# Licensed to the Apache Software Foundation (ASF) under one# or more contributor license agreements. See the NOTICE file# distributed with this work for additional information# regarding copyright ownership. The ASF licenses this file# to you under the Apache License, Version 2.0 (the# "License"); you may not use this file except in compliance# with the License. You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing,# software distributed under the License is distributed on an# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY# KIND, either express or implied. See the License for the# specific language governing permissions and limitations# under the License.fromcollectionsimportdefaultdictfromconcurrentimportfuturesfromcontextlibimportnullcontextfromfunctoolsimportpartial,reduceimportinspectimportjsonfromcollections.abcimportCollectionimportnumpyasnpimportosimportreimportoperatorimporturllib.parseimportwarningsimportpyarrowaspaimportpyarrow.libaslibimportpyarrow._parquetas_parquetfrompyarrow._parquetimport(ParquetReader,Statistics,# noqaFileMetaData,RowGroupMetaData,ColumnChunkMetaData,ParquetSchema,ColumnSchema,ParquetLogicalType,FileEncryptionProperties,FileDecryptionProperties)frompyarrow.fsimport(LocalFileSystem,FileSystem,FileType,_resolve_filesystem_and_path,_ensure_filesystem)frompyarrowimportfilesystemaslegacyfsfrompyarrow.utilimportguid,_is_path_like,_stringify_path,_deprecate_api_URI_STRIP_SCHEMES=('hdfs',)def_parse_uri(path):path=_stringify_path(path)parsed_uri=urllib.parse.urlparse(path)ifparsed_uri.schemein_URI_STRIP_SCHEMES:returnparsed_uri.pathelse:# ARROW-4073: On Windows returning the path with the scheme# stripped removes the drive letter, if anyreturnpathdef_get_filesystem_and_path(passed_filesystem,path):ifpassed_filesystemisNone:returnlegacyfs.resolve_filesystem_and_path(path,passed_filesystem)else:passed_filesystem=legacyfs._ensure_filesystem(passed_filesystem)parsed_path=_parse_uri(path)returnpassed_filesystem,parsed_pathdef_check_contains_null(val):ifisinstance(val,bytes):forbyteinval:ifisinstance(byte,bytes):compare_to=chr(0)else:compare_to=0ifbyte==compare_to:returnTrueelifisinstance(val,str):return'\x00'invalreturnFalsedef_check_filters(filters,check_null_strings=True):""" Check if filters are well-formed. """iffiltersisnotNone:iflen(filters)==0orany(len(f)==0forfinfilters):raiseValueError("Malformed filters")ifisinstance(filters[0][0],str):# We have encountered the situation where we have one nesting level# too few:# We have [(,,), ..] instead of [[(,,), ..]]filters=[filters]ifcheck_null_strings:forconjunctioninfilters:forcol,op,valinconjunction:if(isinstance(val,list)andall(_check_contains_null(v)forvinval)or_check_contains_null(val)):raiseNotImplementedError("Null-terminated binary strings are not supported ""as filter values.")returnfilters_DNF_filter_doc="""Predicates 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'}) """deffilters_to_expression(filters):""" 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')]) <pyarrow.compute.Expression (foo == "bar")> Returns ------- pyarrow.compute.Expression An Expression representing the filters """importpyarrow.datasetasdsifisinstance(filters,ds.Expression):returnfiltersfilters=_check_filters(filters,check_null_strings=False)defconvert_single_predicate(col,op,val):field=ds.field(col)ifop=="="orop=="==":returnfield==valelifop=="!=":returnfield!=valelifop=='<':returnfield<valelifop=='>':returnfield>valelifop=='<=':returnfield<=valelifop=='>=':returnfield>=valelifop=='in':returnfield.isin(val)elifop=='not in':return~field.isin(val)else:raiseValueError('"{0}" is not a valid operator in predicates.'.format((col,op,val)))disjunction_members=[]forconjunctioninfilters:conjunction_members=[convert_single_predicate(col,op,val)forcol,op,valinconjunction]disjunction_members.append(reduce(operator.and_,conjunction_members))returnreduce(operator.or_,disjunction_members)_filters_to_expression=_deprecate_api("_filters_to_expression","filters_to_expression",filters_to_expression,"10.0.0",DeprecationWarning)# ----------------------------------------------------------------------# Reading a single Parquet file
[docs]classParquetFile:""" 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. 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<values=string, indices=int32, ordered=0> ---- n_legs: [[2,2,4,4,5,100]] animal: [ -- dictionary: ["Flamingo","Parrot",...,"Brittle stars","Centipede"] -- indices: [0,1,2,3,4,5]] """
