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Pine Property Services: Job cost report, estimate against actual

Job cost report, estimate against actual for Pine Property Services. 12 work orders with estimate and actual side by side. actual_labour_cost equals actual_labour_hours times actual_labour_rate on every row, actual_total is that plus actual_materials, and variance is estimate_total minus actual_total. Estimates total 3858.00 against 2838.00 of recorded cost, a variance of 1020.00, which is a uniform 85.00 per work order because every estimate carries 65.00 of labour margin and 20.00 of material margin.

csv

text/csv

1.4 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
Estimate Usd
3858.00
Actual Usd
2838.00
Variance Usd
1020.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Recompute the actual and variance columns and check the uniform margin.
Expected result
All 12 rows recompute to the cent. Actuals total 2838.00, 2340.00 of labour and 498.00 of materials, against 3120.00 and 738.00 budgeted. Every single variance is 85.00 and variance_percent falls as the item gets bigger, from 35.42 on WO-01 to 21.09 on WO-12.

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: Job cost report, estimate against actual” 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("job-cost-report.csv")
print(df.head())
print(df.dtypes)

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