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Pine Assembly Workshop: Rated freight charges

Rated freight charges for Pine Assembly Workshop. 16 rated shipments. line_total equals base_charge plus weight_charge plus fuel_surcharge on every row, weight_charge equals gross_weight_kg times rate_per_kg rounded half up, and fuel_surcharge equals 0.065 of the two added together. The 13 billable rows total 861.19 and all 16 rows total 1093.24.

csv

text/csv

1.2 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
Billable Rows
13
Billable Total
861.19
All Rows Total
1093.24
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Rate the shipment export from rate-sheet.csv and compare every column with this file.
Expected result
All 16 rows reproduce to the cent. Filtering billable=true leaves 13 rows summing to 861.19, which is the total on carrier-invoice-CARR-2026-08.pdf; the three billable=false rows are the shipments still in transit and are not on the invoice.

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: Rated freight charges” 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 · 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("freight-charges.csv")
print(df.head())
print(df.dtypes)

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