Fieldnote Supply Shop: Settlement whose deposit misses by the FX rounding
Settlement whose deposit misses by the FX rounding for Fieldnote Supply Shop. Every USD figure is identical to settlement-payout.csv, but the deposit was produced by converting each of the 41 settlement lines to EUR and rounding before summing. deposit_amount_eur reads 603.19 where the single conversion of net_payable_usd gives 603.17, a difference of 0.02 EUR that no USD column reveals.
csv
text/csv
- Document Set
- ecommerce
- Industry
- retail
- Source Kit
- retail-commerce
- 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
- ecommerce
- Industry
- retail
- Source Kit
- retail-commerce
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 16
- Correct Deposit Eur
- 603.17
- Stated Deposit Eur
- 603.19
- Difference Eur
- 0.02
- Intentionally Invalid
- true
Testing contract
Expected to fail- Scenario
- Reconcile the deposit from the USD figures on the same sheet and compare with deposit_amount_eur.
- Expected result
- net_payable_usd 661.01 converted once at 0.91250 is 603.17 EUR, but deposit_amount_eur states 603.19. The file is out by 0.02 EUR because each line was rounded to cents before summing; the USD side reconciles perfectly, which is why this class of break survives a reconciliation that only checks the settlement currency.
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
“Fieldnote Supply Shop: Settlement whose deposit misses by the FX rounding” 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: 16 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("settlement-payout-fx-rounding-mismatch.csv")
print(df.head())
print(df.dtypes)Related files
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