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Novus Examples
csv363 B

Pine Assembly Workshop: Delivery route plan and capacity

Delivery route plan and capacity for Pine Assembly Workshop. 4 rounds with 4 stops each. Parcels per round are 8, 12, 16, 20 against a vehicle capacity of 30 parcels, so capacity_used_percent runs from 26.67 to 66.67 and no round is over capacity. freight_charged_usd counts only delivered shipments and sums to 861.19.

csv

text/csv

363 B
Document Set
logistics
Industry
logistics
Source Kit
inventory-manufacturing
Synthetic
true
As Of
2026-09-08
Rows
4

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
4
Stops Per Route
4
Parcels
56
Freight Usd
861.19
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Group the shipment export by route and compare the parcel counts and the utilisation.
Expected result
All 4 rounds reproduce: parcels sum to 56, every capacity_used_percent equals parcels times 100 divided by 30 rounded half up to two decimals, no round exceeds 100, and freight_charged_usd sums to 861.19, the carrier invoice total.

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: Delivery route plan and capacity” 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: 4 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("route-plan.csv")
print(df.head())
print(df.dtypes)

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