POS SKU Catalog (CSV)
A point-of-sale SKU catalog with tax codes and active flags — fictional Meridian Supply inventory. CSV twin.
sku,name,category,price_cents,tax_code,active
SKU-1001,Wireless Mouse,peripherals,1250,TAX-A,True
SKU-1002,USB-C Hub,peripherals,2900,TAX-A,True
SKU-1003,Cable Organiser,accessories,320,TAX-B,True
SKU-1004,Notebook A5,stationery,450,TAX-B,True
SKU-1005,Desk Mat,accessories,1800,TAX-A,False
SKU-1006,Noise-cancelling Headphones,audio,7999,TAX-A,True
SKU-1007,Laptop Stand,furniture,4500,TAX-A,True
SKU-1008,Sample Sticker Pack,promo,0,TAX-Z,True
Specifications
- Rows
- 8
- Schema
- sku, name, category, price_cents, tax_code, active
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“POS SKU Catalog (CSV)” is a deterministic Novus Examples fixture for Data import, CSV parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 8 rows · schema: sku, name, category, price_cents, tax_code, active. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import pandas as pd
df = pd.read_csv("sku-catalog.csv")
print(df.head())
print(df.dtypes)Related files
- csv10,000-Row CSVA CSV with 10,000 data rows — for testing streaming parsers, memory handling, and import performance.
- csvClean CSVA clean, well-formed CSV with a header and 20 rows — the baseline case for CSV parser testing.
- csvCSV — Hash Comment LinesA CSV with # comment lines mixed among data — common in scientific exports; tests comment skipping.
- csvCSV — Pipe DelimiterA pipe-delimited (| ) tabular file — for testing non-comma delimiter autodetection.
- csvCSV — Semicolon Delimiter (EU)European-style CSV using semicolons as delimiters and comma decimals — a common locale edge case.
- csvCSV — Single ColumnA one-column CSV of email addresses — edge case for delimiter sniffers that expect commas in every row.
Generated by generation/data_domains_wave_c.py. Free for any use, no attribution required — license.