By Team Edtoolkits · Data
Create reproducible samples or train, validation and test partitions.
Use the guide alongside the working tool.
Open Dataset Sampler & Splitter →Explicit working sample
id,region,product,amount,units,date
001,North,Notebook,24.5,2,2026-10-01
002,South,Pen,9,3,2026-10-02
003,North,Pen,15,5,2026-10-03
004,West,Notebook,,1,2026-10-04
005,South,Notebook,49,4,2026-10-05
006,North,Pen,1200,2,2026-10-06Create reproducible samples or train, validation and test partitions. Seeded sampling is reproducible, not cryptographic. Random or stratified sampling and random, stratified or group-isolated splitting are supported. Group mode keeps each supplied group in one partition and may miss exact target sizes. Original rows are unchanged. Partition ratios sum to 100.
Prepare the input
Create reproducible samples or train, validation and test partitions.
CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.
Review the method
Seeded sampling is reproducible, not cryptographic. Random or stratified sampling and random, stratified or group-isolated splitting are supported. Group mode keeps each supplied group in one partition and may miss exact target sizes. Original rows are unchanged. Partition ratios sum to 100.
Keep the useful output
Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.
CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.
Ready to apply this workflow?
Try Dataset Sampler & Splitter →Explore a related workflow
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Fit numeric transforms and apply saved parameters without refitting.
- Categorical Data Encoder: methods and data limits
Create reusable one-hot or ordinal category mappings.
- Class Balance & Resampling: methods and data limits
Inspect class counts and resample training records reproducibly.
- Data Anonymizer: methods and data limits
Replace selected identifiers consistently or redact sensitive columns.
Keep your original files and review the result. Found unclear instructions? Contact Team Edtoolkits.