Python Pandas用缺少的值填充数据帧

Question

I have this dataframe as an example

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

What I need to do is fill in some missing data with a 0 Billings for each groupby['ISO','Product'] where there is a break in the sequence ie no billings were created in a certain week so it is missing. It needs to be based on the maximum of Billed Week and Minimum of Created Week. ie that is the combinations that should be complete with no break in sequence.

So for the above, the missing records i need to programmatically append into the database are shown below:

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

Answer 1

Here is my solution.I believe some genius will provide better solution~ Let us waiting for it ~

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

Answer 2

Build a MultiIndex with all the gaps in Created Weeks filled and then reindex the original DF.

idx = (df.groupby(['Billed Week'])
       .apply(lambda x: [(x['ISO'].min(),
                          x['Product'].min(),
                          x['Billed Week'].min(),
                          e) for e in range(x['Created Week'].min(), x['Created Week'].max()+1)])
       .tolist()
)

multi_idx = pd.MultiIndex.from_tuples(sum(idx,[]),names=['ISO','Product','Billed Week','Created Week'])

(df.set_index(['ISO','Product','Billed Week','Created Week'])
     .reindex(multi_idx)
     .reset_index()
     .fillna(0)
)

Out[671]: 
  ISO Product  Billed Week  Created Week  Billings
0  DE   Table       201705        201701    1000.0
1  DE   Table       201705        201702    1000.0
2  DE   Table       201705        201703       0.0
3  DE   Table       201705        201704    1000.0
4  DE   Table       201705        201705    1000.0
5  AT   Table       201708        201704    1000.0
6  AT   Table       201708        201705    1000.0
7  AT   Table       201708        201706    1000.0
8  AT   Table       201708        201707       0.0
9  AT   Table       201708        201708    1000.0

Answer 3

def seqfix(x):
    s = x['Created Week']
    x = x.set_index('Created Week')
    x = x.reindex(range(min(s), max(s)+1))
    x['Billings'] = x['Billings'].fillna(0)
    x = x.ffill().reset_index()
    return x

df = df.groupby(['ISO', 'Billed Week']).apply(seqfix).reset_index(drop=True)
df[['Billed Week', 'Billings']] = df[['Billed Week', 'Billings']].astype(int)
df = df[['ISO', 'Product', 'Billed Week', 'Created Week', 'Billings']]

print(df)

  ISO Product  Billed Week  Created Week  Billings
0  AT   Table       201708        201704      1000
1  AT   Table       201708        201705      1000
2  AT   Table       201708        201706      1000
3  AT   Table       201708        201707         0
4  AT   Table       201708        201708      1000
5  DE   Table       201705        201701      1000
6  DE   Table       201705        201702      1000
7  DE   Table       201705        201703         0
8  DE   Table       201705        201704      1000
9  DE   Table       201705        201705      1000