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

IIUC you can do it this way:

In [63]: dates = pd.date_range(df['date'].min(), df['date'].max())

In [64]: idx = pd.MultiIndex.from_product((dates,
                                           df['customer'].unique(), 
                                           df['product'].unique()))

In [72]: (df.set_index(['date','customer','product'])
            .reindex(idx, fill_value=0)
            .reset_index()
            .set_axis(df.columns, axis=1, inplace=False))
Out[72]:
         date customer product  amt
0  2017-01-01      tim   apple    3
1  2017-01-01      tim   melon    0
2  2017-01-01      tim  orange    0
3  2017-01-01      jim   apple    0
4  2017-01-01      jim   melon    2
5  2017-01-01      jim  orange    0
6  2017-01-01      tom   apple    5
7  2017-01-01      tom   melon    4
8  2017-01-01      tom  orange    0
9  2017-01-02      tim   apple    0
..        ...      ...     ...  ...
26 2017-01-03      tom  orange    0
27 2017-01-04      tim   apple    0
28 2017-01-04      tim   melon    3
29 2017-01-04      tim  orange    0
30 2017-01-04      jim   apple    2
31 2017-01-04      jim   melon    0
32 2017-01-04      jim  orange    0
33 2017-01-04      tom   apple    0
34 2017-01-04      tom   melon    1
35 2017-01-04      tom  orange    4

[36 rows x 4 columns]