awswrangler.db.read_sql_table(table: str, con: sqlalchemy.engine.base.Engine, schema: Optional[str] = None, index_col: Optional[Union[str, List[str]]] = None, params: Optional[Union[List[Any], Tuple[Any, ], Dict[Any, Any]]] = None, chunksize: Optional[int] = None, dtype: Optional[Dict[str, pyarrow.lib.DataType]] = None, safe: bool = True) → Union[pandas.core.frame.DataFrame, Iterator[pandas.core.frame.DataFrame]]

Return a DataFrame corresponding to the result set of the query string.

Support for Redshift, PostgreSQL and MySQL.


Redshift: For large extractions (1MM+ rows) consider the function wr.db.unload_redshift().

  • table (str) – Nable name.

  • con (sqlalchemy.engine.Engine) – SQLAlchemy Engine. Please use, wr.db.get_engine(), wr.db.get_redshift_temp_engine() or wr.catalog.get_engine()

  • schema (str, optional) – Name of SQL schema in database to query (if database flavor supports this). Uses default schema if None (default).

  • index_col (Union[str, List[str]], optional) – Column(s) to set as index(MultiIndex).

  • params (Union[List, Tuple, Dict], optional) – List of parameters to pass to execute method. The syntax used to pass parameters is database driver dependent. Check your database driver documentation for which of the five syntax styles, described in PEP 249’s paramstyle, is supported. Eg. for psycopg2, uses %(name)s so use params={‘name’ : ‘value’}.

  • chunksize (int, optional) – If specified, return an iterator where chunksize is the number of rows to include in each chunk.

  • dtype (Dict[str, pyarrow.DataType], optional) – Specifying the datatype for columns. The keys should be the column names and the values should be the PyArrow types.

  • safe (bool) – Check for overflows or other unsafe data type conversions.


Result as Pandas DataFrame(s).

Return type

Union[pandas.DataFrame, Iterator[pandas.DataFrame]]


Reading from Redshift with temporary credentials

>>> import awswrangler as wr
>>> df = wr.db.read_sql_table(
...     table="my_table",
...     schema="public",
...     con=wr.db.get_redshift_temp_engine(cluster_identifier="...", user="...")
... )

Reading from Redshift from Glue Catalog Connections

>>> import awswrangler as wr
>>> df = wr.db.read_sql_table(
...     table="my_table",
...     schema="public",
...     con=wr.catalog.get_engine(connection="...")
... )