Alder Books Reconciliation: Journal batch with one entry out by 45.00
Journal batch with one entry out by 45.00 for Alder Books Reconciliation. The same 3 journals as journal-batch.csv except that line 2 of JB-2026-08-02 credits 19.50 where its debit line is 64.50. That one journal is out by 45.00; the other two balance exactly, which is what makes this the realistic case: a batch-level check catches it only because nothing else offsets it.
csv
text/csv
- Document Set
- accounting
- Industry
- finance
- Source Kit
- accounting-reconciliation
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 6
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
- 6
- Offending Journal
- JB-2026-08-02
- Imbalance
- 45.00
- Intentionally Invalid
- true
Testing contract
Expected to fail- Scenario
- Import the batch and validate double entry per journal, not only for the batch as a whole.
- Expected result
- JB-2026-08-02 posts 64.50 of debit against 19.50 of credit, an imbalance of 45.00. JB-2026-08-01 and JB-2026-08-03 balance, so the batch totals differ by the same 45.00; an importer that only checks the batch total still rejects it, but one that posts line by line leaves the ledger permanently out of balance.
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: Journal batch with one entry out by 45.00” is a deterministic Novus Examples fixture for Data import, Error handling. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 6 rows. 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("journal-batch-unbalanced.csv")
print(df.head())
print(df.dtypes)Related files
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