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- UNPIVOT Statement 12612
- The UNPIVOT statement allows multiple columns to be stacked into fewer columns. In the basic case, multiple columns are stacked into two columns: a NAME column (which contains the name of the source column) and a VALUE column (which contain
...EGER); INSERT INTO monthly_sales VALUES (1, 'electronics', 1, 2, 3, 4, 5, 6), (2, 'clothes', 10, 20, 30, 40, 50, 60), (3, 'cars', 100, 200, 300, 400, 500, 600); FROM monthly_sales; empid dept Jan Feb Mar Apr May Jun 1 electronics 1 2 3 4 5 6 2 clothe...s 10 20 30 40 50 60 3 cars 100 200 300 400 500 600 UNPIVOT Manually The most typical UNPIVOT transformation is to take already pivoted data and re-st...electronics Jun 6 2 clothes Jan 10 2 clothes Feb 20 2 clothes Mar 30 2 clothes Apr 40 2 clothes May 50 2 clothes Jun 60 3 cars Jan 100 3 cars Feb 200 3 cars Mar 300 3 cars Apr 400 3 cars May 500 3 cars Jun 600 UNPIVOT Dynamically Using COLUMNS Expre... - https://man.mtool.work/duckdb/2.0/sql/statements/unpivot.html
- Pivot Internals 9615
- PIVOT Pivoting is implemented as a combination of SQL query re-writing and a dedicated PhysicalPivot operator for higher performance. Each PIVOT is implemented as set of aggregations into lists and then the dedicated PhysicalPivot operator
...electronics jun 6 2 clothes jan 10 2 clothes feb 20 2 clothes mar 30 2 clothes apr 40 2 clothes may 50 2 clothes jun 60 3 cars jan 100 3 cars feb 200 3 cars mar 300 3 cars apr 400 3 cars may 500 3 cars jun 600... - https://man.mtool.work/duckdb/2.0/internals/pivot.html
- Integration with Polars 8034
- Polars is a DataFrames library built in Rust with bindings for Python and Node.js. It uses Apache Arrow's columnar format as its memory model. DuckDB can read Polars DataFrames and convert query results to Polars DataFrames. It does this in
...= pl.DataFrame( { "A": [1, 2, 3, 4, 5], "fruits": ["banana", "banana", "apple", "apple", "banana"], "B": [5, 4, 3, 2, 1], "cars": ["beetle", "audi", "beetle", "beetle", "beetle"], } ) duckdb.sql("SELECT * FROM df").show() DuckDB to Polars DuckDB can... - https://man.mtool.work/duckdb/2.0/guides/python/polars.html
- MySQL Extension 7850
- The mysql extension allows DuckDB to directly read and write data from/to a running MySQL instance. The data can be queried directly from the underlying MySQL database. Data can be loaded from MySQL tables into DuckDB tables, or vice versa.
...ample: ATTACH 'host=localhost database=mysql' AS mysqldb (TYPE mysql); SELECT * FROM mysql_query('mysqldb', 'SELECT * FROM cars LIMIT 3'); The mysql_execute Function The mysql_execute function allows running arbitrary queries within MySQL, including... - https://man.mtool.work/duckdb/2.0/core_extensions/mysql.html
- PostgreSQL Extension 7813
- The postgres extension allows DuckDB to directly read and write data from a running PostgreSQL database instance. The data can be queried directly from the underlying PostgreSQL database. Data can be loaded from PostgreSQL tables into DuckD
...ATTACH 'dbname=postgresscanner' AS postgres_db (TYPE postgres); SELECT * FROM postgres_query('postgres_db', 'SELECT * FROM cars LIMIT 3'); brand model color Ferrari Testarossa red Aston Martin DB2 blue Bentley Mulsanne gray The postgres_execute Funct... - https://man.mtool.work/duckdb/2.0/core_extensions/postgres/overview.html
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