import duckdb import pyarrow as pa my_arrow_table = pa.Table.from_pydict({'i': [1, 2, 3, 4], 'j': ["one", "two", "three", "four"]}) # query the Apache Arrow Table "my_arrow_table" and return as an Arrow Table results = duckdb.sql("SELECT * FROM my_arrow_table").fetch_arrow_table() 

import duckdb import pyarrow as pa my_arrow_table = pa.Table.from_pydict({'i': [1, 2, 3, 4], 'j': ["one", "two", "three", "four"]}) # query the Apache Arrow Table "my_arrow_table" and return as an Arrow RecordBatchReader chunk_size = 1_000_000 result = duckdb.sql("SELECT * FROM my_arrow_table").arrow(chunk_size) # Loop through the results. A StopIteration exception is thrown when the RecordBatchReader is empty while (batch := result.read_next_batch()): # Process a single chunk here  print(batch.to_pandas()) 

import duckdb # connect to an in-memory database con = duckdb.connect() con.execute('CREATE TABLE integers (i integer)') con.execute('INSERT INTO integers VALUES (0), (1), (2), (3), (4), (5), (6), (7), (8), (9), (NULL)') # Create a relation from the table and export the entire relation as Arrow rel = con.table("integers") relation_as_arrow = rel.to_arrow_table() # or .fetch_arrow_table()  # Calculate a result using that relation and export that result to Arrow res = rel.aggregate("sum(i)").execute() arrow_table = res.to_arrow_table() # or .fetch_arrow_table()  # You can also create an Arrow record batch reader from a relation arrow_batch_reader = res.arrow() # or .fetch_arrow_reader() while (batch := arrow_batch_reader.read_next_batch()): # Process a single chunk here  print(batch.to_pandas())