+ NV-j%^RIHt^RIHt^RIHt^RIHt]!R4RRRRRR RR R R RR RRR/RRlll4tRRRRRRRRR/RRlllt R#))Any)import_optional_dependency) set_module) DataFramepandasNcatalog_propertiescolumns row_filtercase_sensitiveT snapshot_idlimitscan_propertiescLV^8dQhR\R\R,R\\\3,R,R\\,R,R\R,R\R\ R,R \ R,R \\\3,R,R \ / #) table_identifier catalog_nameNrr r r r r rreturn)strdictrlistboolintr)formats"b/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/pandas/io/iceberg.py __annotate__r sWWW*WS#X- W #Y  W d WWtW :W#s(^d*WWc (\R4p \R4p Vf/pV P!V3/VBp V PV4p VfV P4pVfRp M \ V4p Vf/pV P VV VVVVR7pVP 4#)a Read an Apache Iceberg table into a pandas DataFrame. .. versionadded:: 3.0.0 .. warning:: read_iceberg is experimental and may change without warning. Parameters ---------- table_identifier : str Table identifier. catalog_name : str, optional The name of the catalog. catalog_properties : dict of {str: str}, optional The properties that are used next to the catalog configuration. columns : list of str, optional A list of strings representing the column names to return in the output dataframe. row_filter : str, optional A string that describes the desired rows. case_sensitive : bool, default True If True column matching is case sensitive. snapshot_id : int, optional Snapshot ID to time travel to. By default the table will be scanned as of the current snapshot ID. limit : int, optional An integer representing the number of rows to return in the scan result. By default all matching rows will be fetched. scan_properties : dict of {str: obj}, optional Additional Table properties as a dictionary of string key value pairs to use for this scan. Returns ------- DataFrame DataFrame based on the Iceberg table. See Also -------- read_parquet : Read a Parquet file. Examples -------- >>> df = pd.read_iceberg( ... table_identifier="my_table", ... catalog_name="my_catalog", ... catalog_properties={"s3.secret-access-key": "my-secret"}, ... row_filter="trip_distance >= 10.0", ... columns=["VendorID", "tpep_pickup_datetime"], ... ) # doctest: +SKIP pyiceberg.catalogzpyiceberg.expressions)r selected_fieldsr r optionsr )*)r load_catalog load_table AlwaysTruetuplescan to_pandas)rrrr r r r r rpyiceberg_catalogpyiceberg_expressionscatalogtablerresults&&$$$$$$$ r read_icebergr- sD33FG67NO!,,\P=OPG   / 0E*557  . ZZ'% F    rlocationappendFsnapshot_propertiescV^8dQhR\R\R\R,R\\\3,R,R\R,R\R\\\3,R,R R/#) rdfrrNrr.r/r0r)rrrrr)rs"rrrfs5N5N5N5N*5N S#X- 5N Dj 5N 5Nc3h$.5N 5Nrc>\R4p\R4pVf/pVP!V3/VBp VPPV4p V P VV P VR7p Vf/pV'dV P WR7R#V PWR7R#)a Write a DataFrame to an Apache Iceberg table. .. versionadded:: 3.0.0 Parameters ---------- table_identifier : str Table identifier. catalog_name : str, optional The name of the catalog. catalog_properties : dict of {str: str}, optional The properties that are used next to the catalog configuration. location : str, optional Location for the table. append : bool, default False If ``True``, append data to the table, instead of replacing the content. snapshot_properties : dict of {str: str}, optional Custom properties to be added to the snapshot summary See Also -------- read_iceberg : Read an Apache Iceberg table. DataFrame.to_parquet : Write a DataFrame in Parquet format. pyarrowrN) identifierschemar.)r0)rr"Table from_pandascreate_table_if_not_existsr6r/ overwrite) r2rrrr.r/r0par(r* arrow_tabler+s &&&$$$$ r to_icebergr=fsF $I .B23FG!,,\P=OPG((&&r*K  . .#!! / E"   [ J  Mr)N) typingrpandas.compat._optionalrpandas.util._decoratorsrrrr-r=rrrBs?. HW15 W !% W " W W#WW.2WWt5N 15 5N  5N5N265N5Nr