Tables of Mixed Data - MATLAB & Simulink - MathWorks Deutschland (2024)

Store Related Data in Single Container

You can use the table data type to collect mixed-type data and metadata properties, such as variable names, row names, descriptions, and variable units, in a single container. Tables are suitable for column-oriented or tabular data that is often stored as columns in a text file or in a spreadsheet. For example, you can use a table to store experimental data, with rows representing different observations and columns representing different measured variables.

Tables consist of rows and column-oriented variables. Variables in a table can have different data types and different sizes, but the variables must have the same number of rows. Also, the data within a variable is hom*ogeneous, which enables you to treat a table variable like an array of data.

For example, load sample data about patients from the patients.mat MAT-file. Combine blood pressure data into a single variable. Convert a four-category variable called SelfAssessedHealthStatus—which has values of Poor, Fair, Good, or Excellent—to a categorical array. View information about several of the variables.

load patientsBloodPressure = [Systolic Diastolic];SelfAssessedHealthStatus = categorical(SelfAssessedHealthStatus);whos("Age","Smoker","BloodPressure","SelfAssessedHealthStatus")

 Name Size Bytes Class Attributes Age 100x1 800 double BloodPressure 100x2 1600 double SelfAssessedHealthStatus 100x1 560 categorical Smoker 100x1 100 logical 

Now, create a table from these variables and display it. The variables can be stored together in a table because they all have the same number of rows, 100.

T = table(Age,Smoker,BloodPressure,SelfAssessedHealthStatus)

T=100×4 table Age Smoker BloodPressure SelfAssessedHealthStatus ___ ______ _____________ ________________________ 38 true 124 93 Excellent 43 false 109 77 Fair 38 false 125 83 Good 40 false 117 75 Fair 49 false 122 80 Good 46 false 121 70 Good 33 true 130 88 Good 40 false 115 82 Good 28 false 115 78 Excellent 31 false 118 86 Excellent 45 false 114 77 Excellent 42 false 115 68 Poor 25 false 127 74 Poor 39 true 130 95 Excellent 36 false 114 79 Good 48 true 130 92 Good ⋮

Each variable in a table has one data type. If you add a new row to the table, MATLAB® forces consistency of the data type between the new data and the corresponding table variables. For example, if you try to add information for a new patient where the first column contains the patient's health status instead of age, as in the expression T(end+1,:) = {"Poor",true,[130 84],37}, then you receive the error:

Right hand side of an assignment to a categorical array must be a categorical or text representing a category name.

The error occurs because MATLAB® cannot assign numeric data, 37, to the categorical array, SelfAssessedHealthStatus.

Access Data Using Numeric or Named Indexing

You can index into a table using parentheses, curly braces, or dot notation. Parentheses allow you to select a subset of the data in a table and preserve the table container. Curly braces and dot notation allow you to extract data from a table. Within each table indexing method, you can specify the rows or variables to access by name or by numeric index.

Consider the sample table from above. Each row in the table, T, represents a different patient. The workspace variable, LastName, contains unique identifiers for the 100 rows. Add row names to the table by setting the RowNames property to LastName and display the first five rows of the updated table.

T.Properties.RowNames = LastName;T(1:5,:)

ans=5×4 table Age Smoker BloodPressure SelfAssessedHealthStatus ___ ______ _____________ ________________________ Smith 38 true 124 93 Excellent Johnson 43 false 109 77 Fair Williams 38 false 125 83 Good Jones 40 false 117 75 Fair Brown 49 false 122 80 Good 

In addition to labeling the data, you can use row and variable names to access data in the table. For example, use named indexing to display the age and blood pressure of the patients Williams and Brown.

T(["Williams","Brown"],["Age","BloodPressure"])

ans=2×2 table Age BloodPressure ___ _____________ Williams 38 125 83 Brown 49 122 80 

Now, use numeric indexing to return an equivalent subtable. Return the third and fifth rows from the first and third variables.

T([3 5],[1 3])

ans=2×2 table Age BloodPressure ___ _____________ Williams 38 125 83 Brown 49 122 80 

For more information on table indexing, see Access Data in Tables.

