polars.SQLContext.execute#
- SQLContext.execute( ) LazyFrame | DataFrame [source]#
Parse the given SQL query and execute it against the registered frame data.
- Parameters:
- query
A valid string SQL query.
- eager
Apply the query eagerly, returning
DataFrame
instead ofLazyFrame
. If unset, the value of the init-time “eager” parameter will be used. Note that the query itself is always executed in lazy-mode; this parameter only impacts the type of the returned frame.
Examples
Declare frame data and register with a SQLContext:
>>> df = pl.DataFrame( ... data=[ ... ("The Godfather", 1972, 6_000_000, 134_821_952, 9.2), ... ("The Dark Knight", 2008, 185_000_000, 533_316_061, 9.0), ... ("Schindler's List", 1993, 22_000_000, 96_067_179, 8.9), ... ("Pulp Fiction", 1994, 8_000_000, 107_930_000, 8.9), ... ("The Shawshank Redemption", 1994, 25_000_000, 28_341_469, 9.3), ... ], ... schema=["title", "release_year", "budget", "gross", "imdb_score"], ... orient="row", ... ) >>> ctx = pl.SQLContext(films=df)
Execute a SQL query against the registered frame data:
>>> ctx.execute( ... ''' ... SELECT title, release_year, imdb_score ... FROM films ... WHERE release_year > 1990 ... ORDER BY imdb_score DESC ... ''', ... eager=True, ... ) shape: (4, 3) ┌──────────────────────────┬──────────────┬────────────┐ │ title ┆ release_year ┆ imdb_score │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ f64 │ ╞══════════════════════════╪══════════════╪════════════╡ │ The Shawshank Redemption ┆ 1994 ┆ 9.3 │ │ The Dark Knight ┆ 2008 ┆ 9.0 │ │ Schindler's List ┆ 1993 ┆ 8.9 │ │ Pulp Fiction ┆ 1994 ┆ 8.9 │ └──────────────────────────┴──────────────┴────────────┘
Execute a GROUP BY query:
>>> ctx.execute( ... ''' ... SELECT ... MAX(release_year / 10) * 10 AS decade, ... SUM(gross) AS total_gross, ... COUNT(title) AS n_films, ... FROM films ... GROUP BY (release_year / 10) -- decade ... ORDER BY total_gross DESC ... ''', ... eager=True, ... ) shape: (3, 3) ┌────────┬─────────────┬─────────┐ │ decade ┆ total_gross ┆ n_films │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ u32 │ ╞════════╪═════════════╪═════════╡ │ 2000 ┆ 533316061 ┆ 1 │ │ 1990 ┆ 232338648 ┆ 3 │ │ 1970 ┆ 134821952 ┆ 1 │ └────────┴─────────────┴─────────┘