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Novus Examples
csv712 B

Willow Community Fund: Donor summary export

Donor summary export for Willow Community Fund. 8 supporters with their year-to-date position. pledged_total equals received_total plus outstanding_total on every row, and received_total less value_of_benefits equals deductible_total. The columns total 2850.00 pledged, 1800.00 received, 130.50 of benefits and 1669.50 deductible.

csv

text/csv

712 B
Document Set
nonprofit
Industry
nonprofit
Source Kit
nonprofit-fundraising
Synthetic
true
As Of
2026-09-08
Rows
8

Binary csv: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
nonprofit
Industry
nonprofit
Source Kit
nonprofit-fundraising
Synthetic
true
As Of
2026-09-08
Rows
8
Pledged Usd
2850.00
Deductible Usd
1669.50
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Group the pledges and receipts by donor and compare with this export.
Expected result
All 8 donors reproduce from pledge-schedule.csv and donation-receipts.csv, both identities hold on every row, and the column totals match the two source files exactly at 1800.00 and 1669.50.

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

“Willow Community Fund: Donor summary 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: 8 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("donor-export.csv")
print(df.head())
print(df.dtypes)

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