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.
Live Editor Tasks
|Clean Missing Data||Find, fill, or remove missing data in the Live Editor|
|Clean Outlier Data||Find, fill, or remove outliers in the Live Editor|
|Compute by Group||Summarize, transform, or filter by group|
|Find Change Points||Find abrupt changes in data in the Live Editor|
|Find Local Extrema||Find local maxima and minima in the Live Editor|
|Normalize Data||Center and scale data in the Live Editor|
|Smooth Data||Smooth noisy data in the Live Editor|
|Remove Trends||Remove polynomial trend from data in the Live Editor|
Missing Data and Outliers
|Find missing values|
|Remove missing entries|
|Fill missing values|
|Create missing values|
|Insert standard missing values|
|Find outliers in data|
|Detect and replace outliers in data|
|Detect and remove outliers in data|
|Moving median absolute deviation|
Detecting Change Points and Local Extrema
Smoothing Data and Finding Trends
Grouping and Binning Data
|Group data into bins or categories|
|Number of group elements|
|Filter by group|
|Group summary computations|
|Transform by group|
|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|
|Accumulate vector elements|
This example shows how to find, clean, and delete table rows with missing data.
Remove linear trends from data.
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.