Parquet — Null-heavy Columns
Tiny Parquet table with null name/score/note cells — tests null handling in columnar readers.
| id | name | score | note |
|---|---|---|---|
| 1 | Alpha | 90 | null |
| 2 | null | null | missing name |
| 3 | Gamma | 88.5 | null |
Specifications
- Edge
- null cells
- Rows
- 3
- Seed
- 42042
Testing contract
Expected to pass- Scenario
- Exercise Parquet — Null-heavy Columns in its columnar workflow. Tiny Parquet table with null name/score/note cells — tests null handling in columnar readers.
- Expected result
- 3 rows, 4 columns; fields: id: int64; name: string; score: double; note: string; column null counts=[0, 1, 1, 2]. Declared feature checks: edge=null cells.
What is a .parquet file?
Apache Parquet (.parquet) is a binary, columnar storage format for analytical data. It stores each column separately with per-column compression and encoding, embeds a schema and statistics, and is the de-facto standard for data lakes and engines like Spark, DuckDB, and pandas/pyarrow.
How to use this file
Use an example .parquet file to test columnar readers (pyarrow, DuckDB, Spark), schema and predicate-pushdown handling, and Parquet-to-CSV/JSON converters.
How to use this file for testing
“Parquet — Null-heavy Columns” is a deterministic Novus Examples fixture for Data import, Conversion testing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: seed 42042 · 3 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 # pip install pyarrow
df = pd.read_parquet("nulls-heavy.parquet")
print(df.head())
print(df.dtypes)Related files
- featherFeather — Dictionary-encoded ColumnFeather file with dictionary-encoded strings — Arrow IPC edge-case fixture.
- parquetParquet — Decimal as String ColumnAmounts stored as strings in Parquet — common ingestion edge case for ETL parsers.
- parquetParquet — Dictionary-encoded ColumnParquet with dictionary-encoded string column — tests dictionary page decoding.
- parquetParquet — Duplicate Key ColumnParquet with repeated key values — tests join/aggregation edge cases.
- parquetParquet — Empty TableEmpty Parquet file with schema but no rows — edge case for readers.
- parquetParquet — List ColumnParquet with a list-of-string column — tests repeated-field decoding.
Generated by generation/data_wave_f.py. Free for any use, no attribution required, license.