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Novus Examples
csv496 B

Cobalt Operations Team: Leave balances

Leave balances for Cobalt Operations Team. 8 employees and a TOTAL row. remaining_hours is entitlement less taken less approved future leave, and deliberately does not subtract pending requests: 16 hours are awaiting approval and are reported in their own column. Across the team 96 hours have been taken, 16 are approved for the future and 1168 of 1280 remain. remaining_days converts at 8 hours a day.

csv

text/csv

496 B
Document Set
payroll
Industry
people-operations
Source Kit
people-operations
Synthetic
true
As Of
2026-09-08
Rows
9

Binary csv: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
payroll
Industry
people-operations
Source Kit
people-operations
Synthetic
true
As Of
2026-09-08
Rows
9
Entitlement Hours
160
Taken Hours
96
Approved Future Hours
16
Pending Hours
16
Remaining Hours
1168
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Recompute the balance and check how pending requests are treated.
Expected result
All 8 rows satisfy entitlement - taken - approved_future = remaining, and the TOTAL row equals the column sums at 1168 hours. Subtracting the 16 pending hours as well would report 1152, which is the error of treating a request as a booking.

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

“Cobalt Operations Team: Leave balances” 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: 9 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("leave-balances.csv")
print(df.head())
print(df.dtypes)

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