Column Count Mismatch (intentionally corrupt CSV)
Intentionally corrupt. A submissions export whose header declares eight columns while its three data rows carry six, ten and eight, so a reader has to choose between padding, truncating and refusing.
submission_id,form,submitted_at,full_name,email,topic,urgency,consent
SUB-9001,broken-column-count,2026-08-07T09:00:00Z,Sample Person,person@example.com,support
SUB-9002,broken-column-count,2026-08-07T09:05:00Z,Sample Person,person@example.com,support,normal,true,an extra ninth value,and a tenth
SUB-9003,broken-column-count,2026-08-07T09:10:00Z,Sample Person,person@example.com,support,normal,true
Specifications
- Corrupt
- true
- Header Columns
- 8
- Row Columns
- 6, 10, 8
- Defect
- data rows disagree with the header width
Testing contract
Expected to fail- Scenario
- Exercise Column Count Mismatch (intentionally corrupt CSV) in its form data workflow. Intentionally corrupt.
- Expected result
- Report the intended validation failure (data rows disagree with the header width); never silently treat it as a conforming input. 3 data records using ',' delimiters and UTF-8; header fields are submission_id, form, submitted_at, full_name, email, topic, urgency, consent; data-record widths (columns:count) are {"6":1,"8":1,"10":1}. Declared feature checks: corrupt=True; headerColumns=8; rowColumns=6, 10, 8; defect=data rows disagree with the header width.
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
“Column Count Mismatch (intentionally corrupt CSV)” is a deterministic Testaroo fixture for Form parsing, Form testing, CSV parsing. PDF AcroForms with documented fields and live HTML forms for testing form parsers and fillers.
Documented properties for this file: CSV · 400 bytes. 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.
This form is fictional SAMPLE content for tooling tests (autofill, scrapers, PDF fillers). It is not an official government, bank, or clinic form. Open the HTML offline or fill the PDF in any viewer; the demo submit path never stores data.
Code examples
import pandas as pd
df = pd.read_csv("broken-column-count-mismatch.csv")
print(df.head())
print(df.dtypes)Related files
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Generated by generation/forms_p7.py. Free for any use, no attribution required, license.