Tables

Arrays in tabular form whose named columns can have different types

table is a data type suitable for column-oriented or tabular data that is often stored as columns in a text file or in a spreadsheet. Tables consist of rows and column-oriented variables. Each variable in a table can have a different data type and a different size with the one restriction that each variable must have the same number of rows. For more information, see Create Tables and Assign Data to Them or watch Tables and Categorical Arrays.

Functions

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Create Tables and Convert Type

`table`	Table array with named variables that can contain different types
`array2table`	Convert homogeneous array to table
`cell2table`	Convert cell array to table
`struct2table`	Convert structure array to table
`table2array`	Convert table to homogeneous array
`table2cell`	Convert table to cell array
`table2struct`	Convert table to structure array
`table2timetable`	Convert table to timetable
`timetable2table`	Convert timetable to table
`vartype`	Subscript into table or timetable by variable type
`convertvars`	Convert table or timetable variables to specified data type

Read and Write Files

Basic Import and Export

`readtable`	Create table from file
`writetable`	Write table to file

Define Import Rules

`detectImportOptions`	Create import options based on file content
`spreadsheetImportOptions`	Import options object for Spreadsheets
`getvaropts`	Get variable import options
`setvaropts`	Set variable import options
`setvartype`	Set variable data types
`preview`	Preview eight rows from file using import options

Parquet Files

`parquetread`	Read columnar data from a Parquet file
`parquetwrite`	Write columnar data to Parquet file
`parquetinfo`	Get information about Parquet file

Summary Information and Stacked Plot

`summary`	Print summary of table, timetable, or categorical array
`height`	Number of table rows
`width`	Number of table variables
`istable`	Determine if input is table
`istabular`	Determine if input is table or timetable
`head`	Get top rows of array or table
`tail`	Get bottom rows of array or table
`stackedplot`	Stacked plot of several variables with common x-axis

Sort, Filter, and Rearrange

Sort

`sortrows`	Sort rows of matrix or table
`unique`	Unique values in array
`issortedrows`	Determine if matrix or table rows are sorted
`topkrows`	Top rows in sorted order

Filter Rows and Variables

`rowfilter`	Selectively import rows of interest
`vartype`	Subscript into table or timetable by variable type

Rearrange Variables

`addvars`	Add variables to table or timetable
`renamevars`	Rename variables in table or timetable
`movevars`	Move variables in table or timetable
`removevars`	Delete variables from table or timetable
`splitvars`	Split multicolumn variables in table or timetable
`mergevars`	Combine table or timetable variables into multicolumn variable
`convertvars`	Convert table or timetable variables to specified data type

Reshape

`rows2vars`	Reorient table or timetable so that rows become variables
`stack`	Stack data from input table or timetable into one variable in output table or timetable
`unstack`	Unstack data from single variable into multiple variables
`inner2outer`	Invert nested table-in-table hierarchy in tables or timetables

Customize Properties

`addprop`	Add custom properties to table or timetable
`rmprop`	Remove custom properties from table or timetable

Join and Set Operations

Join Using Key Variables

`join`	Combine two tables or timetables by rows using key variables
`innerjoin`	Inner join between two tables or timetables
`outerjoin`	Outer join between two tables or timetables

Set Operations

`union`	Set union of two arrays
`intersect`	Set intersection of two arrays
`ismember`	Array elements that are members of set array
`setdiff`	Set difference of two arrays
`setxor`	Set exclusive OR of two arrays

Missing Values

`anymissing`	Determine if any array element is missing
`ismissing`	Find missing values
`standardizeMissing`	Insert standard missing values
`rmmissing`	Remove missing entries
`fillmissing`	Fill missing entries

Apply Functions to Table Contents

`pivot`	Summarize tabular data in pivoted table
`groupcounts`	Number of group elements
`groupfilter`	Filter by group
`groupsummary`	Group summary computations
`grouptransform`	Transform by group
`findgroups`	Find groups and return group numbers
`splitapply`	Split data into groups and apply function
`rowfun`	Apply function to table or timetable rows
`varfun`	Apply function to table or timetable variables
`vartype`	Subscript into table or timetable by variable type
`convertvars`	Convert table or timetable variables to specified data type

Apps

Data Cleaner

Preprocess and organize column-oriented data

Live Editor Tasks

Compute by Group	Summarize, transform, or filter by group in the Live Editor
Join Tables	Combine two tables using key variables in the Live Editor
Stack Table Variables	Combine values from multiple table variables into one table variable in the Live Editor
Unstack Table Variables	Distribute values from one table variable to multiple table variables in the Live Editor

Topics

Create Tables and Assign Data to Them
There are several ways to create tables and assign data to them. You can create tables from input arrays, preallocate tables and fill them in later, or import tables from text files or spreadsheets.
Define Import Options for Tables
If you want to control the import process beyond the options provided by the readtable function, such as defining how to handle missing data or errors, then create an import options object before importing the data.
Access Data in Tables

Indexing into tables with parentheses, dot notation, and curly braces accesses table data in different ways. You can use indexing to create a table that is a subset of a larger table or to create an array from data in a table.
Direct Calculations on Tables and Timetables

You can perform calculations directly on tables and timetables without indexing to extract their data. All the variables in your tables and timetables must have data types that support calculations.
Plots That Support Tables
Create plots by passing tables directly to plotting functions.
Advantages of Using Tables
Use tables to store mixed-type data conveniently, access data with numeric or named indexing, and to store metadata.
Changes to DimensionNames Property in R2016b
Tables can raise compatibility warnings, and change dimension names, when you modify the DimensionNames property.

Featured Examples

Data Cleaning and Calculations in Tables

Clean data stored in a table or timetable. Perform computations by using the numeric and categorical data that the table contains.

Open Live Script

Grouped Calculations in Tables and Timetables

Perform in-place calculations on groups of data in tables and timetables.

Open Live Script