DataFrame.to_sql(name: str, con, schema=None, if_exists: str = 'fail', index: bool = 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.

  • 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. If passing a sqlalchemy.engine.Connection which is already in a transaction, the transaction will not be committed. If passing a sqlite3.Connection, it will not be possible to roll back the record insertion.

  • 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.


Number of rows affected by to_sql. None is returned if the callable passed into method does not return an integer number of rows.

The number of returned rows affected is the sum of the rowcount attribute of sqlite3.Cursor or SQLAlchemy connectable which may not reflect the exact number of written rows as stipulated in the sqlite3 or SQLAlchemy.

New in version 1.4.0(pandas).

Return type

None or int


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

See also


Read a DataFrame from a table.


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.







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  
0  User 1
1  User 2
2  User 3
>>> df.to_sql('users', con=engine)  
>>> from sqlalchemy import text  
>>> with engine.connect() as conn:  
...    conn.execute(text("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')  
>>> with engine.connect() as conn:  
...    conn.execute(text("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')
>>> with engine.connect() as conn:  
...    conn.execute(text("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  
0  1.0
1  NaN
2  2.0
>>> from sqlalchemy.types import Integer  
>>> df.to_sql('integers', con=engine, index=False,  
...           dtype={"A": Integer()})
>>> with engine.connect() as conn:  
...   conn.execute(text("SELECT * FROM integers")).fetchall()
[(1,), (None,), (2,)]

This docstring was copied from pandas.core.frame.DataFrame.