add notebook to contributing

Author: kevinjqliu

mkdocs/docs/contributing.md

@@ -211,6 +211,41 @@ export PYICEBERG_CATALOG__TEST_CATALOG__ACCESS_KEY_ID=username
 export PYICEBERG_CATALOG__TEST_CATALOG__SECRET_ACCESS_KEY=password
 ```
 
+## Notebooks for Experimentation
+
+PyIceberg provides Jupyter notebooks for quick experimentation and learning. Two Make commands are available depending on your needs:
+
+### PyIceberg Examples (`make notebook`)
+
+For basic PyIceberg experimentation without additional infrastructure:
+
+```bash
+make notebook
+```
+
+This will install notebook dependencies and launch Jupyter Lab in the `notebooks/` directory.
+
+**PyIceberg Example Notebook** (`notebooks/pyiceberg_example.ipynb`) is based on the [Getting Started with PyIceberg](https://py.iceberg.apache.org/#getting-started-with-pyiceberg) page. It demonstrates basic PyIceberg operations like creating catalogs, schemas, and querying tables without requiring any external services.
+
+### Spark Integration Examples (`make notebook-infra`)
+
+For working with PyIceberg alongside Spark, use the infrastructure-enabled notebook environment:
+
+```bash
+make notebook-infra
+```
+
+This command spins up the full integration test infrastructure via Docker Compose, including:
+
+- **Spark** (with Spark Connect)
+- **Iceberg REST Catalog** (using the [`apache/iceberg-rest-fixture`](https://hub.docker.com/r/apache/iceberg-rest-fixture) image)
+- **Hive Metastore**
+- **S3-compatible object storage** (Minio)
+
+**Spark Example Notebook** (`notebooks/spark_integration_example.ipynb`) is based on the [Spark Getting Started](https://iceberg.apache.org/docs/nightly/spark-getting-started/) guide. This notebook demonstrates how to work with PyIceberg alongside Spark, leveraging the Docker-based testing setup for a complete local development environment.
+
+After running `make notebook-infra`, open `spark_integration_example.ipynb` in the Jupyter Lab interface to explore Spark integration capabilities.
+
 ## Code standards
 
 Below are the formalized conventions that we adhere to in the PyIceberg project. The goal of this is to have a common agreement on how to evolve the codebase, but also using it as guidelines for newcomers to the project.