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Pine Assembly Workshop: Packing list by carton

Packing list by carton for Pine Assembly Workshop. 168 lines describing the contents of all 56 cartons: three part lines per carton, 4 LEG, 1 PANEL and 12 SCREW, which is the leaf level of the DESK bill of materials. The carton count equals the 56 parcels on shipments-export.csv and every carton weighs 12.200 kg gross.

csv

text/csv

9.9 KB
Document Set
logistics
Industry
logistics
Source Kit
inventory-manufacturing
Synthetic
true
As Of
2026-09-08
Rows
168

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

Specifications

Document Set
logistics
Industry
logistics
Source Kit
inventory-manufacturing
Synthetic
true
As Of
2026-09-08
Rows
168
Cartons
56
Parts Per Carton
3
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Group the packing list by carton_number and reconcile the carton count and the part quantities against the bill of materials.
Expected result
56 distinct carton numbers appear, one per parcel, and each has exactly 3 part lines with quantities 4, 1 and 12. Multiplying the quantities by unit_weight_kg and adding 0.856 kg of packaging gives 12.200 kg, the declared carton_weight_kg.

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: Packing list by carton” 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: 168 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("packing-list.csv")
print(df.head())
print(df.dtypes)

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