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Alder Books Reconciliation: Bank reconciliation

Bank reconciliation for Alder Books Reconciliation. All 24 statement transactions matched one for one to a cash-account ledger line, with a difference column that is 0.00 on every row. There are no unpresented items and no deposits in transit, so the book balance 10236.00 equals the statement balance 10236.00 and the reconciling difference is 0.00.

csv

text/csv

1.6 KB
Document Set
accounting
Industry
finance
Source Kit
accounting-reconciliation
Synthetic
true
As Of
2026-09-08
Rows
24

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
24
Matched
24
Unmatched
0
Reconciling Difference
0.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Run the reconciliation and check both the per-row differences and the closing reconciling difference.
Expected result
24 rows all report matched with a 0.00 difference, every statement_transaction_id resolves to exactly one ledger entry on account 1000, and the reconciled book balance 10236.00 equals the statement closing 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: Bank reconciliation” 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: 24 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("bank-reconciliation.csv")
print(df.head())
print(df.dtypes)

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