Question
Hello guys, i need to create a java OOP program and i would really appreciate if you guys could help me out This program needs
Hello guys, i need to create a java OOP program and i would really appreciate if you guys could help me out
This program needs do to some operations as SUM, COUNT, AVG, MIN and MAX
Here are some input and output examples
Generic ex:
Input:
F // File name
Column1Name,Column2Name
SUM //Operation
Column3Name // Column where are the values we want to do the operation
Here are some examples of input and output for this CSV file :
Ex.1:
Input:
Customer_Data.csv
Geography,Gender
SUM
Tenure
Output: {[Germany, Male]=6646, [Germany, Female]=5924, [France, Male]=13901, [France, Female]=11192, [Spain, Male]=7020, [Spain, Female]=5445.}
Basiclly we need to sum all the values of tenure where the gender is female and from france, all the values of tenure where the gender is male and from france, all the values of tenure where the gender is male and from germany, all the values where the gender is female and from german and soo on
Note that the order of columns in the output does not matter.
I will leave here the first 26 rows of the csv, the csv has 10 000 rows and thats why the output may look different
RowNumber,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited |
1,15634602,Hargrave,619,France,Female,42,2,0,1,1,1,101348.88,1 |
2,15647311,Hill,608,Spain,Female,41,1,83807.86,1,0,1,112542.58,0 |
3,15619304,Onio,502,France,Female,42,8,159660.8,3,1,0,113931.57,1 |
4,15701354,Boni,699,France,Female,39,1,0,2,0,0,93826.63,0 |
5,15737888,Mitchell,850,Spain,Female,43,2,125510.82,1,1,1,79084.1,0 |
6,15574012,Chu,645,Spain,Male,44,8,113755.78,2,1,0,149756.71,1 |
7,15592531,Bartlett,822,France,Male,50,7,0,2,1,1,10062.8,0 |
8,15656148,Obinna,376,Germany,Female,29,4,115046.74,4,1,0,119346.88,1 |
9,15792365,He,501,France,Male,44,4,142051.07,2,0,1,74940.5,0 |
10,15592389,H?,684,France,Male,27,2,134603.88,1,1,1,71725.73,0 |
11,15767821,Bearce,528,France,Male,31,6,102016.72,2,0,0,80181.12,0 |
12,15737173,Andrews,497,Spain,Male,24,3,0,2,1,0,76390.01,0 |
13,15632264,Kay,476,France,Female,34,10,0,2,1,0,26260.98,0 |
14,15691483,Chin,549,France,Female,25,5,0,2,0,0,190857.79,0 |
15,15600882,Scott,635,Spain,Female,35,7,0,2,1,1,65951.65,0 |
16,15643966,Goforth,616,Germany,Male,45,3,143129.41,2,0,1,64327.26,0 |
17,15737452,Romeo,653,Germany,Male,58,1,132602.88,1,1,0,5097.67,1 |
18,15788218,Henderson,549,Spain,Female,24,9,0,2,1,1,14406.41,0 |
19,15661507,Muldrow,587,Spain,Male,45,6,0,1,0,0,158684.81,0 |
20,15568982,Hao,726,France,Female,24,6,0,2,1,1,54724.03,0 |
21,15577657,McDonald,732,France,Male,41,8,0,2,1,1,170886.17,0 |
22,15597945,Dellucci,636,Spain,Female,32,8,0,2,1,0,138555.46,0 |
23,15699309,Gerasimov,510,Spain,Female,38,4,0,1,1,0,118913.53,1 |
24,15725737,Mosman,669,France,Male,46,3,0,2,0,1,8487.75,0 |
25,15625047,Yen,846,France,Female,38,5,0,1,1,1,187616.16,0 |
26,15738191,Maclean,577,France,Male,25,3,0,2,0,1,124508.29,0 |
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