'hSSKJr SSKrSSKrSSKJr SSKJrJr SSK J r SSK J r \(aSSKJr SSKJr SSKJrJrJr SSKJr \ "5S S S .SS jj5r\ "5S S .SSjj5rg)) annotationsN)Iterator) TYPE_CHECKINGCallable)unstable)r) DataFrameExpr LazyFrame) SchemaDictF)validate_schemais_purecj^SU4Sjjn[RRUUSUUS9$)a  Register your IO plugin and initialize a LazyFrame. See the `user guide `_ for more information about plugins. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. Parameters ---------- io_source Function that accepts the following arguments: with_columns Columns that are projected. The reader must project these columns if applied predicate Polars expression. The reader must filter their rows accordingly. n_rows Materialize only n rows from the source. The reader can stop when `n_rows` are read. batch_size A hint of the ideal batch size the reader's generator must produce. The function should return a an iterator/generator that produces DataFrames. schema Schema or function that when called produces the schema that the reader will produce before projection pushdown. validate_schema Whether the engine should validate if the batches generated match the given schema. It's an implementation error if this isn't the case and can lead to bugs that are hard to solve. Returns ------- LazyFrame c >SnSnU(a [RRU5nT"XX#5U4$![aKn[R R S5(a[SU3[RS9 SnSnAN[SnAff=f)NTPOLARS_VERBOSEzMfailed parsing IO plugin expression filter will be handled on Polars' side: )fileF) plr deserialize Exceptionosenvirongetprintsysstderr) with_columns predicaten_rows batch_sizeparsed_predicate_successparsed_predicatee io_sources dC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\polars/io/plugins.pywrap register_io_source..wrapHs $(   1#%77#6#6y#A  F #$ $ 1::>>"233ijkilm ZZ,1(  1s8 B ABB F)schemascan_fnpyarrowr r ) rlist[str] | Nonerz bytes | Noner int | Nonerr*returnz tuple[Iterator[DataFrame], bool])rr _scan_python_function)r"r&r r r$s` r#register_io_sourcer-sdj$&$$$ $ * $. << - -' . T)r c<^SU4Sjjn[X1US9$)u Deferred execution. Takes a function that produces a `DataFrame` but defers execution until the `LazyFrame` is collected. Parameters ---------- function Function that takes no arguments and produces a `DataFrame`. schema Schema of the `DataFrame` the deferred function will return. The caller must ensure this schema is correct. validate_schema Whether the engine should validate if the batches generated match the given schema. It's an implementation error if this isn't the case and can lead to bugs that are hard to solve. Examples -------- Delay DataFrame execution until query is executed. >>> import numpy as np >>> np.random.seed(0) >>> lf = pl.defer( ... lambda: pl.DataFrame({"a": np.random.randn(3)}), schema={"a": pl.Float64} ... ) >>> lf.collect() shape: (3, 1) ┌──────────┐ │ a │ │ --- │ │ f64 │ ╞══════════╡ │ 1.764052 │ │ 0.400157 │ │ 0.978738 │ └──────────┘ Run an eager source in Polars Cloud >>> ( ... pl.defer( ... lambda: pl.read_database("select * from tbl"), ... schema={"a": pl.Float64, "b": pl.Boolean}, ... ) ... .filter("b") ... .sum("a") ... .remote() ... .collect() ... ) # doctest: +SKIP c3># T"5R5nUbURU5nUbURU5nUbURU5nUR 5v g7f)N)lazyselectfilterlimitcollect)rrrrlffunctions r#source_defer..sources^ Z__   #<(B  9%B  &!BjjlsA%A()r"r&r ) rr)rz Expr | Nonerr*rr*r+zIterator[DataFrame])r-)r7r&r r8s` r#_deferr:hsK| &        r.) r"zVCallable[[list[str] | None, Expr | None, int | None, int | None], Iterator[DataFrame]]r&z%Callable[[], SchemaDict] | SchemaDictr boolr r;r+r )r7zCallable[[], DataFrame]r&z%SchemaDict | Callable[[], SchemaDict]r r;r+r ) __future__rrrcollections.abcrtypingrrpolars._reexport _reexportrpolars._utils.unstablerpolarsrr r polars._typingr r-r:r.r#rEs" $*+(11) "QQ 2 Q  QQQ Qh  ! N%N 2N N  N Nr.