Declare Class List
public class TypeData
{
public string Times {get;set;}
}
Write Down Main Function inside Console Application
void Main()
{
List<TypeData> data = new List<TypeData>();
data.Add(new TypeData {Times = "01:00 PM"});
data.Add(new TypeData {Times = "02:00 PM"});
data.Add(new TypeData {Times = "03:00 PM"});
data.Add(new TypeData {Times = "04:00 PM"});
data.Add(new TypeData {Times = "05:00 PM"});
data.Add(new TypeData {Times = "06:00 PM"});
data.Add(new TypeData {Times = "07:00 PM"});
data.Add(new TypeData {Times = "08:00 PM"});
data.Add(new TypeData {Times = "09:00 PM"});
data.Add(new TypeData {Times = "10:00 PM"});
data.Add(new TypeData {Times = "11:00 PM"});
data.Add(new TypeData {Times = "12:00 PM"});
data.Add(new TypeData {Times = "01:00 AM"});
data.Add(new TypeData {Times = "02:00 AM"});
data.Add(new TypeData {Times = "03:00 AM"});
data.Add(new TypeData {Times = "04:00 AM"});
data.Add(new TypeData {Times = "05:00 AM"});
data.Add(new TypeData {Times = "06:00 AM"});
data.Add(new TypeData {Times = "07:00 AM"});
data.Add(new TypeData {Times = "08:00 AM"});
data.Add(new TypeData {Times = "09:00 AM"});
data.Add(new TypeData {Times = "10:00 AM"});
data.Add(new TypeData {Times = "11:00 AM"});
data.Add(new TypeData {Times = "12:00 AM"});
//data.Dump();
var datafinal_AM = data.Where(x=>x.Times.Contains("AM")).ToList();
var datafinal_PM = data.Where(x=>x.Times.Contains("PM")).ToList();
List<TypeData> finalR = new List<TypeData>();
foreach(var rows in datafinal_AM)
{
finalR.Add(new TypeData {Times = rows.Times});
}
foreach(var rows in datafinal_PM)
{
finalR.Add(new TypeData {Times = rows.Times});
}
}
Output :-
![](data:image/png;base64,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)