[docs]def__init__(self,source,*,metadata=None,common_metadata=None,read_dictionary=None,memory_map=False,buffer_size=0,pre_buffer=False,coerce_int96_timestamp_unit=None,decryption_properties=None,thrift_string_size_limit=None,thrift_container_size_limit=None,filesystem=None):self._close_source=getattr(source,'closed',True)filesystem,source=_resolve_filesystem_and_path(source,filesystem,memory_map)iffilesystemisnotNone:source=filesystem.open_input_file(source)self._close_source=True# We opened it here, ensure we close it.self.reader=ParquetReader()self.reader.open(source,use_memory_map=memory_map,buffer_size=buffer_size,pre_buffer=pre_buffer,read_dictionary=read_dictionary,metadata=metadata,coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,decryption_properties=decryption_properties,thrift_string_size_limit=thrift_string_size_limit,thrift_container_size_limit=thrift_container_size_limit,)self.common_metadata=common_metadataself._nested_paths_by_prefix=self._build_nested_paths()
def__enter__(self):returnselfdef__exit__(self,*args,**kwargs):self.close()def_build_nested_paths(self):paths=self.reader.column_pathsresult=defaultdict(list)fori,pathinenumerate(paths):key=path[0]rest=path[1:]whileTrue:result[key].append(i)ifnotrest:breakkey='.'.join((key,rest[0]))rest=rest[1:]returnresult@propertydefmetadata(self):""" Return the Parquet metadata. """returnself.reader.metadata@propertydefschema(self):""" Return the Parquet schema, unconverted to Arrow types """returnself.metadata.schema@propertydefschema_arrow(self):""" 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 """returnself.reader.schema_arrow@propertydefnum_row_groups(self):""" 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 """returnself.reader.num_row_groups
[docs]defread_row_group(self,i,columns=None,use_threads=True,use_pandas_metadata=False):""" 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"]] """column_indices=self._get_column_indices(columns,use_pandas_metadata=use_pandas_metadata)returnself.reader.read_row_group(i,column_indices=column_indices,use_threads=use_threads)
[docs]defread_row_groups(self,row_groups,columns=None,use_threads=True,use_pandas_metadata=False):""" 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"]] """column_indices=self._get_column_indices(columns,use_pandas_metadata=use_pandas_metadata)returnself.reader.read_row_groups(row_groups,column_indices=column_indices,use_threads=use_threads)
[docs]defiter_batches(self,batch_size=65536,row_groups=None,columns=None,use_threads=True,use_pandas_metadata=False):""" 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 """ifrow_groupsisNone:row_groups=range(0,self.metadata.num_row_groups)column_indices=self._get_column_indices(columns,use_pandas_metadata=use_pandas_metadata)batches=self.reader.iter_batches(batch_size,row_groups=row_groups,column_indices=column_indices,use_threads=use_threads)returnbatches
[docs]defread(self,columns=None,use_threads=True,use_pandas_metadata=False):""" 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"]] """column_indices=self._get_column_indices(columns,use_pandas_metadata=use_pandas_metadata)returnself.reader.read_all(column_indices=column_indices,use_threads=use_threads)
[docs]defscan_contents(self,columns=None,batch_size=65536):""" 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 """column_indices=self._get_column_indices(columns)returnself.reader.scan_contents(column_indices,batch_size=batch_size)
