Index and Search Using Nominal and Ordinal Arrays

Note

The nominal and ordinal array data types are not recommended. To represent ordered and unordered discrete, nonnumeric data, use the Categorical Arrays (MATLAB) data type instead.

Index By Category

It is often useful to index and search data by its category, or group. If you store categories as labels inside a cell array of character vectors or char array, it can be difficult to index and search the categories. When using nominal or ordinal arrays, you can easily:

Index elements from particular categories. For both nominal and ordinal arrays, you can use the logical operators == and ~= to index the observations that are in, or not in, a particular category. For ordinal arrays, which have an encoded order, you can also use inequalities, >, >=, <, and <=, to find observations in categories above or below a particular category.
Search for members of a category. In addition to the logical operator ==, you can use ismember to find observations in a particular group.
Find elements that are not in a defined category. Nominal and ordinal arrays indicate which elements do not belong to a defined category by <undefined>. You can use isundefined to find observations missing a category.
Delete observations that are in a particular category. You can use logical operators to include or exclude observations from particular categories. Even if you remove all observations from a category, the category level remains defined unless you remove it using droplevels.

Common Indexing and Searching Methods

Open Live Script

This example shows several common indexing and searching methods.

Load the sample data.

load carsmall;

Convert the char array, Origin, to a nominal array. This variable contains the country of origin, or manufacture, for each sample car.

Origin = nominal(Origin);

Search for observations in a category. Determine if there are any cars in the sample that were manufactured in Canada.

any(Origin=='Canada')

ans = logical
   0

There are no sample cars manufactured in Canada.

List the countries that are levels of Origin.

getlevels(Origin)

ans = 1x6 nominal
     France      Germany      Italy      Japan      Sweden      USA

Index elements that are in a particular category. Plot a histogram of the acceleration measurements for cars made in the U.S.

figure();
histogram(Acceleration(Origin=='USA'))
title('Acceleration of Cars Made in the USA')

Delete observations that are in a particular category. Delete all cars made in Sweden from Origin.

Origin = Origin(Origin~='Sweden');
any(ismember(Origin,'Sweden'))

ans = logical
   0

The cars made in Sweden are deleted from Origin, but Sweden is still a level of Origin.

getlevels(Origin)

ans = 1x6 nominal
     France      Germany      Italy      Japan      Sweden      USA

Remove Sweden from the levels of Origin.

Origin = droplevels(Origin,'Sweden');
getlevels(Origin)

ans = 1x5 nominal
     France      Germany      Italy      Japan      USA

Check for observations not in a defined category. Get the indices for the cars made in France.

ix = find(Origin=='France')

There are four cars from France. Remove France from the levels of Origin.

Origin = droplevels(Origin,'France');

This returns a warning indicating that you are dropping a category level that has elements in it. These observations are no longer in a defined category, indicated by undefined.

Origin(ix)

ans = 4x1 nominal
     <undefined> 
     <undefined> 
     <undefined> 
     <undefined>

You can use isundefined to search for observations with an undefined category.

find(isundefined(Origin))

These indices correspond to the observations that were in category France, before that category was dropped from Origin.

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