z-Ph\ddlmZddlmZddlmZddlZddlZddlZddl Z ddl Z ddl Z ddl mZn&#e$rZedeedddZ[wwxYwddl mZmZmZmZmZmZmZmZmZmZmZddlmZmZm Z m!Z!m"Z"dd l#m$Z$m%Z%m&Z&m'Z'd Z(de_Ad=d,ZBed4ZCd5ZDd6?e4eDeC_A d?d7ZEd@d8ZF dAd9ZG dAd:ZHd;ZIdS)B) defaultdict) nullcontext)reduceNzPThe pyarrow installation is not built with support for the Parquet file format ()) ParquetReader Statistics FileMetaDataRowGroupMetaDataColumnChunkMetaData ParquetSchema ColumnSchemaParquetLogicalTypeFileEncryptionPropertiesFileDecryptionProperties SortingColumn)LocalFileSystem FileSystemFileType_resolve_filesystem_and_path_ensure_filesystem)guid _is_path_like_stringify_path_deprecate_apict|tr6|D]2}t|trtd}nd}||krdS3nt|trd|vSdS)NrTF) isinstancebyteschrstr)valbyte compare_tos T/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/pyarrow/parquet/core.py_check_contains_nullr%4s#u   D$&&  VV  z!!tt"   C  } 5Tc|t|dkstd|Drtdt|ddtr|g}|rZ|D]W}|D]R\}}}t|t rt d|Dst|rtdSX|S)z+ Check if filters are well-formed. Nrc3<K|]}t|dkVdS)rN)len).0fs r$ z!_check_filters..Gs,#A#AACFFaK#A#A#A#A#A#Ar&zMalformed filtersc34K|]}t|VdSN)r%)r*vs r$r,z!_check_filters..Ss+AA033AAAAAAr&zBNull-terminated binary strings are not supported as filter values.) r)any ValueErrorrr listallr%NotImplementedError)filterscheck_null_strings conjunctioncolopr!s r$_check_filtersr:Bs w<<1  #A#A#A#A#A A A 011 1 gajmS ) ) iG  &   $/  LCS"3--AASAAAAA-S11 20   Nr&aPredicates are expressed using an ``Expression`` or using the disjunctive normal form (DNF), like ``[[('x', '=', 0), ...], ...]``. DNF allows arbitrary boolean logical combinations of single column predicates. The innermost tuples each describe a single column predicate. The list of inner predicates is interpreted as a conjunction (AND), forming a more selective and multiple column predicate. Finally, the most outer list combines these filters as a disjunction (OR). Predicates may also be passed as List[Tuple]. This form is interpreted as a single conjunction. To express OR in predicates, one must use the (preferred) List[List[Tuple]] notation. Each tuple has format: (``key``, ``op``, ``value``) and compares the ``key`` with the ``value``. The supported ``op`` are: ``=`` or ``==``, ``!=``, ``<``, ``>``, ``<=``, ``>=``, ``in`` and ``not in``. If the ``op`` is ``in`` or ``not in``, the ``value`` must be a collection such as a ``list``, a ``set`` or a ``tuple``. Examples: Using the ``Expression`` API: .. code-block:: python import pyarrow.compute as pc pc.field('x') = 0 pc.field('y').isin(['a', 'b', 'c']) ~pc.field('y').isin({'a', 'b'}) Using the DNF format: .. code-block:: python ('x', '=', 0) ('y', 'in', ['a', 'b', 'c']) ('z', 'not in', {'a','b'}) c$ddlmt|jr|St |d}fdg}|D]=}fd|D}|t tj|>t tj |S)a Check if filters are well-formed and convert to an ``Expression``. Parameters ---------- filters : List[Tuple] or List[List[Tuple]] Notes ----- See internal ``pyarrow._DNF_filter_doc`` attribute for more details. Examples -------- >>> filters_to_expression([('foo', '==', 'bar')]) Returns ------- pyarrow.compute.Expression An Expression representing the filters rNF)r6c|}|dks|dkr||kS|dkr||kS|dkr||kS|dkr||kS|dkr||kS|dkr||kS|dkr||S|d kr||Std |||f) N=z==z!=<>z<=z>=inznot inz,"{0}" is not a valid operator in predicates.)fieldisinr1format)r8r9r!rAdss r$convert_single_predicatez7filters_to_expression..convert_single_predicates  99d C<  4ZZC<  3YY3;  3YY3;  4ZZC<  4ZZC<  4ZZ::c?? " 8^^JJsOO# #>EE"cN$$%% %r&c2g|]\}}}|||SrG)r*r8r9r!rEs r$ z)filters_to_expression..s>   R % $S"c 2 2   r&) pyarrow.datasetdatasetr Expressionr:appendroperatorand_or_)r5disjunction_membersr7conjunction_membersrErDs @@r$filters_to_expressionrRs.!     '2=))W???G%%%%%0OO     +    ""6(-9L#M#MNNNN (, 3 4 44r&_filters_to_expressionrRz10.0.0c eZdZdZddddddddddddd dZdZdZd Zed Z ed Z ed Z ed Z dde fdZede fdZ ddZ ddZ ddZddZddZddZdS) ParquetFilea Reader interface for a single Parquet file. Parameters ---------- source : str, pathlib.Path, pyarrow.NativeFile, or file-like object Readable source. For passing bytes or buffer-like file containing a Parquet file, use pyarrow.BufferReader. metadata : FileMetaData, default None Use existing metadata object, rather than reading from file. common_metadata : FileMetaData, default None Will be used in reads for pandas schema metadata if not found in the main file's metadata, no other uses at the moment. read_dictionary : list List of column names to read directly as DictionaryArray. memory_map : bool, default False If the source is a file path, use a memory map to read file, which can improve performance in some environments. buffer_size : int, default 0 If positive, perform read buffering when deserializing individual column chunks. Otherwise IO calls are unbuffered. pre_buffer : bool, default False Coalesce and issue file reads in parallel to improve performance on high-latency filesystems (e.g. S3). If True, Arrow will use a background I/O thread pool. coerce_int96_timestamp_unit : str, default None Cast timestamps that are stored in INT96 format to a particular resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 timestamps will be inferred as timestamps in nanoseconds. decryption_properties : FileDecryptionProperties, default None File decryption properties for Parquet Modular Encryption. thrift_string_size_limit : int, default None If not None, override the maximum total string size allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. thrift_container_size_limit : int, default None If not None, override the maximum total size of containers allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. page_checksum_verification : bool, default False If True, verify the checksum for each page read from the file. Examples -------- Generate an example PyArrow Table and write it to Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') Create a ``ParquetFile`` object from the Parquet file: >>> parquet_file = pq.ParquetFile('example.parquet') Read the data: >>> parquet_file.read() pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[2,2,4,4,5,100]] animal: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]] Create a ParquetFile object with "animal" column as DictionaryArray: >>> parquet_file = pq.ParquetFile('example.parquet', ... read_dictionary=["animal"]) >>> parquet_file.read() pyarrow.Table n_legs: int64 animal: dictionary ---- n_legs: [[2,2,4,4,5,100]] animal: [ -- dictionary: ["Flamingo","Parrot",...,"Brittle stars","Centipede"] -- indices: [0,1,2,3,4,5]] NFr) metadatacommon_metadataread_dictionary memory_map buffer_size pre_buffercoerce_int96_timestamp_unitdecryption_propertiesthrift_string_size_limitthrift_container_size_limit filesystempage_checksum_verificationc Ht|dd|_t|| |\} }| | |}d|_t |_|j|||||||| | | |  ||_||_ dS)NclosedT)rY) use_memory_maprZr[rXrVr\r]r^r_ra) getattr _close_sourceropen_input_filerreaderopenrW_build_nested_paths_nested_paths_by_prefix)selfsourcerVrWrXrYrZr[r\r]r^r_r`ras r$__init__zParquetFile.__init__,s%VXt<<9 J:777 F  !//77F!%D #oo   :# +h(C"7%=(C'A   /'+'?'?'A'A$$$r&c|Sr.rGrls r$ __enter__zParquetFile.__enter__I r&c.|dSr.closerlargskwargss r$__exit__zParquetFile.__exit__Ls r&c"|jj}tt}t |D]^\}}|d}|dd} ||||sn(d||df}|dd}F_|S)NrT.)rh column_pathsrr2 enumeraterLjoin)rlpathsresultipathkeyrests r$rjzParquetFile._build_nested_pathsOs (T"" '' GAtq'C8D s ""1%%%hhT!W~..ABBx   r&c|jjS)z. Return the Parquet metadata. )rhrVrps r$rVzParquetFile.metadatabs {##r&c|jjS)zG Return the Parquet schema, unconverted to Arrow types )rVschemarps r$rzParquetFile.schemais }##r&c|jjS)a Return the inferred Arrow schema, converted from the whole Parquet file's schema Examples -------- Generate an example Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') Read the Arrow schema: >>> parquet_file.schema_arrow n_legs: int64 animal: string )rh schema_arrowrps r$rzParquetFile.schema_arrowps0{''r&c|jjS)a. Return the number of row groups of the Parquet file. Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.num_row_groups 1 )rhnum_row_groupsrps r$rzParquetFile.num_row_groupss${))r&forcecN|js|r|jdSdSr.)rfrhru)rlrs r$ruzParquetFile.closes8    K         r&returnc|jjSr.)rhrcrps r$rczParquetFile.closeds {!!r&Tcj|||}|j|||S)a Read a single row group from a Parquet file. Parameters ---------- i : int Index of the individual row group that we want to read. columns : list If not None, only these columns will be read from the row group. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.table.Table Content of the row group as a table (of columns) Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.read_row_group(0) pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[2,2,4,4,5,100]] animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]] use_pandas_metadatacolumn_indices use_threads)_get_column_indicesrhread_row_group)rlrcolumnsrrrs r$rzParquetFile.read_row_groupsNT11 )<2>>{))!N6A*CC Cr&cj|||}|j|||S)a Read a multiple row groups from a Parquet file. Parameters ---------- row_groups : list Only these row groups will be read from the file. columns : list If not None, only these columns will be read from the row group. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.table.Table Content of the row groups as a table (of columns). Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.read_row_groups([0,0]) pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[2,2,4,4,5,...,2,4,4,5,100]] animal: [["Flamingo","Parrot","Dog",...,"Brittle stars","Centipede"]] rr)rrhread_row_groups)rl row_groupsrrrrs r$rzParquetFile.read_row_groupssOT11 )<2>>{**::H7B+DD Dr&c|td|jj}|||}|j||||}|S)a Read streaming batches from a Parquet file. Parameters ---------- batch_size : int, default 64K Maximum number of records to yield per batch. Batches may be smaller if there aren't enough rows in the file. row_groups : list Only these row groups will be read from the file. columns : list If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : boolean, default True Perform multi-threaded column reads. use_pandas_metadata : boolean, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Yields ------ pyarrow.RecordBatch Contents of each batch as a record batch Examples -------- Generate an example Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> for i in parquet_file.iter_batches(): ... print("RecordBatch") ... print(i.to_pandas()) ... RecordBatch n_legs animal 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Nrr)rrr)rangerVrrrh iter_batches)rl batch_sizerrrrrbatchess r$rzParquetFile.iter_batchessqf  q$-">??J11 )<2>>+**:6@:H7B+DDr&ch|||}|j||S)aW Read a Table from Parquet format. Parameters ---------- columns : list If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.table.Table Content of the file as a table (of columns). Examples -------- Generate an example Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') Read a Table: >>> parquet_file.read(columns=["animal"]) pyarrow.Table animal: string ---- animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]] rr)rrhread_all)rlrrrrs r$readzParquetFile.readCsIR11 )<2>>{##>0;$== =r&cd||}|j||S)a  Read contents of file for the given columns and batch size. Notes ----- This function's primary purpose is benchmarking. The scan is executed on a single thread. Parameters ---------- columns : list of integers, default None Select columns to read, if None scan all columns. batch_size : int, default 64K Number of rows to read at a time internally. Returns ------- num_rows : int Number of rows in file Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.scan_contents() 6 )r)rrh scan_contents)rlrrrs r$rzParquetFile.scan_contentsqs?D11'::{((4>)@@ @r&c<|dSg}|D]+}|jvr |j|,|rdjj}j jjnd}|rd|vrt |}n|rd|vrt |}ng}||r|fd|Dz }|S)Npandascng|]1}t|tj|2SrG)rdictrhcolumn_name_idx)r*descrrls r$rHz3ParquetFile._get_column_indices..sH<<< %#-eT#:#:><<d}tj||j|j|j}||\||rtj||j}||fS|dfS)NsparkFT)rV) rrparAtypenullablerVrLr) rflavorsanitized_fieldsschema_changedrArsanitized_namesanitized_field new_schemas r$_sanitize_schemars& / /E:D8>>N%%!