pdf2.3 KB

Fieldnote Supply Shop: Credit note PDF

Credit note PDF for Fieldnote Supply Shop. A one-page credit note reversing the whole of line LINE-007 on order ORD-004: merchandise 32.00, tax 3.28 and a total credit of 35.28. It states on its face that the order promotion is not reversed.

Data import

Rendered preview of Fieldnote Supply Shop: Credit note PDF

Rendered preview of the pdf file (2.3 KB). Download above for the original.

Specifications

Document Set: ecommerce
Industry: retail
Source Kit: retail-commerce
Synthetic: true
As Of: 2026-09-08
Pages: 1
Refund Id: RFND-004
Total Credit: 35.28

Testing contract

Expected to pass

Scenario: Extract the credit note and reconcile it with the refund and RMA registers.
Expected result: The credited merchandise 32.00 and tax 3.28 equal the RFND-004 row of refunds.csv, and the return authorisation quoted, RMA-2026-0004, is the row of returns-rma.csv whose refund_id is RFND-004.

What is a .pdf file?

PDF (Portable Document Format) is a page-oriented format that fixes layout, fonts, and vector and raster graphics so a page renders identically anywhere. A `%PDF-` header is followed by numbered objects, a cross-reference table mapping each to a byte offset, and a trailer; edits append incremental updates rather than rewrite the file. Page content is a stream of drawing operators, so a PDF holds no words or paragraphs, only positioned glyph runs. Adobe released it in 1993 and gave it to ISO as ISO 32000-1 in 2008.

How to use this file

Use an example PDF to test text extraction, rendering, metadata parsing, AcroForm handling, and OCR pipelines: checking that extraction reconstructs reading order from glyph positions, that a scanned page yields no text, and that an incremental update leaves earlier revisions in the file.

How to use this file for testing

“Fieldnote Supply Shop: Credit note PDF” 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: 1 pages. 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.

Document fixtures list their internal structure (pages, fields, tracked changes, embedded objects) in the spec table. Test extractors, converters, and OCR against that known structure, and compare searchable↔scanned or format-twin companions when present.

Code examples

import pdfplumber  # pip install pdfplumber

with pdfplumber.open("credit-note-RFND-004.pdf") as pdf:
    print(len(pdf.pages), "pages")
    print(pdf.pages[0].extract_text())