diff --git a/AGENTS.md b/AGENTS.md index 494fb3194d..db95902ed2 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -56,6 +56,10 @@ lives under `pyiceberg/`, organized by concern rather than by engine: - Large/integration libraries must be **optional extras** in `pyproject.toml`, not core `dependencies`. +### Documentation + +- Any external URL mentioned in the docs should have `` on the previous line. + ## Testing - Bias towards adding tests to existing files, rather than creating new files. diff --git a/mkdocs/docs/api.md b/mkdocs/docs/api.md index 5ab0643902..786223d47e 100644 --- a/mkdocs/docs/api.md +++ b/mkdocs/docs/api.md @@ -1893,6 +1893,7 @@ This will return a Pandas dataframe: [116981 rows x 3 columns] ``` + It is recommended to use Pandas 2 or later, because it stores the data in an [Apache Arrow backend](https://datapythonista.me/blog/pandas-20-and-the-arrow-revolution-part-i) which avoids copies of data. ### DuckDB @@ -1961,6 +1962,7 @@ Dataset( ) ``` + Using [Ray Dataset API](https://docs.ray.io/en/latest/data/api/dataset.html) to interact with the dataset: ```python @@ -1981,6 +1983,7 @@ print(ray_dataset.take(2)) ### Bodo + PyIceberg interfaces closely with Bodo Dataframes (see [Bodo Iceberg Quick Start](https://docs.bodo.ai/latest/quick_start/quickstart_local_iceberg/)), which provides a drop-in replacement for Pandas that applies query, compiler and HPC optimizations automatically. Bodo accelerates and scales Python code from single laptops to large clusters without code rewrites. @@ -2027,6 +2030,7 @@ Bodo is optimized to take advantage of Iceberg features such as hidden partition ### Daft + PyIceberg interfaces closely with Daft Dataframes (see also: [Daft integration with Iceberg](https://docs.daft.ai/en/stable/io/iceberg/)) which provides a full lazily optimized query engine interface on top of PyIceberg tables.