Group data in bins

I have a file with data numbers. I would like to group them , let's say in "bins", by a specific step (eg minimum value is 0, maximum is 20. So I would like to make bins by 0.5 step and make groups with these values).

I think this:

% Define the bin edges you want
EDGES = 0:0.5:20;

% Bin the data according to the predefined edges:
Y = histcounts(x, EDGES);

But the point is that I would like to take the y values that correspont to these bins.

Could you help me please?

[KALYAN ACHARJYA]

If you want help, you need to be more clear, so that members can help you.

[Ivan Mich]

ok, I have one input file with data (x,y). I want to group my data as I mentioned above (I did this). I would like to have the y values that correspond to each bin.

How could I do this?

[Paul Hoffrichter]

If you provide a small set of input data, and show the desired output, members should be able to help you better.

Answer 1

[Cris LaPierre]

The first output of histcounts is the count of items in each bin.

You could use this syntax to at least determine which bin each X value was assigned to. You could probably use that to then group the y values, too.

[N,edges,bin] = histcounts(___)

You might want to look into the findgroups function.

Answer 2

[Paul Hoffrichter]

Hope this is close to what you are looking for.

EDGES'
ans =
         0
    0.5000
    1.0000
    1.5000
    2.0000
    2.5000
    3.0000
    3.5000  <- Bin 8 covers [3.5 to 4.0)
    4.0000
    4.5000
    ...
   19.0000
   19.5000
   20.0000
X = abs( randn(16,1)*5 );
X =
    0.1739
    3.9908  <- Bin 8 in [3.5 to 4.0)
    5.0934
    0.6661
    3.5727  <- Bin 8 in [3.5 to 4.0)
    6.7569
    1.1239
    2.9451
    1.4688
    4.2396
    5.6006
   12.6300
[~, ~, bin] = histcounts(X, EDGES);
bin_X = [bin X]
    1.0000    0.1739
    8.0000    3.9908  <- Bin 8 in [3.5 to 4.0)
   11.0000    5.0934
    2.0000    0.6661
    8.0000    3.5727  <- Bin 8 in [3.5 to 4.0)
   14.0000    6.7569
    3.0000    1.1239
    6.0000    2.9451
    3.0000    1.4688
    9.0000    4.2396
   12.0000    5.6006
   26.0000   12.6300
X_bin_sorted = sort(bin_X)
    1.0000    0.1739
    2.0000    0.6661
    3.0000    1.1239
    3.0000    1.4688
    6.0000    2.9451
    8.0000    3.5727  <- Bin 8 in [3.5 to 4.0)
    8.0000    3.9908  <- Bin 8 in [3.5 to 4.0)
    9.0000    4.2396
   11.0000    5.0934
   12.0000    5.6006
   14.0000    6.7569
   26.0000   12.6300

Answer 3

[Steven Lord]

If you just need to know in which bin each element falls, use discretize.

E = 0:11;
x = 10*rand(20, 1);
bin = discretize(x, E);
result = table(x, bin)

result = 20x2 table
       x       bin
    _______    ___

     4.2765     5 
     9.6732    10 
     9.5604    10 
     3.2379     4 
      8.338     9 
     8.4339     9 
     7.7412     8 
     2.1064     3 
    0.67424     1 
    0.50379     1 
     2.7735     3 
     3.0882     4 
     5.5501     6 
     9.5552    10 
     3.2904     4 
     1.7843     2 

If your data could take on the values 9 and 10 and your edges vector was 0:10 the last bin would contain both those elements with value 9 and those with value 10. The last bin contains both its left edge and its right edge, while the other bins contain their left but not their right. That why the edges vector goes to 11.

[Ivan Mich]

I want to know exactly which numbers are in each bin so that I can do statistical calculations (for example mean and standard deviations for each class) that I want.

Do you know how?

Answer 4

[Cris LaPierre]

In that case, i would look into grpstats or groupsummary. You might also be interested in using findgroups in conjunction with splitapply.