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Preprocessing Data
Data sets can require preprocessing techniques to ensure accurate, efficient, or meaningful analysis. Data cleaning refers to methods for finding, removing, and replacing bad or missing data. Smoothing and detrending are processes for removing noise and linear trends from data. Grouping and binning methods are techniques that identify relationships among the data variables.
Functions
Find, Remove, Fill, and Smooth Data
ismissing |
Find missing values |
standardizeMissing |
Insert standard missing-value indicators |
rmmissing |
Remove missing entries |
fillmissing |
Fill missing values |
movmean |
Moving mean |
movmedian |
Moving median |
detrend |
Remove linear trends |
filter |
1-D digital filter |
Group and Bin Data
discretize |
Group data into bins or categories |
histcounts |
Histogram bin counts |
histcounts2 |
Bivariate histogram bin counts |
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 |
accumarray |
Construct array with accumulation |
Topics
Handle missing values from data sets.
Clean Messy and Missing Data in Tables
This example shows how to find, clean, and delete table rows with missing data.
Remove linear trends from data.
This topic defines the filter function
in MATLAB®, and presents two examples of filters that modify input
data.
Grouping Variables To Split Data
You can use grouping variables to categorize data variables.
Split Data into Groups and Calculate Statistics
This example shows how to group data and apply statistics functions to each group.
Split Table Data Variables and Apply Functions
This example shows how to group data variables and apply functions to each group.
