Pandas- adding missing dates to DataFrame while keeping column/index values?

Question

I have a pandas dataframe that incorporates dates, customers, items, and then dollar value for purchases.

   date     customer   product   amt  
 1/1/2017   tim         


        

Answer 1

Notice ,this using the stack and unstack couple of times

df.set_index(['date','customer','product']).amt.unstack(-3).\
  reindex(columns=pd.date_range(df['date'].min(), 
    df['date'].max()),fill_value=0).\
      stack(dropna=False).unstack().stack(dropna=False).\
        unstack('customer').stack(dropna=False).reset_index().\
          fillna(0).sort_values(['level_1','customer','product'])
Out[314]: 
   product    level_1 customer    0
0    apple 2017-01-01      jim  0.0
12   melon 2017-01-01      jim  2.0
24  orange 2017-01-01      jim  0.0
1    apple 2017-01-01      tim  3.0
13   melon 2017-01-01      tim  0.0
25  orange 2017-01-01      tim  0.0
2    apple 2017-01-01      tom  5.0
14   melon 2017-01-01      tom  4.0
26  orange 2017-01-01      tom  0.0
3    apple 2017-01-02      jim  0.0
15   melon 2017-01-02      jim  0.0
27  orange 2017-01-02      jim  0.0
4    apple 2017-01-02      tim  0.0
16   melon 2017-01-02      tim  0.0
28  orange 2017-01-02      tim  0.0
5    apple 2017-01-02      tom  0.0
17   melon 2017-01-02      tom  0.0
29  orange 2017-01-02      tom  0.0
6    apple 2017-01-03      jim  0.0
18   melon 2017-01-03      jim  0.0
30  orange 2017-01-03      jim  0.0
7    apple 2017-01-03      tim  0.0
19   melon 2017-01-03      tim  0.0
31  orange 2017-01-03      tim  0.0
8    apple 2017-01-03      tom  0.0
20   melon 2017-01-03      tom  0.0
32  orange 2017-01-03      tom  0.0
9    apple 2017-01-04      jim  2.0
21   melon 2017-01-04      jim  0.0
33  orange 2017-01-04      jim  0.0
10   apple 2017-01-04      tim  0.0
22   melon 2017-01-04      tim  3.0
34  orange 2017-01-04      tim  0.0
11   apple 2017-01-04      tom  0.0
23   melon 2017-01-04      tom  1.0
35  orange 2017-01-04      tom  4.0