Create pandas DataFrame with iterative values

I want to create a pandas DataFrame with five columns: date, house type, neighborhood, realtor and zip code.

A date range of August 1, 2023 to September 1, 2023
There are 4 house types
6 realtors
Only one neighborhood
Only one zipcode So each the date needs to be repeated 16 times for each house type, in this only neighborhood, for each realtor and for one unique zip code. So there would be 16 rows per day.

I can add the consecutive dates to the date column, however, I have trouble in adding the rest of the data, which I think should be nested in for loops. So far, I have these lines:

import pandas as pd
from datetime import date,timedelta
delta = timedelta(days=1)
start = date(2023,8,1)
end=date(2023,9,1)
loop_date = start

df = pd.DataFrame(columns={"Date", "House type", "Neighborhood", "Realtor", "Zipcode"})
while loop_date<=end:
    df = df.append({"Date":loop_date},ignore_index=True)
    loop_date+=delta

The intended output for each day would be the following:

Date	House type	Neighborhood	Realtor	Zipcode
8/1/23	1	a	Alex	10018
8/1/23	2	a	Alex	10018
8/1/23	3	a	Alex	10018
8/1/23	4	a	Alex	10018
8/1/23	1	a	Maggie	10018
8/1/23	2	a	Maggie	10018
8/1/23	3	a	Maggie	10018
8/1/23	4	a	Maggie	10018
8/1/23	1	a	Ginger	10018
8/1/23	2	a	Ginger	10018
8/1/23	3	a	Ginger	10018
8/1/23	4	a	Ginger	10018
8/1/23	1	a	Pat	10018
8/1/23	2	a	Pat	10018
8/1/23	3	a	Pat	10018
8/1/23	4	a	Pat	10018

Any help would be much appreciated, thank you!

Solution

try this:

import pandas as pd
from itertools import product


house_types = [1,2,3,4]
realtors = [*'ABCDEF']
neighborhood = ['a']
zip_code = ['10018']
daily_data = product(house_types, realtors, neighborhood, zip_code)
dates = pd.date_range('2023-8-1', '2023-9-1')
data = product(dates, daily_data)
idx, data = zip(*data)
cols = ["House_type", "Realtor", "Neighborhood", "Zipcode"]
df = pd.DataFrame(data, index=idx, columns=cols).rename_axis('Date')
print(df)
>>>
           House_type   Realtor    Neighborhood  Zipcode
Date                
2023-08-01  1           A               a        10018
2023-08-01  1           B               a        10018
2023-08-01  1           C               a        10018
2023-08-01  1           D               a        10018
2023-08-01  1           E               a        10018
   ...     ...         ...             ...        ...
2023-09-01  4           B               a        10018
2023-09-01  4           C               a        10018
2023-09-01  4           D               a        10018
2023-09-01  4           E               a        10018
2023-09-01  4           F               a        10018