.. _extras: Extras ====== BigQuery Integration -------------------- `pandas-pyarrow` supports seamless integration with BigQuery, allowing explicit conversion between BigQuery-specific dtypes and PyArrow. Install with the BigQuery extra: .. code-block:: bash pip install pandas-pyarrow[bigquery] Example: .. code-block:: python import pandas_gbq as gbq from pandas_pyarrow import PandasArrowConverter query = "SELECT * FROM `bigquery-public-data.austin_311.311_service_requests` LIMIT 1000" df = gbq.read_gbq(query) converter = PandasArrowConverter() adf = converter(df) print("Original types:\n", df.dtypes, "\n\nPyArrow types:\n", adf.dtypes) Output Example: .. code-block:: unique_key object complaint_description object source object status object status_change_date datetime64[us, UTC] created_date datetime64[us, UTC] last_update_date datetime64[us, UTC] close_date datetime64[us, UTC] incident_address object street_number object street_name object city object dtype: object Converted PyArrow-backed DataFrame types: .. code-block:: unique_key string[pyarrow] complaint_description string[pyarrow] source string[pyarrow] status string[pyarrow] status_change_date timestamp[us, tz=UTC][pyarrow] created_date timestamp[us, tz=UTC][pyarrow] last_update_date timestamp[us, tz=UTC][pyarrow] close_date timestamp[us, tz=UTC][pyarrow] incident_address string[pyarrow] street_number string[pyarrow] street_name string[pyarrow] city string[pyarrow] dtype: object