awswrangler.sqlserver.to_sql¶
- awswrangler.sqlserver.to_sql(df: DataFrame, con: pyodbc.Connection, table: str, schema: str, mode: str = 'append', index: bool = False, dtype: Optional[Dict[str, str]] = None, varchar_lengths: Optional[Dict[str, int]] = None, use_column_names: bool = False, chunksize: int = 200) Any ¶
Write records stored in a DataFrame into Microsoft SQL Server.
Note
This function has arguments which can be configured globally through wr.config or environment variables:
chunksize
Check out the Global Configurations Tutorial for details.
- Parameters
df (pandas.DataFrame) – Pandas DataFrame https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html
con (pyodbc.Connection) – Use pyodbc.connect() to use credentials directly or wr.sqlserver.connect() to fetch it from the Glue Catalog.
table (str) – Table name
schema (str) – Schema name
mode (str) – Append or overwrite.
index (bool) – True to store the DataFrame index as a column in the table, otherwise False to ignore it.
dtype (Dict[str, str], optional) – Dictionary of columns names and Microsoft SQL Server types to be casted. Useful when you have columns with undetermined or mixed data types. (e.g. {‘col name’: ‘TEXT’, ‘col2 name’: ‘FLOAT’})
varchar_lengths (Dict[str, int], optional) – Dict of VARCHAR length by columns. (e.g. {“col1”: 10, “col5”: 200}).
use_column_names (bool) – If set to True, will use the column names of the DataFrame for generating the INSERT SQL Query. E.g. If the DataFrame has two columns col1 and col3 and use_column_names is True, data will only be inserted into the database columns col1 and col3.
chunksize (int) – Number of rows which are inserted with each SQL query. Defaults to inserting 200 rows per query.
- Returns
None.
- Return type
None
Examples
Writing to Microsoft SQL Server using a Glue Catalog Connections
>>> import awswrangler as wr >>> con = wr.sqlserver.connect(connection="MY_GLUE_CONNECTION", odbc_driver_version=17) >>> wr.sqlserver.to_sql( ... df=df, ... table="table", ... schema="dbo", ... con=con ... ) >>> con.close()