在大熊貓的兩列中按姓名劃分和追加

Question

您好，我希望操縱位於主場或客場球隊的單個球隊的英格蘭超級聯賽的運動成績的csv文件。然後我希望能夠創建一系列由該團隊排序的列，然後按照下面的結果返回結果。我一直能夠做到這一點，但很想知道熊貓的方法。我曾試圖將球隊分組，但是我發現很難用兩列選項做到這一點，在那裏我需要翻轉對手球隊的子集。

df = pd.read_csv(
    'http://www.football-data.co.uk/mmz4281/1516/E0.csv', 
    sep=',') 

result= df[(df['HomeTeam'] == "Arsenal") | (df.AwayTeam == "Arsenal")] 


for index, row in result.iterrows(): 
    if row['HomeTeam'] == "Arsenal": 
     if row['FTR'] == "H": 
      print ('Win' , 'Home', row['FTHG'], '-', row['FTAG']) 
     elif row['FTR'] == "D": 
      print ('Draw' , 'Home', row['FTHG'], '-', row['FTAG']) 
     else: 
      print ('Lose' , 'Home', row['FTHG'], '-', row['FTAG']) 

# we dont need to put the conditons for else because we know if arsenal are not the home team they must be the away team, 
# this is because we already set out dataframe filter above to show only games where arsenal is home or away, if we didnt 
# do this we would do an elif and then do an improper result print for else 
    else: 
     if row['FTR'] == "H": 
      print ('Win' , 'Home', row['FTHG'], '-', row['FTAG']) 
     elif row['FTR'] == "D": 
      print ('Draw' , 'Home', row['FTHG'], '-', row['FTAG']) 
     else: 
      print ('Lose' , 'Home', row['FTHG'], '-', row['FTAG']) 

Answer 1

您可以使用雙numpy.where：

df = pd.read_csv('http://www.football-data.co.uk/mmz4281/1516/E0.csv') 
#print (df) 

#add copy - http://stackoverflow.com/a/42439031/2901002 
result= df[(df['HomeTeam'] == "Arsenal") | (df.AwayTeam == "Arsenal")].copy() 

#get output to Series, necessary convert numeric to string 
splitted = ' ' + result['FTHG'].astype(str) + ' - ' + result['FTAG'].astype(str) 
#boolean masks 
mask = (result['HomeTeam'] == "Arsenal") 
mask1 = (result['FTR'] == "H") 
mask2 = (result['FTR'] == "D") 

#for new column as output 
result['out'] = np.where(mask & mask1, 'Win home' + splitted, 
       np.where(mask & mask2, 'Draw home' + splitted, 'Lose home' + splitted)) 

print (result[['HomeTeam', 'AwayTeam', 'out']]) 
      HomeTeam  AwayTeam    out 
6   Arsenal  West Ham Lose home 0 - 2 
17 Crystal Palace   Arsenal Lose home 1 - 2 
29   Arsenal  Liverpool Draw home 0 - 0 
35  Newcastle   Arsenal Lose home 0 - 1 
40   Arsenal   Stoke Win home 2 - 0 
52   Chelsea   Arsenal Lose home 2 - 0 
60  Leicester   Arsenal Lose home 2 - 5 
77   Arsenal  Man United Win home 3 - 0 
86   Watford   Arsenal Lose home 0 - 3 
90   Arsenal   Everton Win home 2 - 1 
104   Swansea   Arsenal Lose home 0 - 3 
117   Arsenal  Tottenham Draw home 1 - 1 
127  West Brom   Arsenal Lose home 2 - 1 
137   Norwich   Arsenal Lose home 1 - 1 
140   Arsenal  Sunderland Win home 3 - 1 
156  Aston Villa   Arsenal Lose home 0 - 2 
169   Arsenal  Man City Win home 2 - 1 
176  Southampton   Arsenal Lose home 4 - 0 
180   Arsenal  Bournemouth Win home 2 - 0 
190   Arsenal  Newcastle Win home 1 - 0 
204  Liverpool   Arsenal Lose home 3 - 3 
218   Stoke   Arsenal Lose home 0 - 0 
228   Arsenal   Chelsea Lose home 0 - 1 
230   Arsenal  Southampton Draw home 0 - 0 
248  Bournemouth   Arsenal Lose home 0 - 2 
257   Arsenal  Leicester Win home 2 - 1 
267  Man United   Arsenal Lose home 3 - 2 
273   Arsenal   Swansea Lose home 1 - 2 
284  Tottenham   Arsenal Lose home 2 - 2 
295   Everton   Arsenal Lose home 0 - 2 
303   Arsenal   Watford Win home 4 - 0 
319  West Ham   Arsenal Lose home 3 - 3 
330   Arsenal Crystal Palace Draw home 1 - 1 
338   Arsenal  West Brom Win home 2 - 0 
344  Sunderland   Arsenal Lose home 0 - 0 
346   Arsenal   Norwich Win home 1 - 0 
364  Man City   Arsenal Lose home 2 - 2 
370   Arsenal  Aston Villa Win home 4 - 0 

對於Series作爲輸出：

out = pd.Series(np.where(mask & mask1, 'Win home' + splitted, 
       np.where(mask & mask2, 'Draw home' + splitted, 'Lose home' + splitted)), 
       index=result.index) 

print (out) 
6  Lose home 0 - 2 
17  Lose home 1 - 2 
29  Draw home 0 - 0 
35  Lose home 0 - 1 
40  Win home 2 - 0 
52  Lose home 2 - 0 
60  Lose home 2 - 5 
77  Win home 3 - 0 
86  Lose home 0 - 3 
90  Win home 2 - 1 
104 Lose home 0 - 3 
117 Draw home 1 - 1 
127 Lose home 2 - 1 
137 Lose home 1 - 1 
140  Win home 3 - 1 
156 Lose home 0 - 2 
169  Win home 2 - 1 
176 Lose home 4 - 0 
180  Win home 2 - 0 
190  Win home 1 - 0 
204 Lose home 3 - 3 
218 Lose home 0 - 0 
228 Lose home 0 - 1 
230 Draw home 0 - 0 
248 Lose home 0 - 2 
257  Win home 2 - 1 
267 Lose home 3 - 2 
273 Lose home 1 - 2 
284 Lose home 2 - 2 
295 Lose home 0 - 2 
303  Win home 4 - 0 
319 Lose home 3 - 3 
330 Draw home 1 - 1 
338  Win home 2 - 0 
344 Lose home 0 - 0 
346  Win home 1 - 0 
364 Lose home 2 - 2 
370  Win home 4 - 0 
dtype: object