%"$(>5:+0>5>#K#K ''8888 ''....Y/&/JJJ >))u}r&cd|vrAfdtjD}tj||SS)Nrc g|] }| SrGrG)r*rtables r$rHz#_sanitize_table..sBBBAuQxBBBr&)r)r num_columnsrTable from_arrays)rrr column_datas` r$_sanitize_tablersR&BBBBu/@)A)ABBB x##K #CCC r&a#version : {"1.0", "2.4", "2.6"}, default "2.6" Determine which Parquet logical types are available for use, whether the reduced set from the Parquet 1.x.x format or the expanded logical types added in later format versions. Files written with version='2.4' or '2.6' may not be readable in all Parquet implementations, so version='1.0' is likely the choice that maximizes file compatibility. UINT32 and some logical types are only available with version '2.4'. Nanosecond timestamps are only available with version '2.6'. Other features such as compression algorithms or the new serialized data page format must be enabled separately (see 'compression' and 'data_page_version'). use_dictionary : bool or list, default True Specify if we should use dictionary encoding in general or only for some columns. When encoding the column, if the dictionary size is too large, the column will fallback to ``PLAIN`` encoding. Specially, ``BOOLEAN`` type doesn't support dictionary encoding. compression : str or dict, default 'snappy' Specify the compression codec, either on a general basis or per-column. Valid values: {'NONE', 'SNAPPY', 'GZIP', 'BROTLI', 'LZ4', 'ZSTD'}. write_statistics : bool or list, default True Specify if we should write statistics in general (default is True) or only for some columns. use_deprecated_int96_timestamps : bool, default None Write timestamps to INT96 Parquet format. Defaults to False unless enabled by flavor argument. This take priority over the coerce_timestamps option. coerce_timestamps : str, default None Cast timestamps to a particular resolution. If omitted, defaults are chosen depending on `version`. For ``version='1.0'`` and ``version='2.4'``, nanoseconds are cast to microseconds ('us'), while for ``version='2.6'`` (the default), they are written natively without loss of resolution. Seconds are always cast to milliseconds ('ms') by default, as Parquet does not have any temporal type with seconds resolution. If the casting results in loss of data, it will raise an exception unless ``allow_truncated_timestamps=True`` is given. Valid values: {None, 'ms', 'us'} allow_truncated_timestamps : bool, default False Allow loss of data when coercing timestamps to a particular resolution. E.g. if microsecond or nanosecond data is lost when coercing to 'ms', do not raise an exception. Passing ``allow_truncated_timestamp=True`` will NOT result in the truncation exception being ignored unless ``coerce_timestamps`` is not None. data_page_size : int, default None Set a target threshold for the approximate encoded size of data pages within a column chunk (in bytes). If None, use the default data page size of 1MByte. flavor : {'spark'}, default None Sanitize schema or set other compatibility options to work with various target systems. filesystem : FileSystem, default None If nothing passed, will be inferred from `where` if path-like, else `where` is already a file-like object so no filesystem is needed. compression_level : int or dict, default None Specify the compression level for a codec, either on a general basis or per-column. If None is passed, arrow selects the compression level for the compression codec in use. The compression level has a different meaning for each codec, so you have to read the documentation of the codec you are using. An exception is thrown if the compression codec does not allow specifying a compression level. use_byte_stream_split : bool or list, default False Specify if the byte_stream_split encoding should be used in general or only for some columns. If both dictionary and byte_stream_stream are enabled, then dictionary is preferred. The byte_stream_split encoding is valid for integer, floating-point and fixed-size binary data types (including decimals); it should be combined with a compression codec so as to achieve size reduction. column_encoding : string or dict, default None Specify the encoding scheme on a per column basis. Can only be used when ``use_dictionary`` is set to False, and cannot be used in combination with ``use_byte_stream_split``. Currently supported values: {'PLAIN', 'BYTE_STREAM_SPLIT', 'DELTA_BINARY_PACKED', 'DELTA_LENGTH_BYTE_ARRAY', 'DELTA_BYTE_ARRAY'}. Certain encodings are only compatible with certain data types. Please refer to the encodings section of `Reading and writing Parquet files `_. data_page_version : {"1.0", "2.0"}, default "1.0" The serialized Parquet data page format version to write, defaults to 1.0. This does not impact the file schema logical types and Arrow to Parquet type casting behavior; for that use the "version" option. use_compliant_nested_type : bool, default True Whether to write compliant Parquet nested type (lists) as defined `here `_, defaults to ``True``. For ``use_compliant_nested_type=True``, this will write into a list with 3-level structure where the middle level, named ``list``, is a repeated group with a single field named ``element``:: group (LIST) { repeated group list { element; } } For ``use_compliant_nested_type=False``, this will also write into a list with 3-level structure, where the name of the single field of the middle level ``list`` is taken from the element name for nested columns in Arrow, which defaults to ``item``:: group (LIST) { repeated group list { item; } } encryption_properties : FileEncryptionProperties, default None File encryption properties for Parquet Modular Encryption. If None, no encryption will be done. The encryption properties can be created using: ``CryptoFactory.file_encryption_properties()``. write_batch_size : int, default None Number of values to write to a page at a time. If None, use the default of 1024. ``write_batch_size`` is complementary to ``data_page_size``. If pages are exceeding the ``data_page_size`` due to large column values, lowering the batch size can help keep page sizes