h SSKJr SSKJr SSKJr SSKJr SSK J r \(aSSK J r Jr SSS.S S jjrS S jrg) ) annotations)partial) TYPE_CHECKINGNpyarrow) DataFrame LazyFrameT)allow_pyarrow_filter batch_sizecn[[XS9n[RR UR X1S9$)a Pickle the partially applied function `_scan_pyarrow_dataset_impl`. The bytes are then sent to the polars logical plan. It can be deserialized once executed and ran. Parameters ---------- ds pyarrow dataset allow_pyarrow_filter Allow predicates to be pushed down to pyarrow. This can lead to different results if comparisons are done with null values as pyarrow handles this different than polars does. batch_size The maximum row count for scanned pyarrow record batches. )r r)r_scan_pyarrow_dataset_implplr _scan_python_functionschema)dsr r funcs b/home/james-whalen/.local/lib/python3.13/site-packages/polars/io/pyarrow_dataset/anonymous_scan.py_scan_pyarrow_datasetr s6. -r ID << - - 4 . c SSKJn SnU(a/SSKJnJnJn Jn SSKJn J n J n [U[U U U UUU U S.5nXS.nUbXNS'U(aU"UR"U40UD65$U"UR"S 0UD65$) a0 Take the projected columns and materialize an arrow table. Parameters ---------- ds pyarrow dataset with_columns Columns that are projected predicate pyarrow expression that can be evaluated with eval n_rows: Materialize only n rows from the arrow dataset batch_size The maximum row count for scanned pyarrow record batches. Warnings -------- Don't use this if you accept untrusted user inputs. Predicates will be evaluated with python 'eval'. There is sanitation in place, but it is a possible attack vector. Returns ------- DataFrame r) from_arrowN) to_py_dateto_py_datetime to_py_timeto_py_timedelta)DateDatetimeDuration)parrrrrrr)columnsfilterr )polarsrpolars._utils.convertrrrrpolars.datatypesrrrevalrheadto_table)r with_columns predicaten_rowsr r_filterrrrrrrr common_paramss rr r *sB"G  >= $$("0(#2   !-@M&0l# "''&:M:;; bkk2M2 33r)rpa.dataset.Datasetr boolr int | Nonereturnr ) rr.r)zlist[str] | Noner*z str | Noner+r0r r0r1r) __future__r functoolsrtypingrpolars._reexport _reexportrpolars._dependenciesrrr#rr rr r"rrr8s" .+ "&!    :C4C4"C4C4  C4  C4  C4r