パンダの2つの列に分割して名前を付ける

Question

こんにちは私はホームリーグまたは離れた列のいずれかに位置する単一のチームを操作したい英語プレミアリーグのスポーツ結果のcsvファイルを持っています。そのチームによってソートされた一連の列を作成し、その結果を以下のように返したいと考えています。私はループでこれを行うことができましたが、パンダの方法を知りたいです。私はチームアーセナルでグループ化しようとしましたが、反対のチームのサブセットを反転する必要がある2つのコラムオプションでは難しいと感じています。

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

あなたが使用することができnumpy.whereダブル：

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 

出力としてSeriesの場合：

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