Skip to content
Novus Examples
feather1.1 KB

Feather — Dictionary-encoded Column

Feather file with dictionary-encoded strings — Arrow IPC edge-case fixture.

Preview: schema + first 5 rowsfeather
colorqty
red1
green2
red3
blue4
green5
Decoded Feather dictionary column.

Specifications

Format
Apache Feather
Edge
dictionary encoding

Testing contract

Expected to pass
Scenario
Exercise Feather — Dictionary-encoded Column in its columnar workflow. Feather file with dictionary-encoded strings — Arrow IPC edge-case fixture.
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 .feather file?

Feather (.feather) is a fast on-disk representation of Apache Arrow tables designed for zero-copy, language-agnostic data exchange between Python (pandas/pyarrow) and R. It preserves column types and structure exactly.

How to use this file

Use an example .feather file to test Arrow/Feather readers, round-trip type fidelity, and Feather-to-Parquet/CSV conversion.

How to use this file for testing

“Feather — 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: Apache Feather. 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.

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