Fieldnote Learning Centre: Certificate eligibility with three conditions
Certificate eligibility with three conditions for Fieldnote Learning Centre. All 18 enrolments tested against three conditions: the enrolment is complete, the score reaches the pass mark, and both sessions were attended. 7 are eligible. 9 enrolments passed, so 2 passing learners are still ineligible on attendance alone: ENR-04 and ENR-11, each of whom attended 1 of 2 sessions. Every ineligible row names its reasons.
csv
text/csv
- Document Set
- education
- Industry
- education
- Source Kit
- education-training
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 18
Binary csv: no in-browser preview. Download it above to open in a compatible application.
Specifications
- Document Set
- education
- Industry
- education
- Source Kit
- education-training
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 18
- Eligible
- 7
- Passed
- 9
- Blocked On Attendance
- 2
- Encoding
- UTF-8
Testing contract
Expected to pass- Scenario
- Apply all three conditions and compare the eligible count with the pass count.
- Expected result
- 7 rows are eligible, not the 9 that passed. The difference is exactly ENR-04 and ENR-11, which carry passed true, sessions_attended 1 and a reason naming the shortfall. Treating a pass as a certificate would issue 2 certificates the attendance record does not support.
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
“Fieldnote Learning Centre: Certificate eligibility with three conditions” 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: 18 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("certificate-eligibility.csv")
print(df.head())
print(df.dtypes)Related files
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- jsonFieldnote Learning Centre: Education reconciliation assertionsEducation reconciliation assertions for Fieldnote Learning Centre. The six identities this set claims with the figure each must produce: 5250.00 of gross fees, 4442.50 net of bursary, 9 passes, 7 certificates, 18 results in both gradebook shapes and 2600.00 of scheduled instalments. It also records both deliberate defects and their exact size.
Generated by generation/industry_documents.py. Free for any use, no attribution required, license.