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# pandas.read_sql_query

pandas.read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize=None, dtype=None, dtype_backend=<no_default>)

  Read SQL query into a DataFrame.

  Returns a DataFrame corresponding to the result set of the query string. Optionally provide an index_col parameter to use one of the columns as the index, otherwise default integer index will be used.

  Parameters:
    sql str SQL query or SQLAlchemy Selectable (select or text object)

      SQL query to be executed.

    con SQLAlchemy connectable, str, or sqlite3 connection

      Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported.

    index_col str or list of str, optional, default: None

      Column(s) to set as index(MultiIndex).

    coerce_float bool, default True

      Attempts to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point. Useful for SQL result sets. This can lose precision: an integral `decimal.Decimal` larger than `2**53` has no exact `float64` representation, so a long identifier can be silently rounded. Pass `False` to leave such values as Python objects in an `object`-dtype column.

    params list, tuple or mapping, 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 `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. By itself this typically does not reduce peak memory usage, as most drivers buffer the full result set unless a server-side cursor is used; see the user guide on streaming results.

    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’}.

    dtype_backend{‘numpy_nullable’, ‘pyarrow’}

      Back-end data type applied to the resultant `DataFrame` (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows:

      - `"numpy_nullable"`: returns nullable-dtype-backed `DataFrame`

      - `"pyarrow"`: returns pyarrow-backed nullable `ArrowDtype` `DataFrame`

      Added in version 2.0.

  Returns:
    DataFrame or Iterator[DataFrame]

      Returns a DataFrame object that contains the result set of the executed SQL query, in relation to the specified database connection.

  See also

  `read_sql_table`

    Read SQL database table into a DataFrame.

  `read_sql`

    Read SQL query or database table into a DataFrame.

  Notes

  Any datetime values with time zone information parsed via the parse_dates parameter will be converted to UTC.

  Examples

      >>> from sqlalchemy import create_engine
      >>> engine = create_engine("sqlite:///database.db")
      >>> sql_query = "SELECT int_column FROM test_data"
      >>> with engine.connect() as conn, conn.begin():
      ...     data = pd.read_sql_query(sql_query, conn)