create a new table from 2 other tables - pandas

Question

If I want to merge the table with 2 other tables b,c
where table a contains columns:( Parent, Style, Ending_Date, WeekNum, Net_Requirment)
tables and calculate how much is required to make product A in a certain date.
The table should like the BOM (Bill of Material)
Can it be applied by pandas?
table b represent the demand for product A per date:
Style Date WeekNum Quantity
A 24/11/2019 0 600
A 01/12/2019 1 500
table c represent Details and quantity used to make product A:
Parent Child Q
A A1 2
A1 A11 3
A1 A12 2
so table a should be filled like this:
Parent Child Date WeekNum Net_Quantity
A A1 24/11/2019 0 1200
A1 A11 24/11/2019 0 3600
A1 A12 24/11/2019 0 2400
A A1 01/12/2019 1 1000
A1 A11 01/12/2019 1 3000
A1 A12 01/12/2019 1 2000

Answer 1

Welcome, in order to properly merge these tables and the rest you would have to have a common key to merge on. What you could do is add said key to each table like this:
data2 = {'Parent':['A','A1','A1'], 'Child':['A1','A11','A12'],
'Q':[2,3,2], 'Style':['A','A','A']}
df2 = pd.DataFrame(data2)
After this you can do a left join on the first table and then you can have multiple rows for the same date. So essentially this:
(notice if you do a left join, your left table will create as many duplicate rows as needed tu suffice the matching key on the right table)
data = {'Style':['A','A'], 'Date':['24/11/2019', '01/12/2019'],
'WeekNum':[0,1], 'Quantity':[600,500]}
df = pd.DataFrame(data)
mergeDf = df.merge(df2,how='left', left_on='Style', right_on='Style')
mergeDf
Then to calculate:
test['Net_Quantity'] = test.Quantity*test.Q
test.drop(['Q'], axis = 1,inplace=True)
result:
Style Date WeekNum Quantity Parent Child Net_Quantity
0 A 24/11/2019 0 600 A A1 1200
1 A 24/11/2019 0 600 A1 A11 1800
2 A 24/11/2019 0 600 A1 A12 1200
3 A 01/12/2019 1 500 A A1 1000
4 A 01/12/2019 1 500 A1 A11 1500
5 A 01/12/2019 1 500 A1 A12 1000