Copper Oven Bakery — Intentionally invalid purchase import
Intentionally invalid purchase import for Copper Oven Bakery. 4 data rows, 8 columns and 0 expected accepted rows.
Supplier,Item,Purchase date,Quantity,Unit,Unit price,Total price,Currency
,Bread Flour,2026-07-15,14,kg,1.3,18.2,CAD
Alder Wholesale,,2026-07-15,14,kg,1.3,18.2,CAD
Alder Wholesale,Bread Flour,not-a-date,14,kg,1.3,18.2,CAD
Alder Wholesale,Bread Flour,2026-07-15,14,kg,1.3,-10,CAD
Specifications
- Kit
- restaurant-bakery
- Industry
- food-service
- Schema Version
- 1
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 4
- Columns
- 8
- Encoding
- UTF-8
Testing contract
Expected to fail- Scenario
- Use intentionally invalid purchase import in the Copper Oven Bakery purchasing, price-history, menu-costing, inventory, sales, reviews, modifiers, refunds, reservations, staffing, stock-movements, menu-pricing workflow.
- Expected result
- 4 data rows, 8 columns and 0 expected accepted rows.
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
“Copper Oven Bakery — Intentionally invalid purchase import” is a deterministic Novus Examples fixture for Data import, Error handling. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 4 rows · 8 columns · UTF-8. 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("invalid-purchases.csv")
print(df.head())
print(df.dtypes)Related files
- jsonAlder Books Reconciliation — Intentionally invalid semantic test recordsIntentionally invalid semantic test records for Alder Books Reconciliation. Three independent cases report duplicate-primary-key, schema-type and foreign-key errors. JSON syntax remains valid.
- csvAlder Table Bistro — Intentionally invalid purchase importIntentionally invalid purchase import for Alder Table Bistro. 4 data rows, 8 columns and 0 expected accepted rows.
- csvAlder Table Bistro — Intentionally invalid review importIntentionally invalid review import for Alder Table Bistro. 4 data rows, 5 columns and 0 expected accepted rows.
- jsonAlder Table Bistro — Intentionally invalid semantic test recordsIntentionally invalid semantic test records for Alder Table Bistro. Three independent cases report duplicate-primary-key, schema-type and foreign-key errors. JSON syntax remains valid.
- csvCedar Street Tacos — Intentionally invalid purchase importIntentionally invalid purchase import for Cedar Street Tacos. 4 data rows, 8 columns and 0 expected accepted rows.
- csvCedar Street Tacos — Intentionally invalid review importIntentionally invalid review import for Cedar Street Tacos. 4 data rows, 5 columns and 0 expected accepted rows.
Generated by generation/business_fixtures.py. Free for any use, no attribution required, license.