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csv1.2 KB

Pine Property Services: Material purchase register

Material purchase register for Pine Property Services. 12 material purchases, one per work order, alternating between two fictional merchants. The amounts are the materials column of the job cost report and sum to 498.00, which is the 02-200 actual on cost-code-summary.csv. Every purchase is dated the day its work order was raised.

csv

text/csv

1.2 KB
Document Set
construction
Industry
trades-property
Source Kit
trades-property
Synthetic
true
As Of
2026-09-08
Rows
12

Binary csv: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
construction
Industry
trades-property
Source Kit
trades-property
Synthetic
true
As Of
2026-09-08
Rows
12
Suppliers
2
Total Usd
498.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Total the register by supplier and by work order and reconcile with the cost code summary.
Expected result
The 12 purchases sum to 498.00 and split 240.00 and 258.00 between the two merchants. Each work_order_id appears exactly once and its amount equals the materials column of that work order on job-cost-report.csv.

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: Material purchase register” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: 12 rows · UTF-8. 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("material-purchases.csv")
print(df.head())
print(df.dtypes)

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