Data Sampling Tool

Take random or stratified samples from large CSV/JSON datasets with reproducible seed support.

Tested tool guide Tested browser tools Checked August 16, 2026

What Data Sampling Tool does and how it behaves

The Data Sampling Tool reduces a CSV or JSON dataset to a selected subset of records. It can draw across the full dataset or sample within strata, and a supplied seed makes the selection reproducible when the input and settings stay unchanged. This is useful for creating manageable test, review, or analysis files without sending the source data elsewhere. The common surprise is that a seed identifies a repeatable selection; it does not make samples from different tools or changed row orders match.

How the result is produced

Sampling modes

In random mode, every record is considered as part of one sampling pool. In stratified mode, records are grouped by the chosen stratum value before selections are made within those groups. Stratification is therefore about representation across categories, while ordinary random sampling addresses the dataset as a whole. The returned data remains a record-level subset rather than a summary.

Seeded repetition

A seed makes one set of sampling choices repeatable. To reproduce a result, keep the source content, record order, input format, sampling mode, sample settings, stratum field, and seed the same. A seed has meaning within this tool's behavior; it is not a portable specification for which records another application must choose. Processing occurs in the browser, so the dataset is not uploaded.

Good uses

Create a small, repeatable CSV fixture from a production-shaped export for testing an import.
Review examples from every labeled class in a JSON classification dataset instead of drawing only from common classes.
Give an analyst a seed and settings that regenerate the same subset from an unchanged source dataset.

Limits and checks

Compare the returned record count with the requested amount, especially when the request exceeds the available records or a stratum is very small.
Inspect counts by stratum before treating the sample as proportionally representative; the mode name alone does not establish the allocation used.
Keep the original record order and all settings when reproducing a run. The same seed alone is insufficient after data, parsing, or configuration changes.

Common questions

Does the same seed always return the same records?

It should reproduce the same selection in this tool when the entire input and every sampling setting are identical. No, the seed alone is not enough if records are added, removed, reordered, or parsed differently. It also does not promise that another sampling program using the same seed will choose the same records.

Does stratified sampling preserve the original group percentages?

Not necessarily. Stratification means selections are organized by group, but the resulting proportions depend on the sample allocation requested or applied for those groups. Check the output counts for each stratum. If you require a proportionally representative sample, verify that the selected settings produce those proportions rather than inferring them from the mode name.

References and verification

The behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.