_SPARK_DISALLOWED_CHARS=re.compile('[ ,;{}()\n\t=]')def_sanitized_spark_field_name(name):return_SPARK_DISALLOWED_CHARS.sub('_',name)def_sanitize_schema(schema,flavor):if'spark'inflavor:sanitized_fields=[]schema_changed=Falseforfieldinschema:name=field.namesanitized_name=_sanitized_spark_field_name(name)ifsanitized_name!=name:schema_changed=Truesanitized_field=pa.field(sanitized_name,field.type,field.nullable,field.metadata)sanitized_fields.append(sanitized_field)else:sanitized_fields.append(field)new_schema=pa.schema(sanitized_fields,metadata=schema.metadata)returnnew_schema,schema_changedelse:returnschema,Falsedef_sanitize_table(table,new_schema,flavor):# TODO: This will not handle prohibited characters in nested field namesif'spark'inflavor:column_data=[table[i]foriinrange(table.num_columns)]returnpa.Table.from_arrays(column_data,schema=new_schema)else:returntable_parquet_writer_arg_docs="""version : {"1.0", "2.4", "2.6"}, default "2.4" 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 Specify if we should use dictionary encoding in general or only for some columns.compression : str or dict 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 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`. By default, for ``version='1.0'`` (the default) and ``version='2.4'``, nanoseconds are cast to microseconds ('us'), while for other `version` values, 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 only for floating-point data types and should be combined with a compression codec.column_encoding : string or dict, default None Specify the encoding scheme on a per column basis. Currently supported values: {'PLAIN', 'BYTE_STREAM_SPLIT'}. Certain encodings are only compatible with certain data types. Please refer to the encodings section of `Reading and writing Parquet files <https://arrow.apache.org/docs/cpp/parquet.html#encodings>`_.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 False Whether to write compliant Parquet nested type (lists) as defined `here <https://github.com/apache/parquet-format/blob/master/ LogicalTypes.md#nested-types>`_, defaults to ``False``. 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``:: <list-repetition> group <name> (LIST) { repeated group list { <element-repetition> <element-type> 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``:: <list-repetition> group <name> (LIST) { repeated group list { <element-repetition> <element-type> 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."""_parquet_writer_example_doc="""\Generate 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 animal0 2 Flamingo1 2 Parrot2 4 Dog3 4 Horse4 5 Brittle stars5 100 Centipedecreate 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 animal0 2 Flamingo1 2 Parrot2 4 Dog3 4 Horse4 5 Brittle stars5 100 Centipede"""
[docs]classParquetWriter:__doc__="""Class for incrementally building a Parquet file for Arrow tables.Parameters----------where : path or file-like objectschema : 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--------{}""".format(_parquet_writer_arg_docs,_parquet_writer_example_doc)
[docs]def__init__(self,where,schema,filesystem=None,flavor=None,version='2.4',use_dictionary=True,compression='snappy',write_statistics=True,use_deprecated_int96_timestamps=None,compression_level=None,use_byte_stream_split=False,column_encoding=None,writer_engine_version=None,data_page_version='1.0',use_compliant_nested_type=False,encryption_properties=None,write_batch_size=None,dictionary_pagesize_limit=None,store_schema=True,**options):ifuse_deprecated_int96_timestampsisNone:# Use int96 timestamps for SparkifflavorisnotNoneand'spark'inflavor:use_deprecated_int96_timestamps=Trueelse:use_deprecated_int96_timestamps=Falseself.flavor=flavorifflavorisnotNone:schema,self.schema_changed=_sanitize_schema(schema,flavor)else:self.schema_changed=Falseself.schema=schemaself.where=where# If we open a file using a filesystem, store file handle so we can be# sure to close it when `self.close` is