Using Python and Dynamic Programming algorithm

How do I find the Max return on investment using $1,000,000 to invest

Skip the first 2 rows which is the header row and United States row

The answer should be:

49455.556598018164

['"Nevada"', '"Colorado"', '"Tennessee"', '"Michigan"']

Dataset:

InvestmentName	InvestmentCost	ReturnOnInvestment
United States	226800	0.03015913
California	547700	0.048065
Texas	196100	0.04092943
New York	300400	0.01323088
Florida	233200	0.04162567
Illinois	181100	0.005052
Pennsylvania	173100	0.0136185
Ohio	139100	0.02190591
Michigan	152000	0.04296877
Georgia	188500	0.02801046
North Carolina	185000	0.02277269
New Jersey	327400	0.00550842
Virginia	258800	0.01400381
Washington	387100	0.03917281
Massachusetts	407900	0.03332137
Indiana	144500	0.02654102
Arizona	253300	0.0406834
Tennessee	167300	0.03421969
Missouri	161100	0.01927588
Maryland	289900	0.00955569
Wisconsin	187800	0.02063872
Minnesota	236700	0.03262538
Colorado	380200	0.05724463
Alabama	131200	0.02117276
South Carolina	167000	0.02233585
Louisiana	145900	0.01485257
Kentucky	145400	0.0247173
Oregon	345800	0.04171159
Oklahoma	124000	0.02603059
Connecticut	243700	-0.0013441
Iowa	143200	0.025706
Mississippi	127600	0.01476834
Arkansas	126600	0.02427704
Kansas	140300	0.01893809
Utah	341600	0.03971773
Nevada	293500	0.05258923
New Mexico	195000	0.00910665
West Virginia	96500	0.01716238
Nebraska	165600	0.0342072
Idaho	266400	0.04333478
Hawaii	614100	0.03404135
Maine	232600	0.03022418
New Hampshire	277200	0.02450347
Rhode Island	281700	0.02070541
Montana	235600	0.02603622
Delaware	236600	0.00675307
South Dakota	190100	0.03011093
Alaska	312800	0.01274184
North Dakota	206700	0.04344248
Vermont	202500	0.01095289
District of Columbia	566900	0.04706701
Wyoming	226300	0.02174417

This is what I got but I got the wrong answer. Please help.

1 2 3 import pandas as pd def loadInvestments(file_name) -> list: data = pd.read_csv(file_name)[1:].reset_index(drop=True) data = pd.DataFrame({ 'InvestmentName':data['RegionName'], 'Investment Cost':data['Zhvi'), 'EstimatedReturnOnInvestment':data['10Year']}) 4 5 6 7 return data.values.tolist() 8 9 10 def optimizeInvestments(A, amount): 11 12 # table to income investment_made = [None for i in range(len(A))) 13 14 15 for i in range(len(A)): investment_made[i] = A[i][1] * A[i][2] 16 17 18 investments = None 19 20 roi = 0 21 22 23 24 25 26 27 28 for i in range(len(A)): invested = amount invested-=A[i][1] investment = 0 iny = [A[i][0]] for j in range(i+1, len(A)): if invested >= 0: invested-=A[j][1] investment+investment_made[j] inv.append(A[j][0]) if investment > roi: roi = investment investments = inv 29 30 31 32 33 34 35 36 return(roi, investments) 37 38 39 40 print(optimizeInvestments(loadInvestments('State_Zhvi_Summary_All Homes.csv'), 1000000)) (41865.455897, ['South Dakota', 'Alaska', 'North Dakota', 'Vermont', 'District of Columbia']) 1 2 3 import pandas as pd def loadInvestments(file_name) -> list: data = pd.read_csv(file_name)[1:].reset_index(drop=True) data = pd.DataFrame({ 'InvestmentName':data['RegionName'], 'Investment Cost':data['Zhvi'), 'EstimatedReturnOnInvestment':data['10Year']}) 4 5 6 7 return data.values.tolist() 8 9 10 def optimizeInvestments(A, amount): 11 12 # table to income investment_made = [None for i in range(len(A))) 13 14 15 for i in range(len(A)): investment_made[i] = A[i][1] * A[i][2] 16 17 18 investments = None 19 20 roi = 0 21 22 23 24 25 26 27 28 for i in range(len(A)): invested = amount invested-=A[i][1] investment = 0 iny = [A[i][0]] for j in range(i+1, len(A)): if invested >= 0: invested-=A[j][1] investment+investment_made[j] inv.append(A[j][0]) if investment > roi: roi = investment investments = inv 29 30 31 32 33 34 35 36 return(roi, investments) 37 38 39 40 print(optimizeInvestments(loadInvestments('State_Zhvi_Summary_All Homes.csv'), 1000000)) (41865.455897, ['South Dakota', 'Alaska', 'North Dakota', 'Vermont', 'District of Columbia'])