Tern Client Services: Expense claim whose mileage was paid at a superseded rate
Expense claim whose mileage was paid at a superseded rate for Tern Client Services. The same 6 lines as expense-claim.csv, but the three mileage amounts were computed at the superseded 0.58 per kilometre while the rate column still reads 0.62. The mileage subtotal is 136.88 where 236 km at the stated rate is 146.32, so the claim is 9.44 light and totals 212.13 instead of 221.57.
csv
text/csv
- 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
- Stated Rate Per Km
- 0.62
- Rate Actually Applied
- 0.58
- Stated Total Usd
- 212.13
- Correct Total Usd
- 221.57
- Difference Usd
- 9.44
- Intentionally Invalid
- true
Testing contract
Expected to fail- Scenario
- Recompute every claim line from its own quantity and rate columns.
- Expected result
- The 3 receipted lines recompute. All 3 mileage lines fail: 48 km at 0.62 is 29.76 but the file states 27.84. The shortfall is 9.44, which is 236 km times the 0.04 difference between the two rates.
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“Tern Client Services: Expense claim whose mileage was paid at a superseded rate” is a deterministic Novus Examples fixture for Data import, Error handling. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 6 rows. 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-mileage-rate-drift.csv")
print(df.head())
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