Parquet — Dictionary-encoded Column
Parquet with dictionary-encoded string column — tests dictionary page decoding.
| id | name | score | note |
|---|---|---|---|
| null | null | null | null |
| null | null | null | null |
| null | null | null | null |
| null | null | null | null |
| null | null | null | null |
Specifications
- Edge
- dictionary encoding
- Rows
- 5
- Seed
- 42042
Testing contract
Expected to pass- Scenario
- Exercise Parquet — Dictionary-encoded Column in its columnar workflow. Parquet with dictionary-encoded string column — tests dictionary page decoding.
- Expected result
- 5 rows, 2 columns; fields: color: dictionary<values=string, indices=int8, ordered=0>; qty: int64; column null counts=[0, 0]. Declared feature checks: edge=dictionary encoding.
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 — Dictionary-encoded Column” 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 · 5 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("dictionary-encoded.parquet")
print(df.head())
print(df.dtypes)Related files
- jsonColumnar Nulls Schema (JSON)JSON description of nullable columns in the null-heavy columnar fixtures.
- featherFeather — Dictionary-encoded ColumnFeather file with dictionary-encoded strings — Arrow IPC edge-case fixture.
- featherFeather — Null-heavy ColumnsFeather/Arrow IPC twin of the null-heavy table — grouped with the Parquet nulls fixture.
- parquetParquet — Decimal as String ColumnAmounts stored as strings in Parquet — common ingestion edge case for ETL parsers.
- 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.
Generated by generation/data_wave_f.py. Free for any use, no attribution required, license.