Willow Event Kitchen: Product mix report
Product mix report for Willow Event Kitchen. 4 menu items across 12 columns for 2026-08-20 to 2026-09-02: quantity sold, mix percent, gross, discount, net, share of net, plate cost, item cost, item margin and item food-cost percent. Quantities and net sales match the shared sales table row for row; total net is 28781.90 CAD.
csv
text/csv
- Kit
- restaurant-catering
- 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-catering
- 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
- 4
- Columns
- 12
- Total Quantity
- 1652
- Total Net Cad
- 28781.9
- Delimiter
- ,
- Encoding
- UTF-8
- Line Endings
- LF
Testing contract
Expected to pass- Scenario
- Use product mix report in the Willow Event Kitchen costing and daily operations workflow.
- Expected result
- 4 menu items across 12 columns for 2026-08-20 to 2026-09-02: quantity sold, mix percent, gross, discount, net, share of net, plate cost, item cost, item margin and item food-cost percent. Quantities and net sales match the shared sales table row for row; total net is 28781.90 CAD.
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
“Willow Event Kitchen: Product mix report” 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: 4 rows · 12 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("sales-mix-pmix.csv")
print(df.head())
print(df.dtypes)Related files
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Generated by generation/restaurant_costing.py. Free for any use, no attribution required, license.