Guides
Apache Arrow Scanners
[Arrow Scanners](https://arrow.apache.org/docs/python/generated/pyarrow.dataset.Scanner.html) stored as variables can also be queried as if they were regular tables. Scanners read over a dataset and select specific columns or apply row-wise filtering. This is similar to how DuckDB pushes column selections and filters down into an Arrow Dataset, but using Arrow compute operations instead. Arrow can use asynchronous IO to quickly access files.  
```python
import duckdb
import pyarrow as pa
import tempfile
import pathlib
import pyarrow.parquet as pq
import pyarrow.dataset as ds
import pyarrow.compute as pc

# connect to an in-memory database
con = duckdb.connect()

my_arrow_table = pa.Table.from_pydict({'i': [1, 2, 3, 4],
'j': ["one", "two", "three", "four"]})

# create example Parquet files and save in a folder
base_path = pathlib.Path(tempfile.gettempdir())
(base_path / "parquet_folder").mkdir(exist_ok = True)
pq.write_to_dataset(my_arrow_table, str(base_path / "parquet_folder"))

# link to Parquet files using an Arrow Dataset
my_arrow_dataset = ds.dataset(str(base_path / 'parquet_folder/'))

# define the filter to be applied while scanning
# equivalent to "WHERE i = 2"
scanner_filter = (pc.field("i") == pc.scalar(2))

arrow_scanner = ds.Scanner.from_dataset(my_arrow_dataset, filter = scanner_filter)

# query the Apache Arrow scanner "arrow_scanner" and return as an Arrow Table
results = con.execute("SELECT * FROM arrow_scanner").arrow()
```