Pine Property Services: Rent roll whose unit areas do not sum to the building area
Rent roll whose unit areas do not sum to the building area for Pine Property Services. The same 6 demises with one number changed: U-03 reads 211.50 sq m instead of 230.00. Unit areas now sum to 1181.50 against the 1200.00 that every row still declares as building_area_sq_m, a shortfall of 18.50 sq m. The rent columns are untouched, so U-03 annual rent no longer equals its area times its rate either, and the two failures are the same mutation seen twice.
csv
text/csv
- 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
- 6
- Mutated Row
- 4
- Mutated Column
- area_sq_m
- Declared Building Area
- 1200.00
- Actual Area Sum
- 1181.50
- Shortfall
- 18.50
- Secondary Failure
- annual_rent no longer equals area times rate on the mutated row
Testing contract
Expected to fail- Scenario
- Sum area_sq_m and compare with building_area_sq_m, then check annual rent against area times rate on each row.
- Expected result
- Areas sum to 1181.50 against a declared 1200.00, a shortfall of 18.50 sq m on row 4 of the file. That same row now prices 211.50 sq m at 240.00, which is 50760.00 and not the 55200.00 its annual_rent column still states.
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: Rent roll whose unit areas do not sum to the building area” is a deterministic Novus Examples fixture for Data import, CSV parsing, Error handling. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 6 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("rent-roll-area-mismatch.csv")
print(df.head())
print(df.dtypes)Related files
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