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 and Work with Tables or watch Tables and Categorical Arrays.

Functions

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

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

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

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
convertvars Convert table or timetable variables to specified data type
splitvars Split multicolumn variables in table or timetable
mergevars Combine table or timetable variables into multicolumn variable
vartype Subscript into table or timetable by variable type

Reshape

rows2vars Reorient table or timetable so that rows become variables
stack Stack data from multiple variables into single variable
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 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
ismissing Find missing values
standardizeMissing Insert standard missing values
rmmissing Remove missing entries
fillmissing Fill missing values
varfun Apply function to table or timetable variables
rowfun Apply function to table or timetable rows
findgroups Find groups and return group numbers
splitapply Split data into groups and apply function
groupsummary Group summary computations

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Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Design of Experiments

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble



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