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.
More
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
, orremove
. SelectClean 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.
Related Examples
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.
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 |
Moving median | Moving median with specified window size |
Moving mean | Moving mean 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 |
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 R2019bR2023b: 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.
See Also
Functions
Live Editor Tasks
- Clean Outlier Data | Find Local Extrema | Smooth Data | Find and Remove Trends | Find Change Points | Normalize Data | Compute by Group
Apps
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