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Novus Examples
csv747 B

Tern Client Services: Expense claim with mileage

Expense claim with mileage for Tern Client Services. 6 claim lines for August: 3 mileage lines worth 146.32 and 3 receipted lines worth 75.25, 221.57 in total. amount equals quantity times rate on every line, including the mileage lines where quantity is kilometres. Each line carries the project it belongs to and a receipt reference.

csv

text/csv

747 B
Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
6

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

Specifications

Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
6
Mileage Lines
3
Receipted Lines
3
Total Usd
221.57
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Import the claim, recompute every line and allocate the total by project.
Expected result
All 6 lines recompute and sum to 221.57. The mileage subset matches mileage-log.csv line for line. Allocating by project gives 43.76, 107.94 and 69.87, the expense column of project-profitability.csv.

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

“Tern Client Services: Expense claim with mileage” 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: 6 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("expense-claim.csv")
print(df.head())
print(df.dtypes)

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