Harbour Kitchen Group: Payroll deduction rates used by the T4 shaped slips
Payroll deduction rates used by the T4 shaped slips for Harbour Kitchen Group. The 5 rates behind every T4 box in this set, with the basis each one is applied to and an explicit statement that no maximum, band or personal exemption is applied. Two rows are multipliers of another row rather than percentages of income, so a reader that treats the rate column as one thing gets the employer lines wrong.
csv
text/csv
- Document Set
- tax
- Industry
- retail
- Source Kit
- retail-commerce
- 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
- tax
- Industry
- retail
- Source Kit
- retail-commerce
- Entity
- Harbour Kitchen Group
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 5
- Multiplier Rows
- 2
- Maxima Applied
- false
- Exemptions Applied
- false
Testing contract
Expected to pass- Scenario
- Apply each rate to the basis its row names and reproduce the T4 slip columns.
- Expected result
- The three employee rates applied to box 14 reproduce boxes 16, 18 and 22 on all 8 slips, and the two employer rows reproduce 546.82 and 213.58 from the employee totals rather than from income.
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: Payroll deduction rates used by the T4 shaped slips” 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: 5 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("payroll-deduction-rates.csv")
print(df.head())
print(df.dtypes)Related files
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Generated by generation/industry_documents_two.py. Free for any use, no attribution required, license.