closer to the intended size. dictionary_pagesize_limit : int, default None Specify the dictionary page size limit per row group. If None, use the default 1MB. store_schema : bool, default True By default, the Arrow schema is serialized and stored in the Parquet file metadata (in the "ARROW:schema" key). When reading the file, if this key is available, it will be used to more faithfully recreate the original Arrow data. For example, for tz-aware timestamp columns it will restore the timezone (Parquet only stores the UTC values without timezone), or columns with duration type will be restored from the int64 Parquet column. write_page_index : bool, default False Whether to write a page index in general for all columns. Writing statistics to the page index disables the old method of writing statistics to each data page header. The page index makes statistics-based filtering more efficient than the page header, as it gathers all the statistics for a Parquet file in a single place, avoiding scattered I/O. Note that the page index is not yet used on the read size by PyArrow. write_page_checksum : bool, default False Whether to write page checksums in general for all columns. Page checksums enable detection of data corruption, which might occur during transmission or in the storage. sorting_columns : Sequence of SortingColumn, default None Specify the sort order of the data being written. The writer does not sort the data nor does it verify that the data is sorted. The sort order is written to the row group metadata, which can then be used by readers. store_decimal_as_integer : bool, default False Allow decimals with 1 <= precision <= 18 to be stored as integers. In Parquet, DECIMAL can be stored in any of the following physical types: - int32: for 1 <= precision <= 9. - int64: for 10 <= precision <= 18. - fixed_len_byte_array: precision is limited by the array size. Length n can store <= floor(log_10(2^(8*n - 1) - 1)) base-10 digits. - binary: precision is unlimited. The minimum number of bytes to store the unscaled value is used. By default, this is DISABLED and all decimal types annotate fixed_len_byte_array. When enabled, the writer will use the following physical types to store decimals: - int32: for 1 <= precision <= 9. - int64: for 10 <= precision <= 18. - fixed_len_byte_array: for precision > 18. As a consequence, decimal columns stored in integer types are more compact. atGenerate an example PyArrow Table and RecordBatch: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> batch = pa.record_batch([[2, 2, 4, 4, 5, 100], ... ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]], ... names=['n_legs', 'animal']) create a ParquetWriter object: >>> import pyarrow.parquet as pq >>> writer = pq.ParquetWriter('example.parquet', table.schema) and write the Table into the Parquet file: >>> writer.write_table(table) >>> writer.close() >>> pq.read_table('example.parquet').to_pandas() n_legs animal 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede create a ParquetWriter object for the RecordBatch: >>> writer2 = pq.ParquetWriter('example2.parquet', batch.schema) and write the RecordBatch into the Parquet file: >>> writer2.write_batch(batch) >>> writer2.close() >>> pq.read_table('example2.parquet').to_pandas() n_legs animal 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede ceZdZdeeZ ddZd Zd Z d Z dd Z dd Z ddZ dZdZdS) ParquetWritera Class for incrementally building a Parquet file for Arrow tables. Parameters ---------- where : path or file-like object schema : pyarrow.Schema {} writer_engine_version : unused **options : dict If options contains a key `metadata_collector` then the corresponding value is assumed to be a list (or any object with `.append` method) that will be filled with the file metadata instance of the written file. Examples -------- {} N2.6TsnappyF1.0c | |d|vrd} nd} ||_|t||\}|_nd|_||_||_d|_t ||\}}|||dx}|_n|}|dd|_ d}tj ||fid|d|d |d |d | d | d | d| d|d|d|d|d|d|d|d|d|d|d|||_ d|_ dS)NrTF) compressionmetadata_collectorV2versionruse_dictionarywrite_statisticsuse_deprecated_int96_timestampscompression_leveluse_byte_stream_splitcolumn_encodingwriter_engine_versiondata_page_versionuse_compliant_nested_typeencryption_propertieswrite_batch_sizedictionary_pagesize_limit store_schemawrite_page_indexwrite_page_checksumsorting_columnsstore_decimal_as_integer)rrrrwhere file_handleropen_output_streampop_metadata_collector_parquetrwriteris_open)rlrrr`rrrrrrrrrrrrrrrrrrrroptionsrsinkengine_versions r$rnzParquetWriter.__init__s, + 2!g&7&726//27/  *:66*J*J 'FD''"'D    7zJJ D  !'1&C&C$'D'('( (D4##D#*;;/CT#J#J , &G$ *>  .-  -L,K 0/#8"7,O#1.0/'@&?#8"7.-'@&? &!".-#$!4 3%&,O'(&>%=+ , r&cTt|ddr|dSdS)NrF)rerurps r$__del__zParquetWriter.__del__s1 4E * *  JJLLLLL  r&c|Sr.rGrps r$rqzParquetWriter.__enter__rrr&c.|dS)NFrtrvs r$ryzParquetWriter.__exit__s ur&ct|tjr|||dSt|tjr|||dSt t|)a{ Write RecordBatch or Table to the Parquet file. Parameters ---------- table_or_batch : {RecordBatch, Table} row_group_size : int, default None Maximum number of rows in each written row group. If None, the row group size will be the minimum of the input table or batch length and 1024 * 1024. N)rr RecordBatch write_batchr write_table TypeErrorr)rltable_or_batchrow_group_sizes r$writezParquetWriter.write sz nbn 5 5 2   ^^ < < < < <  1 1 2   ^^ < < < < <D0011 1r&c~tj|g|j}|||dS)a Write RecordBatch to the Parquet file. Parameters ---------- batch : RecordBatch row_group_size : int, default None Maximum number of rows in written row group. If None, the row group size will be the minimum of the RecordBatch size and 1024 * 1024. If set larger than 64Mi then 64Mi will be used instead. N)rr from_batchesrr)rlbatchrrs r$rzParquetWriter.write_batch3s;%%ugu|<< /////r&c4|jrt||j|j}|jsJ|j|jds/d|j|j}t||j ||dS)a Write Table to the Parquet file. Parameters ---------- table : Table row_group_size : int, default None Maximum number of rows in each written row group. If None, the row group size will be the minimum of the