Preprocessing Data
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.
Applications
Data Cleaner | Preprocess and organize column-oriented data |
Tâches du Live Editor
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 in the Live Editor |
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 |
Fonctions
Rubriques
- Clean Messy and Missing Data in Tables
This example shows how to find, clean, and delete table rows with missing 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.
- Perform Calculations by Group in Table
Specify groups of data in tables and timetables, and perform calculations by group. Choose a function for group calculations using these recommendations.