DataFrame.
to_sql
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 of SQL table.
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.
Specify the schema (if database flavor supports this). If None, use default schema.
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.
Write DataFrame index as a column. Uses index_label as the column name in the table.
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.
Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once.
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.
Controls the SQL insertion clause used:
None : Uses standard SQL INSERT clause (one per row).
INSERT
‘multi’: Pass multiple values in a single INSERT clause.
callable with signature (pd_table, conn, keys, data_iter).
(pd_table, conn, keys, data_iter)
Details and a sample callable implementation can be found in the section insert method.
New in version 0.24.0.
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.
Timestamp with timezone
References
https://docs.sqlalchemy.org
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 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
>>> 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,)]