Fix common spreadsheet problems inside the same analysis workspace.
Use targeted cleaning operations to remove duplicate rows, handle missing values, normalize text, rename columns, remove unwanted records, and correct data types before you chart or report.
- Duplicate removal
- Missing-value actions
- Rename + type override
Less setup. More useful analysis.
Each feature is designed to move a real dataset forward rather than add another disconnected dashboard.
Reduce misleading results
Cleaning prevents duplicated rows, accidental blanks, and inconsistent text categories from silently distorting counts and charts.
Keep changes understandable
Cleaning actions are explicit choices rather than hidden automatic edits, so the user decides how a dataset should change.
Refresh downstream analysis
After a cleaning mutation, Amridata refreshes relevant data, quality checks, and recommendations so the workspace reflects the updated dataset.
What Data Cleaning actually does
Concrete product behavior—not placeholder features.
Remove duplicates
Delete exact duplicate records from the imported dataset.
Handle missing data
Drop rows with missing values or fill missing values using the available cleaning controls.
Trim whitespace
Remove accidental leading or trailing whitespace that creates duplicate-looking categories.
Normalize case
Standardize text case so values such as “North” and “north” can be treated consistently.
Rename columns
Replace awkward source headers with clearer names for analysis and chart building.
Remove by value + type override
Exclude unwanted values and correct a column’s effective type when source formatting caused misclassification.
From raw file to next action
The feature stays connected to the same dataset, so you do not have to recreate context in a second tool.
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1
Review quality
Check missing values, duplicates, and suspicious fields.
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2
Choose one operation
Apply a deliberate cleaning action to the imported dataset.
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3
Verify the result
Review refreshed row counts, quality status, and table data.
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4
Continue analysis
Rebuild charts, AI context, and reports from the cleaned dataset.
Know what is included before you use it.
Amridata is most useful when the product is explicit about what it does, what it stores, and where a user should verify the result.
- Safety model
- Cleaning changes the dataset copy imported into Amridata, not the original source file stored on your computer.
- AI cache
- Dataset mutations invalidate cached AI insights so a report does not quietly reuse analysis from an older version of the data.
- Best practice
- Use one intentional operation at a time and verify row counts or categories after each material change.
- Ownership
- Cleaning endpoints validate dataset access before allowing mutations.
Use it with the rest of Amridata
Analysis, cleaning, visualization, AI, and reporting are designed to work around the same dataset.
Data Cleaning questions
Clear answers about the current product behavior.
Can data cleaning change the number of rows?
Yes. Operations such as duplicate removal, dropping missing rows, or removing rows by value can reduce the row count. The workspace refreshes after the change.
Can I undo every cleaning action automatically?
The current workflow does not promise a universal one-click undo history. For important source data, keep the original file and re-upload it if you need a completely fresh copy.
Will AI insights update after I clean data?
Cleaning invalidates the cached AI insight for that dataset so a later insight generation is based on the updated dataset context.