Table size and 1024 * 1024. If set larger than 64Mi then 64Mi will be used instead. F)check_metadatazTTable schema does not match schema used to create file: table: {!s} vs. file: {!s}rN) rrrrrequalsrCr1rr)rlrrmsgs r$rzParquetWriter.write_tableCs   E#E4; DDE||""4;u"EE "6F5<55 S// ! nEEEEEr&c|jrK|jd|_|j$|j|jj|j|jdSdS)z; Close the connection to the Parquet file. FN)rrrurrLrVrrps r$ruzParquetWriter.close]s{ < F K      DL'3(// 0DEEE   '   " " $ $ $ $ $ ( 'r&cL|jsJ|j|dS)a  Add key-value metadata to the file. This will overwrite any existing metadata with the same key. Parameters ---------- key_value_metadata : dict Keys and values must be string-like / coercible to bytes. N)rradd_key_value_metadata)rlkey_value_metadatas r$rz$ParquetWriter.add_key_value_metadatais/| **+=>>>>>r&)NNrTrTNNFNNrTNNNTFFNFr.)rrrrC_parquet_writer_arg_docs_parquet_writer_example_docrrnrrqryrrrrurrGr&r$rrs$ F #%@AA% (26 $%"&15#'',!%'+#(+/'+"&+/""'%*!%*/)KKKKZ 2222&0000 FFFF4 % % % ? ? ? ? ?r&rchtj|dddS)Nrutf8r)jsonloadsdecode) keyvaluess r$rrws/ Jy+226:: ; ;  r&_SUCCESSaread_dictionary : list, default None List of names or column paths (for nested types) to read directly as DictionaryArray. Only supported for BYTE_ARRAY storage. To read a flat column as dictionary-encoded pass the column name. For nested types, you must pass the full column "path", which could be something like level1.level2.list.item. Refer to the Parquet file's schema to obtain the paths. memory_map : bool, default False If the source is a file path, use a memory map to read file, which can improve performance in some environments. buffer_size : int, default 0 If positive, perform read buffering when deserializing individual column chunks. Otherwise IO calls are unbuffered. partitioning : pyarrow.dataset.Partitioning or str or list of str, default "hive" The partitioning scheme for a partitioned dataset. The default of "hive" assumes directory names with key=value pairs like "/year=2009/month=11". In addition, a scheme like "/2009/11" is also supported, in which case you need to specify the field names or a full schema. See the ``pyarrow.dataset.partitioning()`` function for more details.aaGenerate an example PyArrow Table and write it to a partitioned dataset: >>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2', ... partition_cols=['year']) create a ParquetDataset object from the dataset source: >>> dataset = pq.ParquetDataset('dataset_v2/') and read the data: >>> dataset.read().to_pandas() n_legs animal year 0 5 Brittle stars 2019 1 2 Flamingo 2020 2 4 Dog 2021 3 100 Centipede 2021 4 2 Parrot 2022 5 4 Horse 2022 create a ParquetDataset object with filter: >>> dataset = pq.ParquetDataset('dataset_v2/', ... filters=[('n_legs','=',4)]) >>> dataset.read().to_pandas() n_legs animal year 0 4 Dog 2021 1 4 Horse 2022 ceZdZdeeeZdddddddddddddd dZdZ d Z e d Z dd Z d Zd Ze dZe dZe dZe dZdS)ParquetDataseta Encapsulates details of reading a complete Parquet dataset possibly consisting of multiple files and partitions in subdirectories. Parameters ---------- path_or_paths : str or List[str] A directory name, single file name, or list of file names. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. schema : pyarrow.parquet.Schema Optionally provide the Schema for the Dataset, in which case it will not be inferred from the source. filters : pyarrow.compute.Expression or List[Tuple] or List[List[Tuple]], default None Rows which do not match the filter predicate will be removed from scanned data. Partition keys embedded in a nested directory structure will be exploited to avoid loading files at all if they contain no matching rows. Within-file level filtering and different partitioning schemes are supported. {1} {0} ignore_prefixes : list, optional Files matching any of these prefixes will be ignored by the discovery process. This is matched to the basename of a path. By default this is ['.', '_']. Note that discovery happens only if a directory is passed as source. pre_buffer : bool, default True Coalesce and issue file reads in parallel to improve performance on high-latency filesystems (e.g. S3, GCS). If True, Arrow will use a background I/O thread pool. If using a filesystem layer that itself performs readahead (e.g. fsspec's S3FS), disable readahead for best results. Set to False if you want to prioritize minimal memory usage over maximum speed. coerce_int96_timestamp_unit : str, default None Cast timestamps that are stored in INT96 format to a particular resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 timestamps will be inferred as timestamps in nanoseconds. decryption_properties : FileDecryptionProperties or None File-level decryption properties. The decryption properties can be created using ``CryptoFactory.file_decryption_properties()``. thrift_string_size_limit : int, default None If not None, override the maximum total string size allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. thrift_container_size_limit : int, default None If not None, override the maximum total size of containers allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. page_checksum_verification : bool, default False If True, verify the page checksum for each page read from the file. Examples -------- {2} NFhiveT) r5rXrYrZ partitioningignore_prefixesr[r\r]r^r_rac Fddlm}| | | ||d}|r|d||||| || d|_|t ||_|t ||}n||rt |}t|dr6|4t|t std t|d}d|_ t|tst|r|t|}|9 tj|\}}n #t"$rt |}YnwxYw||}|j t&jkr||_ n|}|jdi|}|C|||}||g|p|j||j |_dS|d kr|jd }||||||| |_dS)Nr)r[r\r^r_raT)use_buffered_streamrZ)dictionary_columns)r])use_mmap __fspath__zQPath-like objects with __fspath__ must only be used with local file systems, not )rrCr`r)infer_dictionary)r`rrCr r!rG)rIrJupdate_filter_expressionrRrrhasattrrrr _base_dirr2rrrfrom_urir1 get_file_infor DirectoryParquetFileFormat make_fragmentFileSystemDatasetphysical_schemar`_datasetHivePartitioningdiscover)rl path_or_pathsr`rr5rXrYrZr r!r[r\r]r^r_rarD read_options single_filefinfoparquet_formatfragments r$rnzParquetDataset.