Describe Data with Table Properties

In addition to storing data, tables have properties to store metadata, such as variable names, row names, descriptions, and variable units. You can access a property using T.Properties.PropName, where T is the name of the table and PropName is the name of a table property.

For example, add a table description, variable descriptions, and variable units for Age.

T.Properties.Description = "Simulated Patient Data";T.Properties.VariableDescriptions = ... ["" ... "true or false" ... "Systolic/Diastolic" ... "Status Reported by Patient"];T.Properties.VariableUnits("Age") = "Yrs";

Individual empty strings within VariableDescriptions indicate that the corresponding variable does not have a description. For more information, see the Properties section of table.

To print a table summary, use the summary function.

summary(T)

Description: Simulated Patient DataVariables: Age: 100x1 double Properties: Units: Yrs Values: Min 25 Median 39 Max 50 Smoker: 100x1 logical Properties: Description: true or false Values: True 34 False 66 BloodPressure: 100x2 double Properties: Description: Systolic/Diastolic Values: Column 1 Column 2 ________ ________ Min 109 68 Median 122 81.5 Max 138 99 SelfAssessedHealthStatus: 100x1 categorical Properties: Description: Status Reported by Patient Values: Excellent 34 Fair 15 Good 40 Poor 11 

Comparison to Cell Arrays

Like a table, a cell array can provide storage for mixed-type data in a single container. But unlike a table, a cell array does not provide metadata that describes its contents. It does not force data in its columns to remain hom*ogenous. You cannot access the contents of a cell array using row names or column names.

For example, convert T to a cell array using the table2cell function. The output cell array contains the same data but has no information about that data. If it is important to keep such information attached to your data, then storing it in a table is a better choice than storing it in a cell array.

C = table2cell(T)

C=100×4 cell array {[38]} {[1]} {[124 93]} {[Excellent]} {[43]} {[0]} {[109 77]} {[Fair ]} {[38]} {[0]} {[125 83]} {[Good ]} {[40]} {[0]} {[117 75]} {[Fair ]} {[49]} {[0]} {[122 80]} {[Good ]} {[46]} {[0]} {[121 70]} {[Good ]} {[33]} {[1]} {[130 88]} {[Good ]} {[40]} {[0]} {[115 82]} {[Good ]} {[28]} {[0]} {[115 78]} {[Excellent]} {[31]} {[0]} {[118 86]} {[Excellent]} {[45]} {[0]} {[114 77]} {[Excellent]} {[42]} {[0]} {[115 68]} {[Poor ]} {[25]} {[0]} {[127 74]} {[Poor ]} {[39]} {[1]} {[130 95]} {[Excellent]} {[36]} {[0]} {[114 79]} {[Good ]} {[48]} {[1]} {[130 92]} {[Good ]} ⋮

To access subsets of data in a cell array, you can only use indexing with parentheses or curly braces.

C(1:5,1:3)

ans=5×3 cell array {[38]} {[1]} {[124 93]} {[43]} {[0]} {[109 77]} {[38]} {[0]} {[125 83]} {[40]} {[0]} {[117 75]} {[49]} {[0]} {[122 80]}

Comparison to Structures

Structures also can provide storage for mixed-type data. A structure has fields that you can access by name, just as you can access table variables by name. However, it does not force data in its fields to remain hom*ogenous. Structures do not provide any metadata to describe their contents.

For example, convert T to a scalar structure where every field is an array, in a way that resembles table variables. Use the table2struct function with the ToScalar name-value argument.

S = table2struct(T,ToScalar=true)

S = struct with fields: Age: [100x1 double] Smoker: [100x1 logical] BloodPressure: [100x2 double] SelfAssessedHealthStatus: [100x1 categorical]

In this structure, you can access arrays of data by using field names.

S.Age

ans = 100×1 38 43 38 40 49 46 33 40 28 31 ⋮

But to access subsets of data in the fields, you can only use numeric indices, and you can only access one field at a time. Table row and variable indexing provides more flexible access to data in a table.

S.Age(1:5)

ans = 5×1 38 43 38 40 49