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Pine Assembly Workshop: Shipment export CSV

Shipment export CSV for Pine Assembly Workshop. 16 shipments over 4 delivery rounds, 13 delivered and 3 still in transit. Every parcel is one DESK kit at 12.200 kg gross, so gross_weight_kg is always parcels times 12.200. Parcels total 56 and delivered_parcels total 44; the in-transit rows report 0 delivered and an empty delivery_date.

csv

text/csv

1.5 KB
Document Set
logistics
Industry
logistics
Source Kit
inventory-manufacturing
Synthetic
true
As Of
2026-09-08
Rows
16

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
16
Columns
14
Routes
4
Parcels
56
Delivered Parcels
44
Encoding
UTF-8
Line Endings
LF

Testing contract

Expected to pass
Scenario
Import the shipment export and recompute the weight and the delivery totals.
Expected result
All 16 rows recompute: gross_weight_kg = parcels * 12.200 and minutes_variance = actual_minutes - planned_minutes for delivered rows, 0 for the rest. Parcels sum to 56, delivered_parcels to 44, and exactly 3 rows carry an empty delivery_date.

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: Shipment export CSV” 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: 16 rows · 14 columns · UTF-8 · LF. 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("shipments-export.csv")
print(df.head())
print(df.dtypes)

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