ymd

Year, month, and day numbers of datetime

Syntax

[y,m,d] = ymd(t)

Description

[y,m,d] = ymd(t) returns the year, month, and day numbers of the datetime values in t as separate numeric arrays. The y, m, and d outputs are the same size as t, and contain integer values.

The ymd function is equivalent to calling the year, month, and day functions on the same datetime array.

Examples

Find Year, Month, and Day Numbers of Dates

t = datetime(2013,05,31):calmonths(3):datetime(2014,06,15)

t = 1×5 datetime
   31-May-2013   31-Aug-2013   30-Nov-2013   28-Feb-2014   31-May-2014

[y,m,d] = ymd(t)

y = 1×5

        2013        2013        2013        2014        2014

m = 1×5

     5     8    11     2     5

d = 1×5

    31    31    30    28    31

ymd returns the year, month, and day values in separate arrays.

Input Arguments

t — Input date and time

datetime array

Input date and time, specified as a datetime array.

Output Arguments

y — ISO year numbers

scalar | vector | matrix | multidimensional array

ISO year numbers, returned as a scalar, vector, matrix, or multidimensional array of integer values. ISO year numbers include a year zero and represent years BCE using negative values. y is of type double and is the same size as t.

m — Month numbers

scalar | vector | matrix | multidimensional array

Month numbers, returned as a scalar, vector, matrix, or multidimensional array of integer values from 1 to 12. m is of type double and is the same size as t.

d — Day of month numbers

scalar | vector | matrix | multidimensional array

Day of month numbers, returned as a scalar, vector, matrix, or multidimensional array of integer values from 1 to 28, 29, 30, or 31, depending on the month and year. d is of type double and is the same size as t.

Extended Capabilities

Tall Arrays

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

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

C/C++ Code Generation
Generate C and C++ code using 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.

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).

Version History

Introduced in R2014b