Alder Books Reconciliation: Chart of accounts
Chart of accounts for Alder Books Reconciliation. 9 postable accounts covering assets, liabilities, equity, income and expenses, with the normal balance side declared on every row. Codes run 1000 to 5200 and every account code used anywhere in the general ledger export appears here.
csv
text/csv
- Document Set
- accounting
- Industry
- finance
- Source Kit
- accounting-reconciliation
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 9
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
- 9
- Encoding
- UTF-8
Testing contract
Reference control- Scenario
- Load the chart and check that every account posted to in the ledger export exists in it.
- Expected result
- All 9 codes are unique and every account_code in general-ledger-export.csv resolves here; the two liability accounts carry a credit normal_balance and the five debit-normal accounts are the assets and expenses.
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: Chart of accounts” 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: 9 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("chart-of-accounts.csv")
print(df.head())
print(df.dtypes)Related files
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Generated by generation/industry_documents.py. Free for any use, no attribution required, license.