awswrangler/postgresql.py [167:231]:
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    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, pa.DataType]] = None,
    safe: bool = True,
    timestamp_as_object: bool = False,
) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
    """Return a DataFrame corresponding to the result set of the query string.

    Parameters
    ----------
    sql : str
        SQL query.
    con : pg8000.Connection
        Use pg8000.connect() to use credentials directly or wr.postgresql.connect() to fetch it from the Glue Catalog.
    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.
    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.
    timestamp_as_object : bool
        Cast non-nanosecond timestamps (np.datetime64) to objects.

    Returns
    -------
    Union[pandas.DataFrame, Iterator[pandas.DataFrame]]
        Result as Pandas DataFrame(s).

    Examples
    --------
    Reading from PostgreSQL using a Glue Catalog Connections

    >>> import awswrangler as wr
    >>> con = wr.postgresql.connect("MY_GLUE_CONNECTION")
    >>> df = wr.postgresql.read_sql_query(
    ...     sql="SELECT * FROM public.my_table",
    ...     con=con
    ... )
    >>> con.close()

    """
    _validate_connection(con=con)
    return _db_utils.read_sql_query(
        sql=sql,
        con=con,
        index_col=index_col,
        params=params,
        chunksize=chunksize,
        dtype=dtype,
        safe=safe,
        timestamp_as_object=timestamp_as_object,
    )


def read_sql_table(
    table: str,
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awswrangler/redshift.py [572:641]:
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    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, pa.DataType]] = None,
    safe: bool = True,
    timestamp_as_object: bool = False,
) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
    """Return a DataFrame corresponding to the result set of the query string.

    Note
    ----
    For large extractions (1K+ rows) consider the function **wr.redshift.unload()**.

    Parameters
    ----------
    sql : str
        SQL query.
    con : redshift_connector.Connection
        Use redshift_connector.connect() to use "
        "credentials directly or wr.redshift.connect() to fetch it from the Glue Catalog.
    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.
    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.
    timestamp_as_object : bool
        Cast non-nanosecond timestamps (np.datetime64) to objects.

    Returns
    -------
    Union[pandas.DataFrame, Iterator[pandas.DataFrame]]
        Result as Pandas DataFrame(s).

    Examples
    --------
    Reading from Redshift using a Glue Catalog Connections

    >>> import awswrangler as wr
    >>> con = wr.redshift.connect("MY_GLUE_CONNECTION")
    >>> df = wr.redshift.read_sql_query(
    ...     sql="SELECT * FROM public.my_table",
    ...     con=con
    ... )
    >>> con.close()

    """
    _validate_connection(con=con)
    return _db_utils.read_sql_query(
        sql=sql,
        con=con,
        index_col=index_col,
        params=params,
        chunksize=chunksize,
        dtype=dtype,
        safe=safe,
        timestamp_as_object=timestamp_as_object,
    )


def read_sql_table(
    table: str,
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