__init__s %$$$$$%+F(@+F*D     9   D,7  9 9 9  &   ?  C C C ,   6K  L L L"&  &;G&D&DD #  !+Z111JJ  J )*===J M< 0 0   ":77 #>+/ +;+;>>   -.. ,]++ , / > > %J4>4G)5+5+1 MM%JJJ%4j%I%I%I J"00??:!333%2DN+ --== ==  "%33KLLH00 6#EX-E%#.1DM F 6 ! !.77!%8''L =Z*00<3B#DD s"D::EEct|tstd|j|jko9|jj|jjko|j|jko|j|jkS)Nz-`other` must be an instance of ParquetDataset)rrrrr3rCr`filesrlothers r$rzParquetDataset.equalsVsq%00 MKLL L u|+* $(==*5#33* ek)  +r&c\ ||S#t$r tcYSwxYwr.)rrNotImplementedr>s r$__eq__zParquetDataset.__eq__`s@ ";;u%% % " " "! ! ! ! "s ++c|jjS)a> Schema of the Dataset. Examples -------- Generate an example dataset: >>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_schema', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_schema/') Read the schema: >>> dataset.schema n_legs: int64 animal: string year: dictionary )r3rrps r$rzParquetDataset.schemafs4}##r&c|jjpi}|rd|vr|}|r|}|]|r[|rYd|vrUdt|D}t |t t |t |z z}|j||j|}|rF|rDd|vr@|jjpi}| d|di| |}|S)a Read (multiple) Parquet files as a single pyarrow.Table. Parameters ---------- columns : List[str] Names of columns to read from the dataset. The partition fields are not automatically included. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.Table Content of the file as a table (of columns). Examples -------- Generate an example dataset: >>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_read', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_read/') Read the dataset: >>> dataset.read(columns=["n_legs"]) pyarrow.Table n_legs: int64 ---- n_legs: [[5],[2],[4,100],[2,4]] rNc<g|]}t|t|SrG)rr)r*r8s r$rHz'ParquetDataset.read..s8!!!%c400!!!!r&)rfilterr) rrV_get_common_pandas_metadatarr2setr3to_tabler)r(replace_schema_metadata) rlrrrrVrWrr new_metadatas r$rzParquetDataset.readsGX;'-2  /(("&"B"B"D"D"/.H  #6  I11!!#>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'table_V2.parquet') >>> dataset = pq.ParquetDataset('table_V2.parquet') Read the dataset with pandas metadata: >>> dataset.read_pandas(columns=["n_legs"]) pyarrow.Table n_legs: int64 ---- n_legs: [[2,2,4,4,5,100]] >>> dataset.read_pandas(columns=["n_legs"]).schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, ...} rTrG)r)rlrxs r$ read_pandaszParquetDataset.read_pandass"Hty<>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_fragments', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_fragments/') List the fragments: >>> dataset.fragments [>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_files', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_files/') List the files: >>> dataset.files ['dataset_v2_files/year=2019/...-0.parquet', ... )r3r=rps r$r=zParquetDataset.files%s0}""r&c|jjS)z< The filesystem type of the Dataset source. )r3r`rps r$r`zParquetDataset.filesystem?s }''r&c|jjS)zH The partitioning of the Dataset source, if discovered. )r3r rps r$r zParquetDataset.partitioningFs }))r&NNr)rrrrC_read_docstring_common_DNF_filter_doc_parquet_dataset_examplerrnrrBrrrrGrVrYr=r`r rGr&r$rrs\:t F !?4LMMu x[Dt!%%T$dt-1'+d-1,1 [D[D[D[D[Dz+++""" $$X$6NNNN`$$=$=$=L33X34##X#2((X( **X***r&ra( {0} Parameters ---------- source : str, pyarrow.NativeFile, or file-like object If a string passed, can be a single file name or directory name. For file-like objects, only read a single file. Use pyarrow.BufferReader to read a file contained in a bytes or buffer-like object. columns : list If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. If empty, no columns will be read. Note that the table will still have the correct num_rows set despite having no columns. use_threads : bool, default True Perform multi-threaded column reads. schema : Schema, optional Optionally provide the Schema for the parquet dataset, in which case it will not be inferred from the source. {1} filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. filters : pyarrow.compute.Expression or List[Tuple] or List[List[Tuple]], default None Rows which do not match the filter predicate will be removed from scanned data. Partition keys embedded in a nested directory structure will be exploited to avoid loading files at all if they contain no matching rows. Within-file level filtering and different partitioning schemes are supported. {3} ignore_prefixes : list, optional Files matching any of these prefixes will be ignored by the discovery process. This is matched to the basename of a path. By default this is ['.', '_']. Note that discovery happens only if a directory is passed as source. pre_buffer : bool, default True Coalesce and issue file reads in parallel to improve performance on high-latency filesystems (e.g. S3). If True, Arrow will use a background I/O thread pool. If using a filesystem layer that itself performs readahead (e.g. fsspec's S3FS), disable readahead for best results. coerce_int96_timestamp_unit : str, default None Cast timestamps that are stored in INT96 format to a particular resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 timestamps will be inferred as timestamps in nanoseconds. decryption_properties : FileDecryptionProperties or None File-level decryption properties. The decryption properties can be created using ``CryptoFactory.file_decryption_properties()``. thrift_string_size_limit : int, default None If not None, override the maximum total string size allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. thrift_container_size_limit : int, default None If not None, override the maximum total size of containers allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. page_checksum_verification : bool, default False If True, verify the checksum for each page read from the file. Returns ------- {2} {4} ad Examples -------- Generate an example PyArrow Table and write it to a partitioned dataset: >>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_name_2', ... partition_cols=['year']) Read the data: >>> pq.read_table('dataset_name_2').to_pandas() n_legs animal year 0 5 Brittle stars 2019 1 2 Flamingo 2020 2 4 Dog 2021 3 100 Centipede 2021 4 2 Parrot 2022 5 4 Horse 