Skip to content
Novus Examples
csv336 B

Fieldnote Supply Shop: Open and resolved chargebacks

Open and resolved chargebacks for Fieldnote Supply Shop. Two disputes against orders ORD-006 and ORD-009. Neither has been debited yet, which is why the 661.01 settlement contains no chargeback line: CB-2026-0001 is still open and CB-2026-0002 was won. The disputed amounts equal the captures on those orders exactly.

csv

text/csv

336 B
Document Set
ecommerce
Industry
retail
Source Kit
retail-commerce
Synthetic
true
As Of
2026-09-08
Rows
2

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
2
Open Disputes
1
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Cross-check the chargeback file against the settlement detail for debits.
Expected result
Both order_id values resolve in orders-export.csv, disputed_amount equals the capture on the same order (135.00 and 120.00), and settlement-transactions.csv correctly contains no chargeback line for either.

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: Open and resolved chargebacks” 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: 2 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("chargebacks.csv")
print(df.head())
print(df.dtypes)

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