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csv955 B

Pine Property Services: Rent roll

Rent roll for Pine Property Services. The 6 demises with area, rent, passing rent and estimated rental value. Unit areas sum to 1200.00 sq m, which is the building_area_sq_m value every row carries. Passing rent is 245,475.00 USD and estimated rental value 271,475.00, the difference being the 26000.00 USD that U-06 would produce if it were let. Annual rent is area times rate on every row and quarterly rent is a quarter of passing rent.

csv

text/csv

955 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
6
Building Area
1200.00
Passing Rent
245475.00
Estimated Rental Value
271475.00
Vacant Units
1
Occupied Area
1070.00

Testing contract

Expected to pass
Scenario
Sum area_sq_m and compare with building_area_sq_m, then check annual rent against area times rate.
Expected result
Areas sum to 1200.00, matching the building area on every row. All six annual rents equal area times rate, passing rent totals 245475.00 and estimated rental value 271475.00. Summing passing rent and calling it rental value understates by 26000.00, the vacant unit.

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” 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: 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.csv")
print(df.head())
print(df.dtypes)

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