awswrangler.db.read_sql_query

awswrangler.db.read_sql_query(sql: str, con: sqlalchemy.engine.base.Engine, 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.

Note

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

Parameters
  • sql (str) – SQL query.

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

  • 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.

Returns

Result as Pandas DataFrame(s).

Return type

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

Examples

Reading from Redshift with temporary credentials

>>> import awswrangler as wr
>>> df = wr.db.read_sql_query(
...     sql="SELECT * FROM public.my_table",
...     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_query(
...     sql="SELECT * FROM public.my_table",
...     con=wr.catalog.get_engine(connection="...")
... )