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csv441 B

Pine Assembly Workshop: Indented bill of materials

Indented bill of materials for Pine Assembly Workshop. The DESK structure exploded to 2 levels in 6 rows, with an indent column so the tree is readable and an is_assembly flag so leaves can be told from sub-assemblies. Per finished unit the leaves are 4 LEG, 1 PANEL and 12 SCREW, which cost 25.20 USD. The 35.00 USD of sub-assembly rows is the same material counted again at a higher level and must not be added to it.

csv

text/csv

441 B
Document Set
manufacturing
Industry
manufacturing
Source Kit
inventory-manufacturing
Entity
Pine Assembly Workshop
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
manufacturing
Industry
manufacturing
Source Kit
inventory-manufacturing
Entity
Pine Assembly Workshop
Synthetic
true
As Of
2026-09-08
Rows
6
Levels
2
Leaf Rows
4
Assembly Rows
2
Material Cost Per Unit
25.20
Double Count Trap
true

Testing contract

Expected to pass
Scenario
Sum the extended cost of the leaf rows, then sum every row and compare.
Expected result
The leaf rows sum to 25.20, the material content of one finished unit. Summing every row gives 60.20, which double counts the 35.00 of sub-assemblies; the is_assembly column is there so that cannot happen by accident.

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 Assembly Workshop: Indented bill of materials” 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("bill-of-materials-indented.csv")
print(df.head())
print(df.dtypes)

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