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Tern Client Services: Contact export with quoted fields

Contact export with quoted fields for Tern Client Services. 8 contacts in 33 physical lines, because every billing_address is a four-line address held in one quoted field. The addresses also contain commas, the notes contain doubled double-quote characters where a quotation appears, and the tags field holds the comma-separated value "billing,renewals" inside quotes. Every one of those is correctly escaped RFC 4180.

csv

text/csv

2 KB
Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
8

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
8
Physical Lines
33
Columns
9
Embedded Newlines
true
Embedded Quotes
true
Delimiter
comma
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Read the file with a real CSV parser and then with a line-splitting, comma-splitting reader.
Expected result
A real parser returns 8 records of 9 fields, with the first address ending "ZZ1 1ZZ" and the notes containing the single characters " around the job title. A reader that splits on newlines sees 33 rows and a reader that splits on commas finds a varying field count on every record.

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: Contact export with quoted fields” 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: 8 rows · 9 columns · 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("contacts-export.csv")
print(df.head())
print(df.dtypes)

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