pyspark.pandas.read_sql_table¶

pyspark.pandas.read_sql_table(table_name: str, con: str, schema: Optional[str] = None, index_col: Union[str, List[str], None] = None, columns: Union[str, List[str], None] = None, **options: Any) → pyspark.pandas.frame.DataFrame¶

Read SQL database table into a DataFrame.

Given a table name and a JDBC URI, returns a DataFrame.

Parameters

table_namestr: Name of SQL table in database.
constr: A JDBC URI could be provided as as str.

Note

The URI must be JDBC URI instead of Python’s database URI.
schemastr, default None: Name of SQL schema in database to query (if database flavor supports this). Uses default schema if None (default).
index_colstr or list of str, optional, default: None: Column(s) to set as index(MultiIndex).
columnslist, default None: List of column names to select from SQL table.
optionsdict: All other options passed directly into Spark’s JDBC data source.

Returns

DataFrame: A SQL table is returned as two-dimensional data structure with labeled axes.