您好我有一个英国顶级联赛体育赛事结果的csv文件，我想操纵一个位于主场或客场专栏的球队。然后，我希望能够创建一系列由该团队排序的列，然后按以下方式返回结果。我已经能够在一个循环中做到这一点，但很想知道熊猫方法。我试图通过团队军火库进行分组，但我发现很难用两个列选项来做到这一点，我需要翻转对方团队的子集。
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'])
答案 0 :(得分：1)
You can use double 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
输出Series：
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