Harbour Kitchen Group: Direct debit collection schedule
Direct debit collection schedule for Harbour Kitchen Group. 8 collections under mandate DDM-2026-0007, each 5 days after its invoice date with advance notice three days before that. The amounts are the supplier invoice totals the restaurant purchase ledger holds and sum to 4,077.58 CAD. The first collection carries sequence type FRST and the other 7 carry RCUR, which is the distinction a scheme rejects a file for getting wrong.
csv
text/csv
- Document Set
- banking
- Industry
- finance
- Source Kit
- accounting-reconciliation
- Entity
- Harbour Kitchen Group
- Synthetic
- true
- As Of
- 2026-09-08
Binary csv: no in-browser preview. Download it above to open in a compatible application.
Specifications
- Document Set
- banking
- Industry
- finance
- Source Kit
- accounting-reconciliation
- Entity
- Harbour Kitchen Group
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 8
- Total
- 4077.58
- Currency
- CAD
- First Sequences
- 1
- Recurring Sequences
- 7
- Lead Days
- 5
Testing contract
Expected to pass- Scenario
- Check both date offsets on every row and count the sequence types.
- Expected result
- All 8 rows are exactly 5 days from invoice to collection and three days from notice to collection. There is exactly 1 FRST row and 7 RCUR rows, and 8 collections fall on or before the 2026-09-08 snapshot.
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
“Harbour Kitchen Group: Direct debit collection schedule” 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. 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("direct-debit-collection-schedule.csv")
print(df.head())
print(df.dtypes)Related files
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