Clean Missing Data

Find, fill, or remove missing data in the Live Editor

Description

The Clean Missing Data task lets you interactively handle missing data values such as NaN or <missing>. The task automatically generates MATLAB^® code for your live script.

Using this task, you can:

• Find, fill, or remove missing data in a workspace variable.

• Customize the method for filling data.

• Define nonstandard missing value indicators.

• Visualize the missing data and the cleaned data.

For more information on Live Editor tasks, see Add Interactive Tasks to a Live Script.

Related Functions

Clean Missing Data generates code that uses the ismissing, standardizeMissing, fillmissing, and rmmissing functions.

Open the Task

To add the Clean Missing Data task to a live script in the MATLAB Editor:

• On the Live Editor tab, select Task > Clean Missing Data.

• In a code block in the script, type a relevant keyword, such as missing, NaN, fill, or remove. Select Clean Missing Data from the suggested command completions. For some keywords, the task automatically updates one or more corresponding parameters.

Examples

Fill Missing Entries in Nonuniformly Sampled Data

Interactively fill missing values in nonuniformly sampled data.

Create a vector of nonuniform sample points, and evaluate the sine function over the points.

x = [-4*pi:0.1:0 0.1:0.2:4*pi];
A = sin(x);

Inject missing values into A.

A(A < 0.75 & A > 0.5) = missing;

Open the Clean Missing Data task in the Live Editor. To clean the data, select A as the input data and x as the x-axis coordinates of the data.

The Clean Missing Data task can fill or remove missing data. To fill the missing entries using linear interpolation of neighboring nonmissing values, use the Cleaning method field to select Fill missing and Linear interpolation.

The task plots the cleaned data and indicates that the linear interpolation filled 21 missing entries in the input data.

Because the default legend location covers some filled missing entries, specify the legend location as the outside top-right corner of the axes.

legend("Location","northeastoutside")

Related Examples

• Add Interactive Tasks to a Live Script
• Clean Messy Data and Locate Extrema Using Live Editor Tasks
• Missing Data in MATLAB

Parameters

Input data — Valid input data from workspace

vector | table | timetable

This task operates on input data contained in a vector, table, or timetable. The data can be of type single, double, duration, calendarDuration, datetime, categorical, string, char, or cell arrays of character vectors.

When providing a table or timetable for the input data, select All supported variables to clean all variables with a supported type. Select All numeric variables to clean all variables of type single or double. To choose specific supported variables to clean, select Specified variables and then select the variables individually.

Select indicators — Values to treat as missing

Use only standard indicators | Specify non-standard indicators

Specify whether to treat standard or non-standard missing value indicators as missing.

• Use only standard indicators — Use standard indicators defined according to the input data type. For more information, see standardizeMissing.

• Specify non-standard indicators — Specify non-standard indicators as a comma-separated list, for example -99, "NA".

Fill method — Method for filling missing data

Linear interpolation (default) | Constant value | Previous value | ...

Specify the method for filling missing data as one of these options.

Method	Description
Linear interpolation	Linear interpolation of neighboring, nonmissing values
Constant value	Specified scalar value, which is 0 by default
Previous value	Previous nonmissing value
Next value	Next nonmissing value
Nearest value	Nearest nonmissing value as defined by the x-axis
Spline interpolation	Piecewise cubic spline interpolation
Shape-preserving cubic interpolation (PCHIP)	Shape-preserving piecewise cubic spline interpolation
Modified Akima cubic interpolation	Modified Akima cubic Hermite interpolation
Mean (since R2026a)	Mean of nonmissing values
Median (since R2026a)	Median of nonmissing values
Mode (since R2026a)	Mode of nonmissing values
Moving mean	Moving mean with specified window size
Moving median	Moving median with specified window size
K-nearest neighbors	Mean of nearest neighbors defined by a distance function
Custom function	Custom fill method, specified as a local function or a function handle

Moving window — Window for moving methods

Centered (default) | Asymmetric

Specify the window type and size when the method for filling missing data is Moving median or Moving mean.

Window	Description
Centered	Specified window length centered about the current point
Asymmetric	Specified window containing the number of elements before the current point and the number of elements after the current point

Window sizes are relative to the X-axis variable units.

Version History

Introduced in R2019b

R2026a: Fill with mean, median, or mode

Fill missing entries with the mean, median, or mode of the nonmissing values along the operating dimension. When the method for cleaning missing data is Fill missing, select the Mean, Median, or Mode fill method.

R2023b: Fill with mean of nearby points from k nearest neighbor rows

Fill missing entries with the mean of nearby points by using the K-nearest neighbors fill method. Specify the number of neighbors, and define the distance between rows using the Euclidean distance, the scaled Euclidean distance, or a custom function.

R2022b: Plot nonnumeric table data and multiple table variables

Plot nonnumeric data in the display of this Live Editor task. To display a categorical histogram, select a nonnumeric input array or set the Variable to display field to a nonnumeric table variable containing categorical, string, cellstr, calendarDuration, or char data types.

In addition, you can simultaneously plot multiple table variables in the display of this task. For table or timetable data, to plot multiple variables in a tiled chart layout, set the Variable to display field.

R2022b: Specify minimum number of missing entries and custom fill method

Specify the minimum number of missing entries required to remove a row of data. When selecting multiple table variables or a matrix of data for cleaning, select the Remove missing cleaning method, and specify the minimum number of missing entries by using the Min missing for removal field.

In addition, you can specify a custom method for filling missing data. First, select the Fill missing cleaning method, and then specify a custom fill method by selecting the Custom function cleaning method parameter and the local function or function handle option.

R2022b: Append cleaned table variables

Append input table variables with table variables containing cleaned data. For table or timetable input data, to append the cleaned data, set the Output format field.

R2022a: Live Editor task does not run automatically if inputs have more than 1 million elements

This Live Editor task does not run automatically if the inputs have more than 1 million elements. In previous releases, the task always ran automatically for inputs of any size. If the inputs have a large number of elements, then the code generated by this task can take a noticeable amount of time to run (more than a few seconds).

When a task does not run automatically, the Autorun indicator is disabled. You can either run the task manually when needed or choose to enable the task to run automatically.

R2021a: Operate on multiple table variables

This Live Editor task can operate on multiple table variables at the same time. For table or timetable input data, to operate on multiple variables, select All supported variables or Specified variables. Return all of the variables or only the modified variables, and specify which variable to visualize.