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Alder Table Bistro: Purchase pack to recipe unit conversions

Purchase pack to recipe unit conversions for Alder Table Bistro. 6 ingredients across 16 columns. Each row carries the purchase pack, the pack price, the cost per stock unit to six decimal places, the cost per recipe unit to eight, and the purchase id, date and supplier the price came from. 0 row(s) report a non-zero rounding residual because the pack size is not a whole number of stock units.

csv

text/csv

1.1 KB
Kit
restaurant-bistro
Industry
food-service
Schema Version
1
Synthetic
true
As Of
2026-09-08
Currency
CAD

Binary csv: no in-browser preview. Download it above to open in a compatible application.

Specifications

Kit
restaurant-bistro
Industry
food-service
Schema Version
1
Synthetic
true
As Of
2026-09-08
Currency
CAD
Period Start
2026-08-20
Period End
2026-09-02
Rows
6
Columns
16
Delimiter
,
Encoding
UTF-8
Line Endings
LF
Non Zero Residuals
0

Testing contract

Expected to pass
Scenario
Use purchase pack to recipe unit conversions in the Alder Table Bistro costing and daily operations workflow.
Expected result
6 ingredients across 16 columns. Each row carries the purchase pack, the pack price, the cost per stock unit to six decimal places, the cost per recipe unit to eight, and the purchase id, date and supplier the price came from. 0 row(s) report a non-zero rounding residual because the pack size is not a whole number of stock units.

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

“Alder Table Bistro: Purchase pack to recipe unit conversions” 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: 6 rows · 16 columns · UTF-8 · LF. 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("ingredient-conversions.csv")
print(df.head())
print(df.dtypes)

Generated by generation/restaurant_costing.py. Free for any use, no attribution required, license.