# pandas.Series.to_sql

Series.to_sql(name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)

  Write records stored in a DataFrame to a SQL database.

  Databases supported by SQLAlchemy [1] are supported. Tables can be newly created, appended to, or overwritten.

  Parameters
    name str

      Name of SQL table.

    con sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection

      Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See here.

    schema str, optional

      Specify the schema (if database flavor supports this). If None, use default schema.

    if_exists{‘fail’, ‘replace’, ‘append’}, default ‘fail’

      How to behave if the table already exists.

      - fail: Raise a ValueError.

      - replace: Drop the table before inserting new values.

      - append: Insert new values to the existing table.

    index bool, default True

      Write DataFrame index as a column. Uses index_label as the column name in the table.

    index_label str or sequence, default None

      Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.

    chunksize int, optional

      Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once.

    dtype dict or scalar, optional

      Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.

    method{None, ‘multi’, callable}, optional

      Controls the SQL insertion clause used:

      - None : Uses standard SQL `INSERT` clause (one per row).

      - ‘multi’: Pass multiple values in a single `INSERT` clause.

      - callable with signature `(pd_table, conn, keys, data_iter)`.

      Details and a sample callable implementation can be found in the section insert method.

  Raises
    ValueError

      When the table already exists and if_exists is ‘fail’ (the default).

  See also

  `read_sql`

    Read a DataFrame from a table.

  Notes

  Timezone aware datetime columns will be written as `Timestamp with timezone` type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

  References

  1

    https://docs.sqlalchemy.org

  2

    https://www.python.org/dev/peps/pep-0249/

  Examples

  Create an in-memory SQLite database.

      >>> from sqlalchemy import create_engine
      >>> engine = create_engine('sqlite://', echo=False)

  Create a table from scratch with 3 rows.

      >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
      >>> df
           name
      0  User 1
      1  User 2
      2  User 3

      >>> df.to_sql('users', con=engine)
      >>> engine.execute("SELECT * FROM users").fetchall()
      [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

  An sqlalchemy.engine.Connection can also be passed to con:

      >>> with engine.begin() as connection:
      ...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
      ...     df1.to_sql('users', con=connection, if_exists='append')

  This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

      >>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
      >>> df2.to_sql('users', con=engine, if_exists='append')
      >>> engine.execute("SELECT * FROM users").fetchall()
      [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
       (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
       (1, 'User 7')]

  Overwrite the table with just `df2`.

      >>> df2.to_sql('users', con=engine, if_exists='replace',
      ...            index_label='id')
      >>> engine.execute("SELECT * FROM users").fetchall()
      [(0, 'User 6'), (1, 'User 7')]

  Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

      >>> df = pd.DataFrame({"A": [1, None, 2]})
      >>> df
           A
      0  1.0
      1  NaN
      2  2.0

      >>> from sqlalchemy.types import Integer
      >>> df.to_sql('integers', con=engine, index=False,
      ...           dtype={"A": Integer()})

      >>> engine.execute("SELECT * FROM integers").fetchall()
      [(1,), (None,), (2,)]