DataFrame.to_sql(name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)[source]

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [R16] are supported. Tables can be newly created, appended to, or overwritten.


name : string

Name of SQL table.

con : sqlalchemy.engine.Engine or sqlite3.Connection

Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects.

schema : string, 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 : boolean, default True

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

index_label : string 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

Rows will be written in batches of this size at a time. By default, all rows will be written at once.

dtype : dict, optional

Specifying the datatype for columns. The keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode.



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

See also

read a DataFrame from a table


[R16](1, 2)


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)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
>>> df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
>>> df1.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')]

Overwrite the table with just df1.

>>> df1.to_sql('users', con=engine, if_exists='replace',
...            index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 4'), (1, 'User 5')]

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