Calculate months of coverage based on multiple parameters in Pandas - python-3.x

Question

I need to mark the month with 1s when the patient was covered by some product. One dose provides coverage for 1 month. Also i would like to see the gaps in coverage.
Another detail is that quantity may affect months of coverage too. Lets say the quantity is 2, then patient is covered for next 2 months.
Right now I using df.loc which works with the first dose, but can't wrap my mind around how to calculate those gaps in coverage.
df = pd.DataFrame({'patient':['1','2','3','4','5','6','7'], 'dose1':['A','B','B','A','C','C','C'],
'qty1':[1,2,1,4,1,3,4],
'days_since_last_dose1':[0,0,0,0,0,0,0],
'dose2':['B','A','B','A','C','B','C'],
'qty2':[1,2,1,4,1,3,4],
'days_since_last_dose2':[23,56,120,43,30,15,60],
'dose3':['B','B','B','A','A','C','B'],
'qty3':[3,1,1,2,1,3,4],
'days_since_last_dose3':[44,22,67,150,76,32,21], 'M1':[0,0,0,0,0,0,0], 'M2':[0,0,0,0,0,0,0], 'M3':[0,0,0,0,0,0,0], 'M4':[0,0,0,0,0,0,0], 'M5':[0,0,0,0,0,0,0], 'M6':[0,0,0,0,0,0,0], 'M7':[0,0,0,0,0,0,0], 'M8':[0,0,0,0,0,0,0], 'M9':[0,0,0,0,0,0,0], 'M10':[0,0,0,0,0,0,0], 'M11':[0,0,0,0,0,0,0], 'M12':[0,0,0,0,0,0,0]})
prod_1 = ['A']
prod_2 = ['B']
prod_3 = ['C']
df.loc[(df['dose1'].isin(prod_1)) & (df['qty']==1), ('MONTH1')] = 1
For example patient got Dose_1 (qty=1), which got him covered for 30 days, and comes back for Dose_2 (qty=2) 120 days later. Now it should be represented as:
M1 = 1, M2 = 0, M3 = 0, M4 = 0, M5 = 1 (patient came back 120 days after the first dose + double qty), M6 = 1, M7 = 0, M8 = 0 and so on.

Answer 1

welcome to stackoverflow,
for i in range(len(df['patient'])): #for loop to separate each patient in a separate dict
newdict={}
for k,v in df.items():
newdict[str(k)]=v[i]
data.append(newdict)
for log in data: # for loop to add up the total quantities and assign months to 1
for dose in range(log['qty1']+log['qty2']+log['qty3']):
log[f'M{dose+1}']=1
df = pd.DataFrame.from_dict(data)
df = df.to_csv('doses.csv')
i enjoyed figuring this out.
so basically i separated each patient and then added up the qty and put that through a for loop which assigns one to the months within the range of the added up quantities i hope that's what you were aiming for.
Edit:
for i in range(len(df['patient'])):
newdict={}
for k,v in df.items():
newdict[str(k)]=v[i]
data.append(newdict)
for log in data:
for dose in range(1,log['qty1']+1):
log[f'M{dose}']=1
gap = 1 + round((log['days_since_last_dose2']/30)+0.5)
for dose in range(gap,(gap+log['qty2'])):
log[f'M{dose}']+=1
gap1 = 1 + round((log['days_since_last_dose3']/30)+0.5)+gap
for dose in range(gap1,gap1+log['qty3']):
log[f'M{dose}']+=1
ok so i found the algorithm to calculate coverage, 2 indicates overlap in coverage.