called.self.file_handle=Nonefilesystem,path=_resolve_filesystem_and_path(where,filesystem,allow_legacy_filesystem=True)iffilesystemisnotNone:ifisinstance(filesystem,legacyfs.FileSystem):# legacy filesystem (eg custom subclass)# TODO deprecatesink=self.file_handle=filesystem.open(path,'wb')else:# ARROW-10480: do not auto-detect compression. While# a filename like foo.parquet.gz is nonconforming, it# shouldn't implicitly apply compression.sink=self.file_handle=filesystem.open_output_stream(path,compression=None)else:sink=whereself._metadata_collector=options.pop('metadata_collector',None)engine_version='V2'self.writer=_parquet.ParquetWriter(sink,schema,version=version,compression=compression,use_dictionary=use_dictionary,write_statistics=write_statistics,use_deprecated_int96_timestamps=use_deprecated_int96_timestamps,compression_level=compression_level,use_byte_stream_split=use_byte_stream_split,column_encoding=column_encoding,writer_engine_version=engine_version,data_page_version=data_page_version,use_compliant_nested_type=use_compliant_nested_type,encryption_properties=encryption_properties,write_batch_size=write_batch_size,dictionary_pagesize_limit=dictionary_pagesize_limit,store_schema=store_schema,**options)self.is_open=True
def__del__(self):ifgetattr(self,'is_open',False):self.close()def__enter__(self):returnselfdef__exit__(self,*args,**kwargs):self.close()# return false since we want to propagate exceptionsreturnFalse
[docs]defwrite(self,table_or_batch,row_group_size=None):""" 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. """ifisinstance(table_or_batch,pa.RecordBatch):self.write_batch(table_or_batch,row_group_size)elifisinstance(table_or_batch,pa.Table):self.write_table(table_or_batch,row_group_size)else:raiseTypeError(type(table_or_batch))
[docs]defwrite_batch(self,batch,row_group_size=None):""" 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. """table=pa.Table.from_batches([batch],batch.schema)self.write_table(table,row_group_size)
[docs]defwrite_table(self,table,row_group_size=None):""" 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. """ifself.schema_changed:table=_sanitize_table(table,self.schema,self.flavor)assertself.is_openifnottable.schema.equals(self.schema,check_metadata=False):msg=('Table schema does not match schema used to create file: ''\ntable:\n{!s} vs. \nfile:\n{!s}'.format(table.schema,self.schema))raiseValueError(msg)self.writer.write_table(table,row_group_size=row_group_size)
[docs]defclose(self):""" Close the connection to the Parquet file. """ifself.is_open:self.writer.close()self.is_open=Falseifself._metadata_collectorisnotNone:self._metadata_collector.append(self.writer.metadata)ifself.file_handleisnotNone:self.file_handle.close()
def_get_pandas_index_columns(keyvalues):return(json.loads(keyvalues[b'pandas'].decode('utf8'))['index_columns'])# ----------------------------------------------------------------------# Metadata container providing instructions about reading a single Parquet# file, possibly part of a partitioned datasetclassParquetDatasetPiece:""" DEPRECATED: A single chunk of a potentially larger Parquet dataset to read. The arguments will indicate to read either a single row group or all row groups, and whether to add partition keys to the resulting pyarrow.Table. .. deprecated:: 5.0 Directly constructing a ``ParquetDatasetPiece`` is deprecated, as well as accessing the pieces of a ``ParquetDataset`` object. Specify ``use_legacy_dataset=False`` when constructing the ``ParquetDataset`` and use the ``ParquetDataset.fragments`` attribute instead. Parameters ---------- path : str or pathlib.Path Path to file in the file system where this piece is located. open_file_func : callable Function to use for obtaining file handle to dataset piece. file_options : dict Options row_group : int, default None Row group to load. By default, reads all row groups. partition_keys : list of tuples Two-element tuples of ``(column name, ordinal index)``. """def__init__(self,path,open_file_func=partial(open,mode='rb'),file_options=None,row_group=None,partition_keys=None):warnings.warn("ParquetDatasetPiece is deprecated as of pyarrow 5.0.0 and will ""be removed in a future version.",FutureWarning,stacklevel=2)self._init(path,open_file_func,file_options,row_group,partition_keys)@staticmethoddef_create(path,open_file_func=partial(open,mode='rb'),file_options=None,row_group=None,partition_keys=None):self=ParquetDatasetPiece.