.832026e+06  ...  Australia  FFA Cup  1.49  1.49  3.58    2.4  W & O 1.5
    6. 53094 2021-09-29 10:00:00  Australia  FFA Cup     ECU Joondalup    Adelaide Olympic          2.042965e+06          1.688064e+06  ...  Australia  FFA Cup  1.49  1.49  3.58    2.4      O 1.5
    7. 54080 2021-09-29 10:00:00  Australia  FFA Cup     ECU Joondalup    Adelaide Olympic
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