Alder Books Reconciliation: Trial balance out by 1250.00
Trial balance out by 1250.00 for Alder Books Reconciliation. Identical to trial-balance.csv except that account 5200 Services expense is stated as 1923.00 instead of 673.00. Debits therefore come to 14350.00 against credits of 13100.00, a difference of exactly 1250.00. The TOTAL row is recalculated and agrees with the account rows, so the file is internally consistent and only the double-entry rule is broken.
csv
text/csv
- Document Set
- accounting
- Industry
- finance
- Source Kit
- accounting-reconciliation
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 10
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
- 10
- Debit Total
- 14350.00
- Credit Total
- 13100.00
- Difference
- 1250.00
- Offending Account
- 5200
- Intentionally Invalid
- true
Testing contract
Expected to fail- Scenario
- Load the trial balance and check that total debits equal total credits.
- Expected result
- Debits 14350.00 exceed credits 13100.00 by exactly 1250.00. The single cause is account 5200, which reports 1923.00 where the general ledger supports 673.00; every other account agrees with general-ledger-export.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: Trial balance out by 1250.00” 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: 10 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("trial-balance-out-of-balance.csv")
print(df.head())
print(df.dtypes)Related files
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