Python Pandas 用缺失值填充数据框

我有这个数据框作为例子

import pandas as pd

#create dataframe
df = pd.DataFrame([['DE', 'Table',201705,201705, 1000], ['DE', 'Table',201705,201704, 1000],\
                   ['DE', 'Table',201705,201702, 1000], ['DE', 'Table',201705,201701, 1000],\
                   ['AT', 'Table',201708,201708, 1000], ['AT', 'Table',201708,201706, 1000],\
                   ['AT', 'Table',201708,201705, 1000], ['AT', 'Table',201708,201704, 1000]],\
                   columns=['ISO','Product','Billed Week', 'Created Week', 'Billings'])
print (df)

  ISO Product  Billed Week  Created Week  Billings
0  DE   Table       201705        201705      1000
1  DE   Table       201705        201704      1000
2  DE   Table       201705        201702      1000
3  DE   Table       201705        201701      1000
4  AT   Table       201708        201708      1000
5  AT   Table       201708        201706      1000
6  AT   Table       201708        201705      1000
7  AT   Table       201708        201704      1000

我需要做的是用 0 Billings 为每个 groupby['ISO','Product'] 填充一些缺失的数据，其中序列中存在中断，即在某一周内没有创建帐单，因此它丢失了。它需要基于计费周的最大值和创建周的最小值。也就是说，这些组合应该是完整的，顺序上没有中断。

因此，对于上述情况，我需要以编程方式追加到数据库中的缺失记录如下所示：

  ISO Product  Billed Week  Created Week  Billings
0  DE   Table       201705        201703         0
1  AT   Table       201708        201707         0

这是我的解决方案，相信有高手会提供更好的解决方案~让我们拭目以待吧~

df1=df.groupby('ISO').agg({'Billed Week' : np.max,'Created Week' : np.min})
df1['ISO']=df1.index

     Created Week  Billed Week ISO
ISO                               
AT         201704       201708  AT
DE         201701       201705  DE

ISO=[]
BilledWeek=[]
CreateWeek=[]
for i in range(len(df1)):
    BilledWeek.extend([df1.ix[i,1]]*(df1.ix[i,1]-df1.ix[i,0]+1))
    CreateWeek.extend(list(range(df1.ix[i,0],df1.ix[i,1]+1)))
    ISO.extend([df1.ix[i,2]]*(df1.ix[i,1]-df1.ix[i,0]+1))
DF=pd.DataFrame({'BilledWeek':BilledWeek,'CreateWeek':CreateWeek,'ISO':ISO})
Target=DF.merge(df,left_on=['BilledWeek','CreateWeek','ISO'],right_on=['Billed Week','Created Week','ISO'],how='left')
Target.Billings.fillna(0,inplace=True)
Target=Target.drop(['Billed Week',  'Created Week'],axis=1)
Target['Product']=Target.groupby('ISO')['Product'].ffill()

Out[75]: 
   BilledWeek  CreateWeek ISO Product  Billings
0      201708      201704  AT   Table    1000.0
1      201708      201705  AT   Table    1000.0
2      201708      201706  AT   Table    1000.0
3      201708      201707  AT   Table       0.0
4      201708      201708  AT   Table    1000.0
5      201705      201701  DE   Table    1000.0
6      201705      201702  DE   Table    1000.0
7      201705      201703  DE   Table       0.0
8      201705      201704  DE   Table    1000.0
9      201705      201705  DE   Table    1000.0