> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oleander.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Tasks

> Interactive Python and TypeScript environments with pre-wired lake access.

Tasks are interactive execution environments built into the platform. Each task gives you a persistent Python or TypeScript sandbox with your catalogs, lake tables, and external connections already wired in - no credentials to configure, no installs to run.

<Tabs>
  <Tab title="Python">
    The Python sandbox runs Python 3.13 and comes with the following libraries pre-installed:

    | Library      | Purpose                |
    | ------------ | ---------------------- |
    | `polars`     | DataFrame processing   |
    | `pandas`     | DataFrame processing   |
    | `duckdb`     | In-process SQL         |
    | `pyiceberg`  | Iceberg catalog client |
    | `numpy`      | Numerical computing    |
    | `matplotlib` | Plotting               |
    | `pyarrow`    | Arrow/Parquet I/O      |

    ### The `oleander` module

    Every cell has access to a generated `oleander` module that wires your catalog and DuckDB connection automatically:

    ```python theme={null}
    # Pre-wired DuckDB connection with all your Iceberg catalogs attached
    conn = oleander.conn

    # Default Lakekeeper catalog as a pyiceberg catalog object
    catalog = oleander.default_catalog

    # Get any registered catalog by name
    my_catalog = oleander.get_catalog("my_catalog_name")
    ```

    Query your lake directly:

    ```python theme={null}
    result = conn.execute("SELECT * FROM oleander.default.flowers LIMIT 10").df()
    result
    ```

    Use pyiceberg for low-level catalog operations:

    ```python theme={null}
    table = catalog.load_table("default.flowers")
    df = table.scan().to_pandas()
    df.head()
    ```

    ### BigQuery

    If you have BigQuery connections configured, the DuckDB `bigquery` extension is installed automatically. BigQuery tables are accessible as `connection_name.dataset.table`:

    ```python theme={null}
    result = conn.execute("""
      SELECT date, SUM(revenue) AS total
      FROM my_bq.analytics.orders
      WHERE date >= '2024-01-01'
      GROUP BY 1
      ORDER BY 1
    """).df()
    ```

    ### State persistence

    Variables persist across cell executions within a session. If you define a DataFrame in one cell, it is available in the next. State is stored in `/tmp/_oleander_state.pkl` and survives cell reruns.

    ### Output

    DataFrames are displayed as structured tables. Matplotlib figures are rendered as inline SVG. Other values are printed as text.
  </Tab>

  <Tab title="TypeScript">
    The TypeScript sandbox runs Node 24 with `tsx` for execution. A generated `oleander` module exposes your Iceberg catalogs:

    ```typescript theme={null}
    import { defaultCatalog, getCatalog, catalogs } from './oleander';

    // List all catalogs
    console.log(catalogs.map(c => c.name));

    // Load a table from the default catalog
    const table = await defaultCatalog.loadTable({ namespace: ['default'], name: 'flowers' });
    ```
  </Tab>
</Tabs>

## Code generation

Each cell has an assist button that generates code based on your prompt, aware of your available libraries and catalog structure. Generated code appears as a new cell ready to run.

## Spark cells

Submit inline PySpark code as a managed Spark job directly from a task cell. The job runs in the `oleander.tasks` namespace and returns a run ID you can track in the platform. Lineage from Spark cell runs is captured automatically.