table

Methods

⌂ table

Method Reference: table.findgroups

table: G = findgroups (T)
table: [G, TID] = findgroups (T)

Find groups defined by the variables of a table.

G = findgroups (T) returns G, a column vector of positive integer group numbers, with one element for each row of the table T. Each variable of T acts as a grouping variable, and the groups are the unique combinations of values across those variables, sorted in ascending order. If N groups are found, every integer between 1 and N labels a group. Rows holding a missing value (NaN, NaT, <missing>, '', or <undefined>) in any grouping variable are labelled NaN in G.

[G, TID] = findgroups (T) also returns TID, a table whose rows are the sorted unique combinations identifying each group, with the same variables as T.

Source Code: table

Example: 1

findgroups assigns each row an integer group number based on its grouping variable(s) — the setup step for a splitapply computation. A second output returns a table naming each group.

 Species = {'setosa'; 'virginica'; 'setosa'; 'virginica'; 'setosa'};
 Petal = [1.4; 5.1; 1.5; 5.9; 1.3];
 T = table (Species, Petal)
T =
  5x2 table

       Species       Petal    
    _____________    _____    

    {'setosa'   }      1.4    
    {'virginica'}      5.1    
    {'setosa'   }      1.5    
    {'virginica'}      5.9    
    {'setosa'   }      1.3
 [G, groupNames] = findgroups (T(:, 'Species'));
 G'
ans =

   1   2   1   2   1
 groupNames
groupNames =
  2x1 table

       Species       
    _____________    

    {'setosa'   }    
    {'virginica'}

The group numbers index into groupNames, and feed straight into splitapply to aggregate Petal per species.

 splitapply (@mean, T(:, 'Petal'), G)
ans =

   1.4000
   5.5000