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Pine Assembly Workshop: Shipment tracking event export

Shipment tracking event export for Pine Assembly Workshop. 74 scan events in chronological order per shipment: five for each of the 13 delivered shipments and three for each of the 3 still in transit, which never reach OUTFORDEL or DELIVERED. The DELIVERED timestamp on each shipment equals the delivered_time recorded for it on proof-of-delivery.csv.

csv

text/csv

5.9 KB
Document Set
logistics
Industry
logistics
Source Kit
inventory-manufacturing
Synthetic
true
As Of
2026-09-08
Rows
74

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
74
Shipments
16
Delivered Scans
13
Encoding
UTF-8
Line Endings
LF

Testing contract

Expected to pass
Scenario
Import the event stream and rebuild the current status of every shipment from its last event.
Expected result
Grouping by shipment_id gives 16 shipments; the 13 whose last event is DELIVERED match the delivered rows of shipments-export.csv exactly, and the 3 whose last event is TRANSIT match the in-transit rows. There are exactly 13 DELIVERED scans and their times equal the proof-of-delivery times.

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 tracking event export” 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: 74 rows · 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("tracking-events.csv")
print(df.head())
print(df.dtypes)

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