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Preprocessing Data

Data cleaning, smoothing, grouping

Data 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. Detecting local extrema and abrupt changes can help to identify significant data trends. Smoothing and detrending are processes for removing noise and polynomial trends from data, while scaling changes the bounds of the data. Grouping and binning methods identify data characteristics by groups.


Data CleanerPreprocess and organize column-oriented data

Live Editor Tasks

Clean Missing DataFind, fill, or remove missing data in the Live Editor
Clean Outlier DataFind, fill, or remove outliers in the Live Editor
Compute by GroupSummarize, transform, or filter by group in the Live Editor
Find Change PointsFind abrupt changes in data in the Live Editor
Find Local ExtremaFind local maxima and minima in the Live Editor
Normalize DataCenter and scale data in the Live Editor
Smooth DataSmooth noisy data in the Live Editor
Find and Remove TrendsFind and remove polynomial or periodic trends from data in the Live Editor


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anymissingDetermine if any array element is missing
ismissingFind missing values
rmmissingRemove missing entries
fillmissingFill missing entries
fillmissing2Fill missing entries in 2-D data
missingCreate missing values
standardizeMissingInsert standard missing values
isoutlierFind outliers in data
filloutliersDetect and replace outliers in data
rmoutliersDetect and remove outliers in data
movmadMoving median absolute deviation
ischangeFind abrupt changes in data
islocalminFind local minima
islocalmaxFind local maxima
smoothdataSmooth noisy data
movmeanMoving mean
movmedianMoving median
detrendRemove polynomial trend
trenddecompFind trends in data
normalizeNormalize data
rescaleScale range of array elements
discretizeGroup data into bins or categories
pivotSummarize tabular data in pivoted table
groupsummaryGroup summary computations
groupcountsNumber of group elements
groupfilterFilter by group
grouptransformTransform by group
histcountsHistogram bin counts
histcounts2Bivariate histogram bin counts
findgroupsFind groups and return group numbers
splitapplySplit data into groups and apply function
rowfunApply function to table or timetable rows
varfunApply function to table or timetable variables
accumarrayAccumulate vector elements