Argument of type 'Timestamp' is not iterable?

I think you are trying to create a NaN row if the date does not exist in the excel file.

Here's a way to do it. You can use the df.merge option.

I am creating df1 to simulate the excel file. It has two columns sale_dt and sale_amt. If the sale_dt does not exist, then we want to create a separate row with NaN in the columns. To ensure we simulate it, I am creating a date range from 1998-12-31 through 2020-06-23 skipping 4 days in between. So we have a dataframe with 4 missing date between each two rows. The solution should create 4 dummy rows with the correct date in ascending order.

import pandas as pd
import random

#create the sales dataframe with missing dates

df1 = pd.DataFrame({'sale_dt':pd.date_range(start='1998-12-31', end='2020-06-23', freq='5D'),
                    'sale_amt':random.sample(range(1, 2000), 1570)
                })
                
print (df1)

#now create a dataframe with all the dates between '1998-12-31' and '2020-06-23'

df2 = pd.DataFrame({'date':pd.date_range(start='1998-12-31', end='2020-06-23', freq='D')})

print (df2)

#now merge both dataframes with outer join so you get all the rows.
#i am also sorting the data in ascending order so you can see the dates
#also dropping the original sale_dt column and renaming the date column as sale_dt
#then resetting index 

df1 = (df1.merge(df2,left_on='sale_dt',right_on='date',how='outer')
          .drop(columns=['sale_dt'])
          .rename(columns={'date':'sale_dt'})
          .sort_values(by='sale_dt')
          .reset_index(drop=True))


print (df1.head(20))

The original dataframe was:

        sale_dt  sale_amt
0    1998-12-31      1988
1    1999-01-05      1746
2    1999-01-10      1395
3    1999-01-15       538
4    1999-01-20      1186
...         ...       ...
1565 2020-06-03       560
1566 2020-06-08       615
1567 2020-06-13       858
1568 2020-06-18       298
1569 2020-06-23      1427

The output of this will be (first 20 rows):

    sale_amt    sale_dt
0     1988.0 1998-12-31
1        NaN 1999-01-01
2        NaN 1999-01-02
3        NaN 1999-01-03
4        NaN 1999-01-04
5     1746.0 1999-01-05
6        NaN 1999-01-06
7        NaN 1999-01-07
8        NaN 1999-01-08
9        NaN 1999-01-09
10    1395.0 1999-01-10
11       NaN 1999-01-11
12       NaN 1999-01-12
13       NaN 1999-01-13
14       NaN 1999-01-14
15     538.0 1999-01-15
16       NaN 1999-01-16
17       NaN 1999-01-17
18       NaN 1999-01-18
19       NaN 1999-01-19