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Alder Books Reconciliation: Bank statement CSV

Bank statement CSV for Alder Books Reconciliation. 24 transactions from 2026-08-01 to 2026-08-24 in separate debit and credit columns, with a running balance. The statement opens at 5000.00 and closes at 10236.00; 8 credits total 7300.00 and 16 debits total 2064.00.

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
Credits
8
Debits
16
Opening Balance
5000.00
Closing Balance
10236.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Import the statement and recompute the running balance from the opening figure.
Expected result
Starting at 5000.00 and applying the 24 rows in order reproduces every balance value and ends at 10236.00, which is also the cash line of trial-balance.csv.

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 statement CSV” 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-statement.csv")
print(df.head())
print(df.dtypes)

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