ง q-Phใ๓Œ—ddlmZddlmZmZmZer ddlmZddlm Z dgZ eddœdd„ฆซZ edd„ฆซZ ddœdd„Z dS)้)ฺ annotations)ฺ TYPE_CHECKINGฺLiteralฺoverload)ฺ DataFrame)ฺ LazyFrameฺsqlF)ฺeagerฺqueryฺstrr ๚Literal[False]ฺreturnrc๓—dSฉNฉฉr r s ๚T/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/polars/sql/functions.pyr r s€ุDGภC๓๚ Literal[True]rc๓—dSrrrs rr r s€ุ;>ธ3rฺbool๚DataFrame | LazyFramec๓<—ddlm}| ||ฌฆซS)uผ Execute a SQL query against frames in the global namespace. .. versionadded:: 0.20.31 Parameters ---------- query SQL query to execute. eager Automatically collect the result and return a DataFrame instead of a LazyFrame. Notes ----- * The Polars SQL engine can operate against Polars DataFrame, LazyFrame, and Series objects, as well as Pandas DataFrame and Series, PyArrow Table and RecordBatch. * Additional control over registration and execution behaviour is available with the :class:`SQLContext` object. See Also -------- SQLContext Examples -------- >>> lf1 = pl.LazyFrame({"a": [1, 2, 3], "b": [6, 7, 8], "c": ["z", "y", "x"]}) >>> lf2 = pl.LazyFrame({"a": [3, 2, 1], "d": [125, -654, 888]}) Query the LazyFrame using SQL: >>> lf1.sql("SELECT c, b FROM self WHERE a > 1").collect() shape: (2, 2) โ”Œโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ” โ”‚ c โ”† b โ”‚ โ”‚ --- โ”† --- โ”‚ โ”‚ str โ”† i64 โ”‚ โ•žโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•ก โ”‚ y โ”† 7 โ”‚ โ”‚ x โ”† 8 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”˜ Join two LazyFrames: >>> pl.sql( ... ''' ... SELECT lf1.*, d ... FROM lf1 ... INNER JOIN lf2 USING (a) ... WHERE a > 1 AND b < 8 ... ''' ... ).collect() shape: (1, 4) โ”Œโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ a โ”† b โ”† c โ”† d โ”‚ โ”‚ --- โ”† --- โ”† --- โ”† --- โ”‚ โ”‚ i64 โ”† i64 โ”† str โ”† i64 โ”‚ โ•žโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•ก โ”‚ 2 โ”† 7 โ”† y โ”† -654 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”˜ Apply SQL transforms and subsequently filter natively (you can freely mix SQL and native operations): >>> pl.sql( ... query=''' ... SELECT ... a, ... (a % 2 == 0) AS a_is_even, ... (b::float4 / 2) AS "b/2", ... CONCAT_WS(':', c, c, c) AS c_c_c ... FROM lf1 ... ORDER BY a ... ''', ... ).filter(~pl.col("c_c_c").str.starts_with("x")).collect() shape: (2, 4) โ”Œโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ a โ”† a_is_even โ”† b/2 โ”† c_c_c โ”‚ โ”‚ --- โ”† --- โ”† --- โ”† --- โ”‚ โ”‚ i64 โ”† bool โ”† f32 โ”† str โ”‚ โ•žโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•ก โ”‚ 1 โ”† false โ”† 3.0 โ”† z:z:z โ”‚ โ”‚ 2 โ”† true โ”† 3.5 โ”† y:y:y โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ Join polars LazyFrame with a pandas DataFrame and a pyarrow Table: >>> import pandas as pd >>> import pyarrow as pa >>> pl_frame = lf1 >>> pd_frame = pd.DataFrame({"a": [2, 3, 4], "d": [-0.5, 0.0, 0.5]}) >>> pa_table = pa.Table.from_arrays( ... [pa.array([1, 2, 3]), pa.array(["x", "y", "z"])], ... names=["a", "e"], ... ) >>> pl.sql( ... query=''' ... SELECT pl_frame.*, d, e ... FROM pl_frame ... JOIN pd_frame USING(a) ... JOIN pa_table USING(a) ... ''', ... ).collect() shape: (2, 5) โ”Œโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ” โ”‚ a โ”† b โ”† c โ”† d โ”† e โ”‚ โ”‚ --- โ”† --- โ”† --- โ”† --- โ”† --- โ”‚ โ”‚ i64 โ”† i64 โ”† str โ”† f64 โ”† str โ”‚ โ•žโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•ก โ”‚ 2 โ”† 7 โ”† y โ”† -0.5 โ”† y โ”‚ โ”‚ 3 โ”† 8 โ”† x โ”† 0.0 โ”† z โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”˜ r)ฺ SQLContextr)ฺ polars.sqlrฺexecute_global)r r rs rr r s:€๐b&ะ%ะ%ะ%ะ%ะ%เ ื $า $ุุ๐ %๑ ๔ ๐rN)r r r r rr)r r r rrr)r r r rrr) ฺ __future__rฺtypingrrrฺpolars.dataframerฺpolars.lazyframerฺ__all__r rrr๚r"sุ๐ุ"ะ"ะ"ะ"ะ"ะ"เ3ะ3ะ3ะ3ะ3ะ3ะ3ะ3ะ3ะ3เ๐+ุ*ะ*ะ*ะ*ะ*ะ*ุ*ะ*ะ*ะ*ะ*ะ*๐ ˆ'€๐ ุ/4ะGะGะGะGะG๑ „ุG๐ ุ>ะ>ะ>๑ „ุ>๐&+๐v๐v๐v๐v๐v๐v๐v๐vr