ujHddlmZddlmZddlmZddlmZddlm Z m Z ed ddddddddd d e d e dzd e e efdzd e e dzde dzdededzdedzde e efdzdefdZ ddddddded e d e dzd e e efdzde dzdede e e fdzddfdZy))Any)import_optional_dependency) set_module) DataFrame)arrow_table_to_pandassuppress_pyarrow_values_warningpandasNT)catalog_propertiescolumns row_filtercase_sensitive snapshot_idlimitscan_propertiestable_identifier catalog_namer r r r rrrreturnc*td} td} |i}| j|fi|} | j|} || j}|d} n t |} |i}| j || ||||}t |jS)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 roptionsr)r load_catalog load_table AlwaysTruetuplescanrto_arrow)rrr r r r rrrpyiceberg_catalogpyiceberg_expressionscatalogtablerresults \/var/www/html/python-recuperauto-other/env/lib/python3.12/site-packages/pandas/io/iceberg.py read_icebergr%sD33FG67NO!,,,\P=OPG   / 0E*557  . ZZ'% F !!2 33F)r locationappendsnapshot_propertiesdfr'r(r)chtd}td}|i}|j|fi|} t5|jj |} ddd| j | j |} |i}|r| j| |y| j| |y#1swYUxYw)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')r)) rrrTable from_pandascreate_table_if_not_existsr.r( overwrite) r*rrr r'r(r)parr! arrow_tabler"s r$ to_icebergr5ksF $I .B23FG!,,,\P=OPG ( */hh**2. /  . .#!! / E"   [6I J  9LM//s B((B1)N)typingrpandas.compat._optionalrpandas.util._decoratorsrr rpandas.io._utilrrstrdictlistboolintr%r5r&r$r@s?.  H $W415 $!"-1W4W4*W4S#X- W4 #Y  W4 d W4W4tW4 :W4#s(^d*W4W4W4z $6N 15156N6N6N*6N S#X- 6N Dj 6N 6Nc3h$.6N 6Nr&