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Novus Examples
csv210 B

Tern Client Services: Retainer ledger

Retainer ledger for Tern Client Services. A three-line retainer account: a 4000.00 deposit received on 2026-07-28 and two draws of 1600.00 and 2400.00 applied when SVCINV-1 and SVCINV-2 were raised. The running balance closes at 0.00, which is why SVCINV-3 is billed for payment rather than drawn.

csv

text/csv

210 B
Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
3

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
3
Deposit Usd
4000.00
Closing Balance Usd
0.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Replay the ledger and check the running balance and the closing figure.
Expected result
The balance column is the running total of the amount column: 4000.00, 2400.00, then 0.00. The two draws equal the amounts of SVCINV-1 and SVCINV-2 on invoice-register.csv exactly, and no draw exists for SVCINV-3 because the balance was already nil.

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: Retainer ledger” 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: 3 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("retainer-ledger.csv")
print(df.head())
print(df.dtypes)

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