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Alder Books Reconciliation: General ledger export

General ledger export for Alder Books Reconciliation. 66 ledger lines in four journals: GEN carries the opening balance, SAL the 8 sales invoices, CSH the 8 cash receipts and PUR the 16 supplier payments. Debits total 22464.00 and credits the same, and every document_id posts a balanced pair of lines.

csv

text/csv

5.4 KB
Document Set
accounting
Industry
finance
Source Kit
accounting-reconciliation
Synthetic
true
As Of
2026-09-08
Rows
66

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
66
Documents
33
Journals
4
Debit Total
22464.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Import the ledger and check double entry both in total and per document.
Expected result
Debits and credits each total 22464.00; grouping by document_id gives 33 documents, each with debits equal to credits; and every account_code resolves in chart-of-accounts.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: General ledger export” 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: 66 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("general-ledger-export.csv")
print(df.head())
print(df.dtypes)

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