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Novus Examples
csv213 B

Alder Books Reconciliation: Budget versus actual

Budget versus actual for Alder Books Reconciliation. 3 expense categories for 2026-08 with budget, actual, variance and variance percent. Budgets total 4200.00 against actuals of 2064.00, leaving 2136.00 unspent. Every actual equals the sum of the ledger postings to that expense account.

csv

text/csv

213 B
Document Set
accounting
Industry
finance
Source Kit
accounting-reconciliation
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
accounting
Industry
finance
Source Kit
accounting-reconciliation
Synthetic
true
As Of
2026-09-08
Rows
3
Budget Total
4200.00
Actual Total
2064.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Compare the actual column with the expense accounts on the trial balance.
Expected result
The 3 actual figures equal accounts 5000, 5100 and 5200 on trial-balance.csv, variance equals budget minus actual on every row, and variance_percent equals variance divided by budget times 100 to two decimals.

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

“Alder Books Reconciliation: Budget versus actual” 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("budget-vs-actual.csv")
print(df.head())
print(df.dtypes)

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