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

Maybe because of my SQL mindset, consider a left join merge on an expanded helper dataframe:

helper_df_list = [pd.DataFrame({'date': pd.date_range(df['date'].min(), df['date'].max()), 
                                'customer': c, 'product': p }) 
                    for c in df['customer'].unique() 
                            for p in df['product'].unique()]

helper_df = pd.concat(helper_df_list, ignore_index=True)

final_df = pd.merge(helper_df, df, on=['date', 'customer', 'product'], how='left')\
                    .fillna(0).sort_values(['date', 'customer']).reset_index(drop=True)

Output

print(final_df)
#    customer       date product  amt
# 0       jim 2017-01-01   apple  0.0
# 1       jim 2017-01-01   melon  2.0
# 2       jim 2017-01-01  orange  0.0
# 3       tim 2017-01-01   apple  3.0
# 4       tim 2017-01-01   melon  0.0
# 5       tim 2017-01-01  orange  0.0
# 6       tom 2017-01-01   apple  5.0
# 7       tom 2017-01-01   melon  4.0
# 8       tom 2017-01-01  orange  0.0
# 9       jim 2017-01-02   apple  0.0
# 10      jim 2017-01-02   melon  0.0
# 11      jim 2017-01-02  orange  0.0
# 12      tim 2017-01-02   apple  0.0
# 13      tim 2017-01-02   melon  0.0
# 14      tim 2017-01-02  orange  0.0
# 15      tom 2017-01-02   apple  0.0
# 16      tom 2017-01-02   melon  0.0
# 17      tom 2017-01-02  orange  0.0
# 18      jim 2017-01-03   apple  0.0
# 19      jim 2017-01-03   melon  0.0
# 20      jim 2017-01-03  orange  0.0
# 21      tim 2017-01-03   apple  0.0
# 22      tim 2017-01-03   melon  0.0
# 23      tim 2017-01-03  orange  0.0
# 24      tom 2017-01-03   apple  0.0
# 25      tom 2017-01-03   melon  0.0
# 26      tom 2017-01-03  orange  0.0
# 27      jim 2017-01-04   apple  2.0
# 28      jim 2017-01-04   melon  0.0
# 29      jim 2017-01-04  orange  0.0
# 30      tim 2017-01-04   apple  0.0
# 31      tim 2017-01-04   melon  3.0
# 32      tim 2017-01-04  orange  0.0
# 33      tom 2017-01-04   apple  0.0
# 34      tom 2017-01-04   melon  1.0
# 35      tom 2017-01-04  orange  4.0