如何将 If-Else 代码转换为 Python？
How can I translate If-Else code to Python?
我在 Tableau 中编写了大量 If-Else 代码
编译器只需要花费大量时间来执行这段代码，所以我想将它移到 Python.
我的 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
我有一个 VLOOKUP table 解释这些值以提供输出
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：
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 是生成的数据帧
例如
对于
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？
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"]:
return "W & O 1.5"
elif df["country"] == df_list["Country"] and df["league"] == df_list["League"] and df["predicted_score_difference"] > df_list["Win"]:
return "W"
...
基本上'='在python中转换为'=='，'>'和'<'保持不变。 'IF' 变为 'if'，'ELSEIF' 变为 'elif'。 'AND' 变为 'and' 并且 'THEN' 被缩进替换。
您可以只进行这些转换：
IF args 到 if args:,
ELSEIF args 至 elif args:，
AND 至 and，
THEN 至 somevar = ，
arg1 = arg2 至 arg1 == arg2
基本上这会起作用，但您的条件可能需要大量优化。
PSD = 'predicted_score_difference'
PTS = 'predicted_total_score'
O = 'O 1.5'
U = 'U 4.5'
out = 'N'
if df["country"] == df_list["Country"] and df["league"] == df_list["League"]:
if df[PSD] > df_list["Win"]:
out = 'W'
elif df[PSD] > df_list["DNB"]:
out = 'DNB'
out = f'{out} {O}' if df[PTS] > df_list[O] or df[PTS] < df_list[U] else f'{out} {U}'
print(out)
像这样？
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)
输出：
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
我在 Tableau 中编写了大量 If-Else 代码 编译器只需要花费大量时间来执行这段代码，所以我想将它移到 Python.
我的 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
我有一个 VLOOKUP table 解释这些值以提供输出
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：
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 是生成的数据帧
例如 对于
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？
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"]:
return "W & O 1.5"
elif df["country"] == df_list["Country"] and df["league"] == df_list["League"] and df["predicted_score_difference"] > df_list["Win"]:
return "W"
...
基本上'='在python中转换为'=='，'>'和'<'保持不变。 'IF' 变为 'if'，'ELSEIF' 变为 'elif'。 'AND' 变为 'and' 并且 'THEN' 被缩进替换。
您可以只进行这些转换：
IF args 到 if args:,
ELSEIF args 至 elif args:，
AND 至 and，
THEN 至 somevar = ，
arg1 = arg2 至 arg1 == arg2
基本上这会起作用，但您的条件可能需要大量优化。
PSD = 'predicted_score_difference'
PTS = 'predicted_total_score'
O = 'O 1.5'
U = 'U 4.5'
out = 'N'
if df["country"] == df_list["Country"] and df["league"] == df_list["League"]:
if df[PSD] > df_list["Win"]:
out = 'W'
elif df[PSD] > df_list["DNB"]:
out = 'DNB'
out = f'{out} {O}' if df[PTS] > df_list[O] or df[PTS] < df_list[U] else f'{out} {U}'
print(out)
像这样？
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)
输出：
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