Harbour Kitchen Group: Direct debit mandate register
Direct debit mandate register for Harbour Kitchen Group. Two mandates for the same debtor account, one active and one cancelled. The active mandate has taken 8 of its 8 collections by the 2026-09-08 snapshot, 4,077.58 CAD of the 4,077.58 authorised. The cancelled mandate still shows the three collections it took before it was cancelled, because cancelling a mandate does not reverse what it already collected.
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
- 2
- Active Mandates
- 1
- Cancelled Mandates
- 1
- Collected Under Active Mandate
- 4077.58
Testing contract
Expected to pass- Scenario
- Sum collected_cad across the register and decide whether the cancelled mandate belongs in the total.
- Expected result
- The active mandate has collected 4077.58 and the cancelled one 412.80, so the account has been debited 4490.38 CAD in all. Excluding the cancelled mandate because its status is cancelled understates the account activity by 412.80.
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 mandate register” 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: 2 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-mandate-register.csv")
print(df.head())
print(df.dtypes)Related files
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