Hello, I have a problem to solve . That's a Java OOP

Given the name of a csv (F) file, a list of columns separated by commas (C1, C2, ..., CN), an aggregation operation (SUM, COUNT, AVG, MIN, MAX) and a column with a numeric value (A), the dynamic table corresponding to the aggregation operation must be obtained. Note that the columns C1, C2, ..., CN can be of the string type or numeric and the column A e always in a generic. Example Input: F C1, C2, ..., CN SUM Output: {[C1A, C2A] = NNN, [C1A, C2B] = NNN, [C1B, C2A] = NNN}

But I need that program work with any csv file...

1 Row Numbler, Customerld, Surname, CreditScore, Geography, Gender, Age,Tenure, Balance, NumOfProducts, HasCrCard, IsActive Member, Estimated Salary,Exited 2 1,15634602, Hargrave,619, France, Female,42,2,0,1,1,1,101348.88,1 3 2,15647311, Hill, 608, Spain, Female,41,1,83807.86,1,0,1,112542.58,0 4 3,15619304,Onio, 502,France, Female, 42,8,159660.8,3,1,0,113931.57,1 5 4,15701354,Boni,699, France, Female, 39,1,0,2,0,0,93826.63,0 6 5,15737888, Mitchell,850, Spain, Female, 43,2,125510.82,1,1,1,79084.1,0 7 6,15574012, Chu, 645, Spain, Male,44,8,113755.78,2,1,0,149756.71,1 8 7,15592531,Bartlett,822,France, Male, 50,7,0,2,1,1,10062.8,0 9 8,15656148, Obinna,376, Germany, Female,29,4,115046.74,4,1,0,119346.88,1 0 9,15792365, He,501, France, Male, 44,4,142051.07,2,0,1,74940.5,0 1 10,15592389,H?,684,France, Male,27,2,134603.88,1,1,1,71725.73,0 2 11,15767821,Bearce,528, France, Male,31,6,102016.72,2,0,0,80181.12,0 3 12,15737173, Andrews,497,Spain, Male,24,3,0,2,1,0,76390.01,0 4 13,15632264, Kay,476,France, Female, 34,10,0,2,1,0,26260.98,0 5 14,15691483,Chin,549, France, Female, 25,5,0,2,0,0,190857.79,0 6 15,15600882,Scott,635, Spain, Female,35,7,0,2,1,1,65951.65,0 7 16,15643966, Goforth,616, Germany, Male, 45,3,143129.41,2,0,1,64327.26,0 8 17,15737452,Romeo,653, Germany, Male,58,1,132602.88,1,1,0,5097.67,1 9 18,15788218, Henderson,549,Spain, Female,24,9,0,2,1,1,14406.41,0 0 19,15661507, Muldrow,587,Spain, Male,45,6,0,1,0,0,158684.81,0 1 20,15568982,Hao,726, France, Female,24,6,0,2,1,1,54724.03,0 2 21,15577657, McDonald, 732,France, Male,41,8,0,2,1,1,170886.17,0 3 22,15597945, Dellucci,636, Spain, Female, 32,8,0,2,1,0,138555.46,0 4 23,15699309, Gerasimov,510, Spain, Female,38,4,0,1,1,0,118913.53,1 5 24,15725737, Mosman,669, France, Male, 46,3,0,2,0,1,8487.75,0 6 25,15625047, Yen,846, France, Female,38,5,0,1,1,1,187616.16,0 27 26,15738191, Maclean,577,France, Male,25,3,0,2,0,1,124508.29,0 8 27,15736816, Young,756, Germany, Male,36,2,136815.64,1,1,1,170041.95,0 9 28,15700772, Nebechi,571, France, Male, 44,9,0,2,0,0,38433.35,0