retail-orders.csv
A category-and-measure dataset for the first complete journey: table inspection, grouped revenue, chart building, and profile summaries.
SAMPLE DATA · OPTIONAL DOWNLOADS
Try the workflow
with a sample file.
Explore imports, cleaning, profiling, and charts with these small sample files. Download a file, open Data Lab, and import it when you are ready.
Choose a test fixture
Pick the file that matches what you want to learn or verify.
clean-small.csv
A compact, clean baseline. Use it to learn table sorting, aggregation choices, and export without edge-case noise.
edge-cases.csv
Quoted commas, embedded line breaks, blank cells, repeated records, and mixed-looking values for parser and cleaning checks.
regions.tsv
A tab-separated file for checking delimiter detection and building a grouped comparison from regional values.
mixed-records.json
Objects with different fields, nested values, missing properties, and mixed scalar types. Useful for understanding JSON normalization.
events.jsonl
One JSON object per line, with timestamp-like labels, nulls, and an optional query field. Use it to check line-oriented import.
chart-measure-cases.csv
Separates a genuine zero, a negative value, and a category with no usable numeric measures. Use it to verify charts never invent zero values.
monthly-trend.csv
Chronological monthly values that intentionally rise and dip. Use it to check the line-chart sequence stays in source order rather than sorting by value.
Download a fixture
Choose the file matching the workflow you want to try.
Import the file
Open Data Lab and select the downloaded file. It stays in this tab.
Explore and export
Try transformations, inspect charts and profiles, then export if you want to keep a result.