<p>试试这个</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame({'Id': [102,103,104,303,305],'ExpG_Home':[1.8,1.5,1.6,1.8,2.9],
'ExpG_Away':[2.2,1.3,1.2,2.8,0.8],
'HomeG_Time':[[93, 109, 187],[169], [31, 159],[176],[16, 48, 66, 128]],
'AwayG_Time':[[90, 177],[],[],[123,136],[40]]})
x1 = [1,0,0]
x2 = [0,1,0]
x3 = [0,0,1]
k=1
total_timeslot = 200 # number of timeslot per game.
def squared_diff(x1, x2, x3,AwayG_Time,HomeG_Time, y):
ssd = []
for k in range(total_timeslot):
if k in HomeG_Time:
ssd.append(sum((x2 - y) ** 2))
elif k in AwayG_Time:
ssd.append(sum((x3 - y) ** 2))
else:
ssd.append(sum((x1 - y) ** 2))
return ssd
s=pd.DataFrame( pd.concat([df,1-(df['ExpG_Home']+df['ExpG_Away'])/k,df['ExpG_Home']/k,df['ExpG_Away']/k],axis=1).values)
df['res']=s.apply(lambda x: sum(squared_diff(x1,x2,x3,x[0],x[3],np.array([x[5],x[6],x[7]]))),axis=1)
del s
print df
</code></pre>
<p>输出:</p>
<pre><code> AwayG_Time ExpG_Away ExpG_Home HomeG_Time Id res
0 [90, 177] 2.2 1.8 [93, 109, 187] 102 4766.4
1 [] 1.3 1.5 [169] 103 2349.4
2 [] 1.2 1.6 [31, 159] 104 2354.4
3 [123, 136] 2.8 1.8 [176] 303 6411.6
4 [40] 0.8 2.9 [16, 48, 66, 128] 305 4496.2
</code></pre>