I need to separate same first column data values and its corresponding second column value.

I have two dimensional data, as shown in figure.

I need to separate same first coloumn (column 1) data values and its corresponding second column (column 2) value, into groups.

How can I do this ?

Thanks in advance.

[the cyclist]

When you say "separated", what specifically do you mean? How do you want the output organized?

Do you still need just one table, but with the values sorted? Or one table, but with an additional variable to define the unique values in column1? Or maybe you need a new table for every value in the first column?

[zhoug zho]

I still need two columns, basically I need to separate all the first column elements that are similar.

And then, as elements of first column are exactly same, so I will consider it one value, but their corresponding values in column 2 will be added and average out of all these similar ,

So in this case, I have one values from column 1 and its corresponding column 2 averaged value.

Answer 1

[Adam Danz]

Another method using groupsummary

% Create demo table
g = repelem((1:2:9)',randi(4,5,1)+1,1);
T = table(g, rand(numel(g),1).*randi([2,8],numel(g),1), ...
    'VariableNames', {'column1','column2'})

T = 16×2 table
    column1    column2 
    _______    ________

       1         5.7244
       1         1.6392
       1         1.7206
       1        0.64037
       3       0.073685
       3         1.9413
       3         1.6698
       5         1.3814
       5         1.4611
       7         1.0805
       7         1.1879
       7        0.42207
       7         1.3179
       9         4.5171
       9        0.38479
       9         2.9267

% Average column 2 for each group in column 1
Tstats = groupsummary(T,'column1','mean','column2')

Tstats = 5×3 table
    column1    GroupCount    mean_column2
    _______    __________    ____________

       1           4            2.4311   
       3           3            1.2283   
       5           2            1.4212   
       7           4            1.0021   
       9           3            2.6095