我有以下数据框：
Race Course Horse Year Month Day Amount Won/Lost
0 Aintree Red Rum 2017 5 12 11.58 won
1 Punchestown Camelot 2016 12 22 122.52 won
2 Sandown Beef of Salmon 2016 11 17 20.00 lost
3 Ayr Corbiere 2016 11 3 25.00 lost
4 Fairyhouse Red Rum 2016 12 2 65.75 won
5 Ayr Camelot 2017 3 11 12.05 won
6 Aintree Hurricane Fly 2017 5 12 11.58 won
7 Punchestown Beef or Salmon 2016 12 22 112.52 won
8 Sandown Aldaniti 2016 11 17 10.00 lost
9 Ayr Henry the Navigator 2016 11 1 15.00 lost
10 Fairyhouse Jumanji 2016 10 2 65.75 won
11 Ayr Came Second 2017 3 11 12.05 won
12 Aintree Murder 2017 5 12 5.00 lost
13 Punchestown King Arthur 2016 6 22 52.52 won
14 Sandown Filet of Fish 2016 11 17 20.00 lost
15 Ayr Denial 2016 11 3 25.00 lost
16 Fairyhouse Don't Gamble 2016 12 12 165.75 won
17 Ayr Ireland 2017 1 11 22.05 won
我正在尝试创建另一个数据框，该数据框仅包含所有种族（行）的总和和所有赢得的种族的总和。理想情况下，外观如下：
total races 18
total won 11
但是，我所能做的就是按计数，对总赢额和总输失进行计数。这是我尝试过的：
df = df.groupby(['Won/Lost']).size().add_prefix('total')
这就是它的返回内容：
Won/Lost
total lost 7
total won 11
dtype: int64
我处于死胡同，无法解决一个简单的解决方案。
答案 0 :(得分：2)
假设races.csv的内容为：
Race Course,Horse,Year,Month,Day,Amount,Won/Lost
Aintree,Red Rum,2017,5,12,11.58,won
Punchestown,Camelot,2016,12,22,122.52,won
Sandown,Beef of Salmon,2016,11,17,20.00,lost
Ayr,Corbiere,2016,11,3,25.00,lost
Fairyhouse,Red Rum,2016,12,2,65.75,won
Ayr,Camelot,2017,3,11,12.05,won
Aintree,Hurricane Fly,2017,5,12,11.58,won
Punchestown,Beef or Salmon,2016,12,22,112.52,won
Sandown,Aldaniti,2016,11,17,10.00,lost
Ayr,Henry the Navigator,2016,11,1,15.00,lost
Fairyhouse,Jumanji,2016,10,2,65.75,won
Ayr,Came Second,2017,3,11,12.05,won
Aintree,Murder,2017,5,12,5.00,lost
Punchestown,King Arthur,2016,6,22,52.52,won
Sandown,Filet of Fish,2016,11,17,20.00,lost
Ayr,Denial,2016,11,3,25.00,lost
Fairyhouse,Don't Gamble,2016,12,12,165.75,won
Ayr,Ireland,2017,1,11,22.05,won
获取新数据框的步骤：
>>> races_df = pd.read_csv('races.csv')
>>> races_df
Race Course Horse Year Month Day Amount Won/Lost
0 Aintree Red Rum 2017 5 12 11.58 won
1 Punchestown Camelot 2016 12 22 122.52 won
2 Sandown Beef of Salmon 2016 11 17 20.00 lost
3 Ayr Corbiere 2016 11 3 25.00 lost
4 Fairyhouse Red Rum 2016 12 2 65.75 won
5 Ayr Camelot 2017 3 11 12.05 won
6 Aintree Hurricane Fly 2017 5 12 11.58 won
7 Punchestown Beef or Salmon 2016 12 22 112.52 won
8 Sandown Aldaniti 2016 11 17 10.00 lost
9 Ayr Henry the Navigator 2016 11 1 15.00 lost
10 Fairyhouse Jumanji 2016 10 2 65.75 won
11 Ayr Came Second 2017 3 11 12.05 won
12 Aintree Murder 2017 5 12 5.00 lost
13 Punchestown King Arthur 2016 6 22 52.52 won
14 Sandown Filet of Fish 2016 11 17 20.00 lost
15 Ayr Denial 2016 11 3 25.00 lost
16 Fairyhouse Don't Gamble 2016 12 12 165.75 won
17 Ayr Ireland 2017 1 11 22.05 won
>>>
>>> total_races = len(races_df)
>>>
>>> total_win = races_df[races_df['Won/Lost'] == 'won']['Won/Lost'].count()
>>>
>>> new_df = pd.DataFrame({'total_races': total_races, 'total_win': total_win}, index=pd.RangeIndex(1))
>>>
>>> new_df
total_races total_win
0 18 11