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Preprocessing Data

Data cleaning, smoothing, grouping

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

ismissing Find missing values
rmmissing Remove missing entries
fillmissing Fill missing values
missing Create missing values
standardizeMissing Insert standard missing values
isoutlier Find outliers in data
filloutliers Detect and replace outliers in data
smoothdata Smooth noisy data
movmean Moving mean
movmedian Moving median
detrend Remove linear trends
filter 1-D digital filter
filter2 2-D digital filter
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

Missing Data

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.

Detrending Data

Remove linear trends from data.

Filter Data

Filtering is a data processing technique used for smoothing data or modifying specific data characteristics, such as signal amplitude.

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.

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