How can I translate If-Else code to Python?

I have a huge If-Else code that I write in Tableau The compiler simply takes a lot of time to execute this code so I want to move it onto Python.

My df:

            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
       

I have a VLOOKUP table that Interprets these values to provide an output

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 


     

My If-Else code simply put is:

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"

Where df_output is the resulting dataframe

e.g. for

            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

The df_output[“result”] would be “DNB”

How can I write the same code in Python to save time?

Answer

Like this?

def func(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"
        
df = df.reset_index().merge(df_list, how="left", left_on=["country", "league"],right_on=["Country", "League"]).set_index('index')
df['result'] = df.apply(func,axis=1)
print(df)

output:

           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
index                                                                                                                            ...
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    
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    
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    
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    
54080 2021-09-29 10:00:00  Australia  FFA Cup     ECU Joondalup    Adelaide Olympic          1.803334e+06          1.554651e+06  ...  Australia  FFA Cup  1.49  1.49  3.58    2.4      O 1.5