2022 Read only a subset of columns: >>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"]) pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[5],[2],[4,100],[2,4]] animal: [["Brittle stars"],["Flamingo"],["Dog","Centipede"],["Parrot","Horse"]] Read a subset of columns and read one column as DictionaryArray: >>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"], ... read_dictionary=["animal"]) pyarrow.Table n_legs: int64 animal: dictionary ---- n_legs: [[5],[2],[4,100],[2,4]] animal: [ -- dictionary: ["Brittle stars"] -- indices: [0], -- dictionary: ["Flamingo"] -- indices: [0], -- dictionary: ["Dog","Centipede"] -- indices: [0,1], -- dictionary: ["Parrot","Horse"] -- indices: [0,1]] Read the table with filter: >>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"], ... filters=[('n_legs','<',4)]).to_pandas() n_legs animal 0 2 Flamingo 1 2 Parrot Read data from a single Parquet file: >>> pq.write_table(table, 'example.parquet') >>> pq.read_table('dataset_name_2').to_pandas() n_legs animal year 0 5 Brittle stars 2019 1 2 Flamingo 2020 2 4 Dog 2021 3 100 Centipede 2021 4 2 Parrot 2022 5 4 Horse 2022 Fr)rrrrrXrYrZr r`r5r!r[r\r]r^r_rac t||| ||||| | | | ||||}n#t$r}| td|dkrtd|tdt|| \} }| | |}t ||||| | |||| }YnwxYw||||S)N)rr`r rYrXrZr5r!r[r\r]r^r_razWthe 'filters' keyword is not supported when the pyarrow.dataset module is not availablerz\the 'partitioning' keyword is not supported when the pyarrow.dataset module is not availablezWthe 'schema' argument is not supported when the pyarrow.dataset module is not available) rXrYrZr[r\r]r^r_ra)rrr)r ImportErrorr1rrgrUr)rmrrrrrXrYrZr r`r5r!r[r\r]r^r_rarJrs r$ read_tablercsT1  !%!+#+!(C"7%=(C'A   "      :  6 ! !:   : 8 KK D  !//55F O!{!(C"7%=(C'A    - B <<[,?  A AAs!BB('B(z Read a Table from Parquet format zuse_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded.z=pyarrow.Table Content of the file as a table (of columns)c "t|f|dd|S)NT)rr)rc)rmrrxs r$rVrV.s.  T  =C  r&zcRead a Table from Parquet format, also reading DataFrame index values if known in the file metadataz6**kwargs additional options for :func:`read_table`zgpyarrow.Table Content of the file as a Table of Columns, including DataFrame indexes as columnsrrrc |d|}|} t||jfid| d|d| d|d|d|d| d | d |d |d | d |d|d|d|d|d|d|d|d|d|d|d||5}|||ddddS#1swxYwYdS#t$rIt |r8 t jt|n#t j $rYnwxYwwxYw)N chunk_sizer`rrrrcoerce_timestampsdata_page_sizeallow_truncated_timestampsrrrrrrrrrrrrrrrr ) rrrr ExceptionrrPremovererror)rrrrrrrrrirkrjrr`rrrrrrrrrrrrrrx use_int96rs r$rr@sj0ZZ n==N/I" u|%: v  .~  "2!1 #4"3 .~,F+E(K1: #4"3'<&;!0#4"3 +D*C!"'<&;#$"2!1%&+D*C'(*\)*"2!1+,%8$7-.!0/0*B)A3 E2$   u^  D D D5 E E E E E E E E E E E E E E E E E E6       /%0011118     sTAB$2B B$BB$BB$$C7>!C C7 C2/C71C22C7aGenerate an example PyArrow Table: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) and write the Table into Parquet file: >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') Defining row group size for the Parquet file: >>> pq.write_table(table, 'example.parquet', row_group_size=3) Defining row group compression (default is Snappy): >>> pq.write_table(table, 'example.parquet', compression='none') Defining row group compression and encoding per-column: >>> pq.write_table(table, 'example.parquet', ... compression={'n_legs': 'snappy', 'animal': 'gzip'}, ... use_dictionary=['n_legs', 'animal']) Defining column encoding per-column: >>> pq.write_table(table, 'example.parquet', ... column_encoding={'animal':'PLAIN'}, ... use_dictionary=False) ap Write a Table to Parquet format. Parameters ---------- table : pyarrow.Table where : string or pyarrow.NativeFile row_group_size : int Maximum number of rows in each written row group. If None, the row group size will be the minimum of the Table size and 1024 * 1024. {} **kwargs : optional Additional options for ParquetWriter Examples -------- {} c | ddd} |%|#t| dd%|#t| ddddlm} t } t j| jj D]}|| vr| || |<| d| d d| d <fd }| }|j di| }|t|}|r1| |j}| |d }|t!dz}| d} | j||f||||||||| d | dS)a%Wrapper around dataset.write_dataset for writing a Table to Parquet format by partitions. For each combination of partition columns and values, a subdirectories are created in the following manner: root_dir/ group1=value1 group2=value1 .parquet group2=value2 .parquet group1=valueN group2=value1 .parquet group2=valueN .parquet Parameters ---------- table : pyarrow.Table root_path : str, pathlib.Path The root directory of the dataset. partition_cols : list, Column names by which to partition the dataset. Columns are partitioned in the order they are given. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. schema : Schema, optional This Schema of the dataset. partitioning : Partitioning or list[str], optional The partitioning scheme specified with the ``pyarrow.dataset.partitioning()`` function or a list of field names. When providing a list of field names, you can use ``partitioning_flavor`` to drive which partitioning type should be used. basename_template : str, optional A template string used to generate basenames of written data files. The token '{i}' will be replaced with an automatically incremented integer. If not specified, it defaults to "guid-{i}.parquet". use_threads : bool, default True Write files in parallel. If enabled, then maximum parallelism will be used determined by the number of available CPU cores. file_visitor : function If set, this function will be called with a WrittenFile instance for each file created during the call. This object will have both a path attribute and a metadata attribute. The path attribute will be a string containing the path to the created file. The metadata attribute will be the parquet metadata of the file. This metadata will have the file path attribute set and can be used to build a _metadata file. The metadata attribute will be None if the format is not parquet. Example visitor which simple collects the filenames created:: visited_paths = [] def file_visitor(written_file): visited_paths.append(written_file.path) existing_data_behavior : 'overwrite_or_ignore' | 'error' | 'delete_matching' Controls how the dataset will handle data that already exists in the destination. The default behaviour is 'overwrite_or_ignore'. 