docs: add "Access Buckets from Code" page

docs/hub/_toctree.yml

@@ -421,6 +421,8 @@
   new: true
   isExpanded: true
   sections:
+  - local: storage-buckets-access
+    title: Access Patterns
   - local: storage-buckets-security
     title: Bucket Security
 

docs/hub/storage-buckets-access.md

@@ -0,0 +1,68 @@
+# Access Patterns
+
+Beyond the [CLI and Python SDK](./storage-buckets#managing-files), there are several ways to access bucket data from your existing tools and workflows.
+
+## Choosing an Access Method
+
+| Method | Best for | Details |
+|--------|----------|---------|
+| **hf-mount** | Mount as local filesystem — any tool works | [See below](#mount-as-a-local-filesystem) |
+| **Volume mounts** | HF Jobs & Spaces (same idea, managed for you) | [See below](#volume-mounts-in-jobs-and-spaces) |
+| **hf:// paths** (fsspec) | Python data tools (pandas, DuckDB) | [See below](#python-data-tools) |
+| **CLI sync** | Batch transfers, backups | [Sync docs](./storage-buckets#syncing-directories) |
+
+## Mount as a Local Filesystem
+
+[hf-mount](https://github.com/huggingface/hf-mount) lets you mount buckets (and repos) as local filesystems via NFS (recommended) or FUSE. Files are fetched lazily — only the bytes your code reads hit the network.
+
+Install:
+
+```bash
+curl -fsSL https://raw.githubusercontent.com/huggingface/hf-mount/main/install.sh | sh
+```
+
+Mount a bucket:
+
+```bash
+hf-mount start bucket username/my-bucket /mnt/data
+```
+
+Once mounted, any tool that reads or writes files works with your bucket — pandas, DuckDB, vLLM, training scripts, shell commands, etc.
+
+> [!TIP]
+> Buckets are mounted read-write; repos are read-only. See the [hf-mount repository](https://github.com/huggingface/hf-mount) for full documentation including backend options, caching, and write modes.
+
+## Volume Mounts in Jobs and Spaces
+
+Volume mounts in [Jobs](./jobs) and [Spaces](./spaces) are the same idea as `hf-mount`, managed for you by the platform — no extra setup needed. Buckets are mounted read-write by default.
+
+```bash
+hf jobs run -v hf://buckets/username/my-bucket:/data python:3.12 python script.py
+```
+
+For the full volume mount syntax and Python API, see the [Jobs configuration docs](./jobs-configuration#volumes) and the [Spaces volume mount guide](/docs/huggingface_hub/guides/manage-spaces#mount-volumes-in-your-space).
+
+## Python Data Tools
+
+The [`HfFileSystem`](/docs/huggingface_hub/guides/hf_file_system) provides [fsspec](https://filesystem-spec.readthedocs.io)-compatible access to buckets using `hf://buckets/` paths. Any Python library that supports fsspec can read and write bucket data directly.
+
+**pandas:**
+
+```python
+import pandas as pd
+
+df = pd.read_parquet("hf://buckets/username/my-bucket/data.parquet")
+df.to_parquet("hf://buckets/username/my-bucket/output.parquet")
+```
+
+**DuckDB** (Python client):
+
+```python
+import duckdb
+from huggingface_hub import HfFileSystem
+
+duckdb.register_filesystem(HfFileSystem())
+duckdb.sql("SELECT * FROM 'hf://buckets/username/my-bucket/data.parquet' LIMIT 10")
+```
+
+For more on `hf://` paths and supported operations, see the [`HfFileSystem` guide](/docs/huggingface_hub/guides/hf_file_system) and the [Buckets Python guide](/docs/huggingface_hub/guides/buckets).

docs/hub/storage-buckets.md

@@ -7,6 +7,9 @@ You can interact with buckets using the Hub web interface, the [`hf` CLI](https:
 > [!TIP]
 > Buckets are available to all users and organizations. See [hf.co/storage](https://huggingface.co/storage) for pricing details.
 
+> [!TIP]
+> Want to use bucket data with pandas, DuckDB, or mount buckets as a local filesystem? See [Access Patterns](./storage-buckets-access).
+
 ## Buckets vs Repositories
 
 The Hub offers two types of storage: Git-based **repositories** for versioned, collaborative work and **buckets** for fast, mutable object storage.