Juniper Corner Cafe: Waste and spoilage log
Waste and spoilage log for Juniper Corner Cafe. 15 dated waste entries across 11 columns, totalling 63.66 CAD at standard cost. The quantities per ingredient are exactly the recorded waste that inventory-variance.xlsx and cogs-report.xlsx deduct.
waste_id,date,ingredient_id,ingredient,quantity,stock_unit,standard_unit_cost_cad,waste_value_cad,reason,disposal_route,recorded_by
WASTE-001,2026-08-22,ING-01,Coffee Beans,0.25,kg,20.270000,5.07,Spoilage past use-by,Food waste collection,Fictional colleague 1
WASTE-002,2026-08-27,ING-01,Coffee Beans,0.40,kg,20.270000,8.11,Prep trim beyond standard,General waste,Fictional colleague 2
WASTE-003,2026-08-22,ING-02,Whole Milk,0.30,l,1.830000,0.55,Prep trim beyond standard,General waste,Fictional colleague 2
WASTE-004,2026-08-27,ING-02,Whole Milk,0.45,l,1.830000,0.82,Dropped or damaged in service,General waste,Fictional colleague 3
WASTE-005,2026-09-01,ING-02,Whole Milk,0.20,l,1.830000,0.37,Overproduction not held,General waste,Fictional colleague 4
WASTE-006,2026-08-22,ING-03,Oat Drink,0.35,l,2.800000,0.98,Dropped or damaged in service,General waste,Fictional colleague 3
WASTE-007,2026-08-27,ING-03,Oat Drink,0.50,l,2.800000,1.40,Overproduction not held,General waste,Fictional colleague 4
WASTE-008,2026-08-22,ING-04,Sourdough Bread,2.00,each,5.860000,11.72,Overproduction not held,General waste,Fictional colleague 4
WASTE-009,2026-08-27,ING-04,Sourdough Bread,3.00,each,5.860000,17.58,Temperature breach disposal,General waste,Fictional colleague 5
WASTE-010,2026-09-01,ING-04,Sourdough Bread,1.00,each,5.860000,5.86,Spoilage past use-by,Food waste collection,Fictional colleague 6
WASTE-011,2026-08-22,ING-05,Eggs,2.00,each,0.320000,0.64,Temperature breach disposal,General waste,Fictional colleague 5
WASTE-012,2026-08-27,ING-05,Eggs,3.00,each,0.320000,0.96,Spoilage past use-by,Food waste collection,Fictional colleague 6
WASTE-013,2026-08-22,ING-06,Avocado,2.00,each,1.600000,3.20,Spoilage past use-by,Food waste collection,Fictional colleague 6
WASTE-014,2026-08-27,ING-06,Avocado,3.00,each,1.600000,4.80,Prep trim beyond standard,General waste,Fictional colleague 7
WASTE-015,2026-09-01,ING-06,Avocado,1.00,each,1.600000,1.60,Dropped or damaged in service,General waste,Fictional colleague 8
Specifications
- Kit
- restaurant-cafe
- 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
- 15
- Columns
- 11
- Total Value Cad
- 63.66
- Half Cent Tie Rows
- Rounding
- half up, 2 places
- Delimiter
- ,
- Encoding
- UTF-8
- Line Endings
- LF
Testing contract
Expected to pass- Scenario
- Use waste and spoilage log in the Juniper Corner Cafe costing and daily operations workflow.
- Expected result
- 15 dated waste entries across 11 columns, totalling 63.66 CAD at standard cost. The quantities per ingredient are exactly the recorded waste that inventory-variance.xlsx and cogs-report.xlsx deduct.
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
“Juniper Corner Cafe: Waste and spoilage log” is a deterministic Testaroo fixture for Data import, CSV parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 15 rows · 11 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("waste-spoilage-log.csv")
print(df.head())
print(df.dtypes)Generated by generation/restaurant_costing.py. Free for any use, no attribution required, license.
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