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.
||Group data into bins or categories|
||Histogram bin counts|
||Bivariate histogram bin counts|
||Find groups and return group numbers|
||Split data into groups and apply function|
||Apply function to table or timetable rows|
||Apply function to table or timetable variables|
||Construct array with accumulation|
Handle missing values from data sets.
This example shows how to find, clean, and delete table rows with missing data.
Remove linear trends from data.
Filtering is a data processing technique used for smoothing data or modifying specific data characteristics, such as signal amplitude.
You can use grouping variables to categorize data variables.
This example shows how to group data and apply statistics functions to each group.
This example shows how to group data variables and apply functions to each group.