'overwrite_or_ignore' will ignore any existing data and will overwrite files with the same name as an output file. Other existing files will be ignored. This behavior, in combination with a unique basename_template for each write, will allow for an append workflow. 'error' will raise an error if any data exists in the destination. 'delete_matching' is useful when you are writing a partitioned dataset. The first time each partition directory is encountered the entire directory will be deleted. This allows you to overwrite old partitions completely. **kwargs : dict, Used as additional kwargs for :func:`pyarrow.dataset.write_dataset` function for matching kwargs, and remainder to :func:`pyarrow.dataset.ParquetFileFormat.make_write_options`. See the docstring of :func:`write_table` and :func:`pyarrow.dataset.write_dataset` for the available options. Using `metadata_collector` in kwargs allows one to collect the file metadata instances of dataset pieces. The file paths in the ColumnChunkMetaData will be set relative to `root_path`. Examples -------- Generate an example PyArrow Table: >>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) and write it to a partitioned dataset: >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_name_3', ... partition_cols=['year']) >>> pq.ParquetDataset('dataset_name_3').files ['dataset_name_3/year=2019/...-0.parquet', ... Write a single Parquet file into the root folder: >>> pq.write_to_dataset(table, root_path='dataset_name_4') >>> pq.ParquetDataset('dataset_name_4/').files ['dataset_name_4/...-0.parquet'] rNz.file_visitorKs  % %l&; < < < < )).*:<<== =%,*B)).*>@@AA A!      66 !122=88 &==(. 3  %17&**\48822-.% = = = = =))++N5N5????M' 33 Cll>229 {6BB   FF^3%!6B y %/M&!{!+5        Fr&c t||\}}t|dr|}t|||fi|}||t ||}t|dr|||D]}|||E||5} | | ddddS#1swxYwYdS| |dSdS)a& Write metadata-only Parquet file from schema. This can be used with `write_to_dataset` to generate `_common_metadata` and `_metadata` sidecar files. Parameters ---------- schema : pyarrow.Schema where : string or pyarrow.NativeFile metadata_collector : list where to collect metadata information. filesystem : FileSystem, default None If nothing passed, will be inferred from `where` if path-like, else `where` is already a file-like object so no filesystem is needed. **kwargs : dict, Additional kwargs for ParquetWriter class. See docstring for `ParquetWriter` for more information. Examples -------- Generate example data: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) Write a dataset and collect metadata information. >>> metadata_collector = [] >>> import pyarrow.parquet as pq >>> pq.write_to_dataset( ... table, 'dataset_metadata', ... metadata_collector=metadata_collector) Write the `_common_metadata` parquet file without row groups statistics. >>> pq.write_metadata( ... table.schema, 'dataset_metadata/_common_metadata') Write the `_metadata` parquet file with row groups statistics. >>> pq.write_metadata( ... table.schema, 'dataset_metadata/_metadata', ... metadata_collector=metadata_collector) seekNrO) rr*tellrrurRrappend_row_groupsrwrite_metadata_file) rrrr`rxcursor_positionrrVmr+s r$write_metadatarksu`5UJGGJuf'**,, 5&* ? ? ? ?F LLNNN%!:>>> 5& ! ! ( JJ ' ' '# * *A  & &q ) ) ) )  !..u55 0,,Q/// 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  ( ( / / / / /&%sC''C+.C+ct||\}}t}|||x}}|5t|||}|jcdddS#1swxYwYdS)a Read FileMetaData from footer of a single Parquet file. Parameters ---------- where : str (file path) or file-like object memory_map : bool, default False Create memory map when the source is a file path. decryption_properties : FileDecryptionProperties, default None Decryption properties for reading encrypted Parquet files. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. Returns ------- metadata : FileMetaData The metadata of the Parquet file Examples -------- >>> import pyarrow as pa >>> import pyarrow.parquet as pq >>> table = pa.table({'n_legs': [4, 5, 100], ... 'animal': ["Dog", "Brittle stars", "Centipede"]}) >>> pq.write_table(table, 'example.parquet') >>> pq.read_metadata('example.parquet') created_by: parquet-cpp-arrow version ... num_columns: 2 num_rows: 3 num_row_groups: 1 format_version: 2.6 serialized_size: ... NrYr])rrrgrUrVrrYr]r`file_ctxfiles r$rRrRsN5UJGGJ}}H%55e<<<5 5Z1FHHH}sA##A'*A'ct||\}}t}|||x}}|5t|||}|jcdddS#1swxYwYdS)a Read effective Arrow schema from Parquet file metadata. Parameters ---------- where : str (file path) or file-like object memory_map : bool, default False Create memory map when the source is a file path. decryption_properties : FileDecryptionProperties, default None Decryption properties for reading encrypted Parquet files. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. Returns ------- schema : pyarrow.Schema The schema of the Parquet file Examples -------- >>> import pyarrow as pa >>> import pyarrow.parquet as pq >>> table = pa.table({'n_legs': [4, 5, 100], ... 'animal': ["Dog", "Brittle stars", "Centipede"]}) >>> pq.write_table(table, 'example.parquet') >>> pq.read_schema('example.parquet') n_legs: int64 animal: string Nr)rrrgrUrto_arrow_schemars r$ read_schemarsD5UJGGJ}}H%55e<<<5 -- j"7999{**,, ------------------s+A55A9<A9)r r rrr rrUrrr rr rrrRrVrrcrrrrSrR)Tr.)NrTrTNNFNNNNFNrTNNNTFFNF)NNNNNNNNr])FNN)J collectionsr contextlibr functoolsrrzrrPrerMpyarrowrpyarrow._parquetrrbexcr rrr r r r r rrrr pyarrow.fsrrrrr pyarrow.utilrrrrr%r:r_rRDeprecationWarningrSrUcompilerrrrrrrrEXCLUDED_PARQUET_PATHSr^r`r_read_table_docstring_read_table_examplercrCrrrVr_write_table_examplerrrRr__all__rGr&r$rsa&$#####"""""" ''''''' + 4(+C 4 4 4    --------------------------JJJJJJJJJJJJJJMMMMMMMMMMMM   6&R@5@5@5F(58%799aaaaaaaaH%"*%5662220^@0fC?C?C?C?C?C?C?C?L %E.#LO*O*O*O*O*O*O*O*d ENL^#'DtQVdD%)D+/*/=A=A=A=A=A@+11*II&'=?@@3(**  ,221II%1233R