我在 Tableau 中撰寫了一個巨大的 If-Else 代碼編譯器只需要花費大量時間來執行此代碼，因此我想將它移到 Python 上。
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
我有一個 VLOOKUP 表，可以解釋這些值以提供輸出
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 代碼簡單地說是：
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 是結果資料幀
例如對于
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"] 將是 "DNB"
如何在 Python 中撰寫相同的代碼以節省時間？
uj5u.com熱心網友回復：
像這樣？
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)
輸出：
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
uj5u.com熱心網友回復：
你可以做這些轉換：
IF args對if args:，
ELSEIF args對elif args:，
AND對and，
THEN對somevar = ，
arg1 = arg2對arg1 == arg2
基本上這會起作用，但您的條件可以得到極大優化。
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)
uj5u.com熱心網友回復：
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 中轉換為 '==' 并且 '>' 和 '<' 保持不變。“IF”變為“if”，“ELSEIF”變為“elif”。“AND”變成“and”，“THEN”用縮進代替。
轉載請註明出處，本文鏈接：https://www.uj5u.com/net/376265.html