Find missing number from a sequence in Pandas dataframes

You can convert year with month to datetimes and then add all missing combination with DataFrame.set_index Series.unstack with new 0 for non exist values and DataFrame.stack with Series.reset_index for original format:

df['dates'] = pd.to_datetime(df[['year','month']].assign(day=1))

df = (df.set_index(['uniquecode1','Name', 'dates'])['Sale']
        .unstack(fill_value=0)
        .stack()
        .reset_index(name='Sale'))

print (df.head(10))
    uniquecode1 Name      dates  Sale
0          1029  ABC 2020-04-01     0
1          1029  ABC 2020-05-01    10
2          1029  ABC 2020-06-01    20
3          1029  ABC 2020-07-01     0
4          1029  ABC 2020-08-01     0
5          1029  ABC 2020-10-01    30
6          1029  ABC 2020-11-01    35
7          1029  ABC 2020-12-01    38
8          1029  ABC 2021-01-01     0
9          1029  ABC 2021-02-01     0

Last for add year and months:

df = df.assign(year = df['dates'].dt.year, month = df['dates'].dt.month)
print (df.head())
   uniquecode1 Name      dates  Sale  year  month
0         1029  ABC 2020-04-01     0  2020      4
1         1029  ABC 2020-05-01    10  2020      5
2         1029  ABC 2020-06-01    20  2020      6
3         1029  ABC 2020-07-01     0  2020      7
4         1029  ABC 2020-08-01     0  2020      8

But unfortuantely there missing 09-2020, so is necessary add DataFrame.reindex:

df['dates'] = pd.to_datetime(df[['year','month']].assign(day=1))
mux = pd.date_range(df['dates'].min(), df['dates'].max(), freq='MS', name='dates')

#for add maximum manaully
#mux = pd.date_range(df['dates'].min(), '2021-03-01', freq='MS', name='dates')

df = (df.set_index(['uniquecode1','Name', 'dates'])['Sale']
        .unstack(fill_value=0)
        .reindex(mux, axis=1, fill_value=0)
        .stack()
        .reset_index(name='Sale')
        )

df = df.assign(year = df['dates'].dt.year, month = df['dates'].dt.month)
print (df.head(10))
   uniquecode1 Name      dates  Sale  year  month
0         1029  ABC 2020-04-01     0  2020      4
1         1029  ABC 2020-05-01    10  2020      5
2         1029  ABC 2020-06-01    20  2020      6
3         1029  ABC 2020-07-01     0  2020      7
4         1029  ABC 2020-08-01     0  2020      8
5         1029  ABC 2020-09-01     0  2020      9
6         1029  ABC 2020-10-01    30  2020     10
7         1029  ABC 2020-11-01    35  2020     11
8         1029  ABC 2020-12-01    38  2020     12
9         1029  ABC 2021-01-01     0  2021      1

One option is with the complete function from pyjanitor, which can be helpful in exposing explicitly missing rows (and can be helpful as well in abstracting the reshaping process):

# pip install pyjanitor
import pandas as pd
import janitor

# create date column, combining year and month
df['dates'] = pd.to_datetime(df[['year','month']].assign(day=1))

# build a dictionary for each group
# where the start date is the first date in the group
# and the last date is `2021-02-01`
dates = {'dates': lambda df: pd.date_range(df.min(), '2021-02-01', freq='MS')}

# apply the function, with uniquecode1 and name as the groupby names
# and do some cleanup to get the final output
(df.complete(dates, by=['uniquecode1', 'Name'], sort =True)
   .fillna({'Sale':0}, downcast='infer')
   .assign(year = lambda df: df.dates.dt.year,
           month = lambda df: df.dates.dt.month)
   .drop(columns='dates')
)

    uniquecode1  year  month Name  Sale
0          1029  2020      5  ABC    10
1          1029  2020      6  ABC    20
2          1029  2020      7  ABC     0
3          1029  2020      8  ABC     0
4          1029  2020      9  ABC     0
5          1029  2020     10  ABC    30
6          1029  2020     11  ABC    35
7          1029  2020     12  ABC    38
8          1029  2021      1  ABC     0
9          1029  2021      2  ABC     0
10         1050  2020      4  DEF    39
11         1050  2020      5  DEF    40
12         1050  2020      6  DEF    31
13         1050  2020      7  DEF    45
14         1050  2020      8  DEF    55
15         1050  2020      9  DEF     0
16         1050  2020     10  DEF     0
17         1050  2020     11  DEF     0
18         1050  2020     12  DEF     0
19         1050  2021      1  DEF     0
20         1050  2021      2  DEF     0
21         1079  2020      4  GHI    65
22         1079  2020      5  GHI     0
23         1079  2020      6  GHI     0
24         1079  2020      7  GHI     0
25         1079  2020      8  GHI     0
26         1079  2020      9  GHI     0
27         1079  2020     10  GHI     0
28         1079  2020     11  GHI     0
29         1079  2020     12  GHI     0
30         1079  2021      1  GHI     0
31         1079  2021      2  GHI    75
32        10810  2021      1  XYZ    85
33        10810  2021      2  XYZ     0

There is no 2020 data for 10810 in your sample dataframe, and as such there is none in the final output above.