height

Number of table rows

Syntax

H = height(T)

Description

H = height(T) returns the number of rows in the table, T.

height(T) is equivalent to size(T,1).

Examples

Number of Table Rows

Create a table, T.

LastName = {'Smith';'Johnson';'Williams';'Jones';'Brown'};
Age = [38;43;38;40;49];
Height = [71;69;64;67;64];
Weight = [176;163;131;133;119];
BloodPressure = [124 93; 109 77; 125 83; 117 75; 122 80];

T = table(Age,Height,Weight,BloodPressure,'RowNames',LastName)

T=5×4 table
                Age    Height    Weight    BloodPressure
                ___    ______    ______    _____________

    Smith       38       71       176       124     93  
    Johnson     43       69       163       109     77  
    Williams    38       64       131       125     83  
    Jones       40       67       133       117     75  
    Brown       49       64       119       122     80  

Find the number of rows in table T.

H = height(T)

H = 5

T contains five rows; height does not count the variable names.

Input Arguments

T — Input table or array

table | array

Input table or array.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. For more information, see Tall Arrays.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

This function fully supports code generation. For more information, see Code Generation for Tables (MATLAB Coder).

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).