parameters: (
          self,
          sql: str,
          index_col: str | None = None,
          coerce_float: bool = True,
          parse_dates=None,
          params=None,
          chunksize: int | None = None,
          dtype: DtypeArg | None = None,
      )
docstring: Read SQL query into a DataFrame.
  
          Parameters
          ----------
          sql : str
              SQL query to be executed.
          index_col : string, optional, default: None
              Column name to use as index for the returned DataFrame object.
          coerce_float : bool, default True
              Attempt to convert values of non-string, non-numeric objects (like
              decimal.Decimal) to floating point, useful for SQL result sets.
          params : list, tuple or dict, optional, default: None
              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'}
          parse_dates : list or dict, default: None
              - List of column names to parse as dates.
              - Dict of ``{column_name: format string}`` where format string is
                strftime compatible in case of parsing string times, or is one of
                (D, s, ns, ms, us) in case of parsing integer timestamps.
              - Dict of ``{column_name: arg dict}``, where the arg dict
                corresponds to the keyword arguments of
                :func:`pandas.to_datetime` Especially useful with databases
                without native Datetime support, such as SQLite.
          chunksize : int, default None
              If specified, return an iterator where `chunksize` is the number
              of rows to include in each chunk.
          dtype : Type name or dict of columns
              Data type for data or columns. E.g. np.float64 or
              {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’}
  
              .. versionadded:: 1.3.0
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          read_sql_table : Read SQL database table into a DataFrame.
          read_sql
docstring_summary: Read SQL query into a DataFrame.
docstring_tokens: Read SQL query into a DataFrame .
function: def read_query(
          self,
          sql: str,
          index_col: str | None = None,
          coerce_float: bool = True,
          parse_dates=None,
          params=None,
          chunksize: int | None = None,
          dtype: DtypeArg | None = None,
      ):
          """
          Read SQL query into a DataFrame.
  
          Parameters
          ----------
          sql : str
              SQL query to be executed.
          index_col : string, optional, default: None
              Column name to use as index for the returned DataFrame object.
          coerce_float : bool, default True
              Attempt to convert values of non-string, non-numeric objects (like
              decimal.Decimal) to floating point, useful for SQL result sets.
          params : list, tuple or dict, optional, default: None
              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'}
          parse_dates : list or dict, default: None
              - List of column names to parse as dates.
              - Dict of ``{column_name: format string}`` where format string is
                strftime compatible in case of parsing string times, or is one of
                (D, s, ns, ms, us) in case of parsing integer timestamps.
              - Dict of ``{column_name: arg dict}``, where the arg dict
                corresponds to the keyword arguments of
                :func:`pandas.to_datetime` Especially useful with databases
                without native Datetime support, such as SQLite.
          chunksize : int, default None
              If specified, return an iterator where `chunksize` is the number
              of rows to include in each chunk.
          dtype : Type name or dict of columns
              Data type for data or columns. E.g. np.float64 or
              {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’}
  
              .. versionadded:: 1.3.0
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          read_sql_table : Read SQL database table into a DataFrame.
          read_sql
  
          """
          args = _convert_params(sql, params)
  
          result = self.execute(*args)
          columns = result.keys()
  
          if chunksize is not None:
              return self._query_iterator(
                  result,
                  chunksize,
                  columns,
                  index_col=index_col,
                  coerce_float=coerce_float,
                  parse_dates=parse_dates,
                  dtype=dtype,
              )
          else:
              data = result.fetchall()
              frame = _wrap_result(
                  data,
                  columns,
                  index_col=index_col,
                  coerce_float=coerce_float,
                  parse_dates=parse_dates,
                  dtype=dtype,
              )
              return frame
function_tokens: def read_query ( self , sql : str , index_col : str | None = None , coerce_float : bool = True , parse_dates = None , params = None , chunksize : int | None = None , dtype : DtypeArg | None = None , ) : args = _convert_params ( sql , params ) result = self . execute ( * args ) columns = result . keys ( ) if chunksize is not None : return self . _query_iterator ( result , chunksize , columns , index_col = index_col , coerce_float = coerce_float , parse_dates = parse_dates , dtype = dtype , ) else : data = result . fetchall ( ) frame = _wrap_result ( data , columns , index_col = index_col , coerce_float = coerce_float , parse_dates = parse_dates , dtype = dtype , ) return frame