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Novus Examples
csv799 B

Pine Property Services: Schedule of dilapidations lines

Schedule of dilapidations lines for Pine Property Services. The same 12 lines as data, with subtotal and total rows carrying an empty item_ref so they can be told apart from costed items. Only the 5 rows with a reference beginning D are remedial works; the rest are on costs, subtotals or the consequential loss.

csv

text/csv

799 B
Document Set
property
Industry
trades-property
Source Kit
trades-property
Entity
Pine Property Services
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
property
Industry
trades-property
Source Kit
trades-property
Entity
Pine Property Services
Synthetic
true
As Of
2026-09-08
Rows
12
Costed Items
5
Subtotal Rows
3
Total Claim
31089.47
Double Count Trap
true

Testing contract

Expected to pass
Scenario
Filter to the costed work items and sum them, then follow the subtotal chain down.
Expected result
The 5 D rows sum to 22430.00, the works subtotal. Summing every row with a cost gives 134268.96, which is meaningless because the subtotal and total rows are included in it.

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

“Pine Property Services: Schedule of dilapidations lines” 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: 12 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("dilapidations-schedule.csv")
print(df.head())
print(df.dtypes)

Generated by generation/industry_documents_two.py. Free for any use, no attribution required, license.