Tern Client Services: Contact export in the semicolon dialect
Contact export in the semicolon dialect for Tern Client Services. The same 8 contacts written with a semicolon delimiter. The billing_address field is still quoted because it contains newlines, but the tags field is now bare: "billing,renewals" needs no quotes when the delimiter is a semicolon, so the same value is quoted in contacts-export.csv and unquoted here. The file has 33 physical lines.
csv
text/csv
- 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
- Delimiter
- semicolon
- Physical Lines
- 33
- Encoding
- UTF-8
Testing contract
Expected to pass- Scenario
- Parse both contact exports with a dialect-aware reader and compare the parsed records.
- Expected result
- Both files yield identical 8-record, 9-field results once the delimiter is set correctly, including the tags value billing,renewals, which is quoted in one file and bare in the other. A reader hard-wired to commas splits the tags field of this file into two and shifts every later column.
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 in the semicolon dialect” 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 · 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-semicolon.csv")
print(df.head())
print(df.dtypes)Related files
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- jsonTern Client Services: Invoice register JSONInvoice register JSON for Tern Client Services. The same 3 invoices as invoice-register.csv with the settlement rule stated: the retainer is drawn while a balance remains and the remainder is billed. totals.outstanding is 3200.00.
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- csvTern Client Services: Professional fee scheduleProfessional fee schedule for Tern Client Services. Five rate codes. STD at 100.00 per hour is the only billing rate any time entry uses, which is why every row of timesheet-export.csv carries 100.00; OOH and TRV are defined but unused in this period and carry used_this_period false. MIL at 0.62 per kilometre is the mileage rate mileage-log.csv applies and COST at 58.00 per hour is the internal rate project-profitability.csv uses.
Generated by generation/industry_documents.py. Free for any use, no attribution required, license.