__new__(ParquetDatasetPiece)self._init(path,open_file_func,file_options,row_group,partition_keys)returnselfdef_init(self,path,open_file_func,file_options,row_group,partition_keys):self.path=_stringify_path(path)self.open_file_func=open_file_funcself.row_group=row_groupself.partition_keys=partition_keysor[]self.file_options=file_optionsor{}def__eq__(self,other):ifnotisinstance(other,ParquetDatasetPiece):returnFalsereturn(self.path==other.pathandself.row_group==other.row_groupandself.partition_keys==other.partition_keys)def__repr__(self):return('{}({!r}, row_group={!r}, partition_keys={!r})'.format(type(self).__name__,self.path,self.row_group,self.partition_keys))def__str__(self):result=''iflen(self.partition_keys)>0:partition_str=', '.join('{}={}'.format(name,index)forname,indexinself.partition_keys)result+='partition[{}] '.format(partition_str)result+=self.pathifself.row_groupisnotNone:result+=' | row_group={}'.format(self.row_group)returnresultdefget_metadata(self):""" Return the file's metadata. Returns ------- metadata : FileMetaData The file's metadata """withself.open()asparquet:returnparquet.metadatadefopen(self):""" Return instance of ParquetFile. """reader=self.open_file_func(self.path)ifnotisinstance(reader,ParquetFile):reader=ParquetFile(reader,**self.file_options)# ensure reader knows it's responsible for closing source# since we opened the source here internally.reader._close_source=Truereturnreaderdefread(self,columns=None,use_threads=True,partitions=None,file=None,use_pandas_metadata=False):""" Read this piece as a pyarrow.Table. Parameters ---------- columns : list of column names, default None use_threads : bool, default True Perform multi-threaded column reads. partitions : ParquetPartitions, default None file : file-like object Passed to ParquetFile. use_pandas_metadata : bool If pandas metadata should be used or not. Returns ------- table : pyarrow.Table The piece as a pyarrow.Table. """ifself.open_file_funcisnotNone:reader=self.open()eliffileisnotNone:reader=ParquetFile(file,**self.file_options)else:# try to read the local pathreader=ParquetFile(self.path,**self.file_options)options=dict(columns=columns,use_threads=use_threads,use_pandas_metadata=use_pandas_metadata)ifself.row_groupisnotNone:table=reader.read_row_group(self.row_group,**options)else:table=reader.read(**options)iflen(self.partition_keys)>0:ifpartitionsisNone:raiseValueError('Must pass partition sets')# Here, the index is the categorical code of the partition where# this piece is located. Suppose we had## /foo=a/0.parq# /foo=b/0.parq# /foo=c/0.parq## Then we assign a=0, b=1, c=2. And the resulting Table pieces will# have a DictionaryArray column named foo having the constant index# value as indicated. The distinct categories of the partition have# been computed in the ParquetManifestfori,(name,index)inenumerate(self.partition_keys):# The partition code is the same for all values in this pieceindices=np.full(len(table),index,dtype='i4')# This is set of all partition values, computed as part of the# manifest, so ['a', 'b', 'c'] as in our example above.dictionary=partitions.levels[i].dictionaryarr=pa.DictionaryArray.from_arrays(indices,dictionary)table=table.append_column(name,arr)# To ParquetFile the source looked like it was already open, so won't# actually close it without overriding.reader.close(force=True)returntableclassPartitionSet:""" A data structure for cataloguing the observed Parquet partitions at a particular level. So if we have /foo=a/bar=0 /foo=a/bar=1 /foo=a/bar=2 /foo=b/bar=0 /foo=b/bar=1 /foo=b/bar=2 Then we have two partition sets, one for foo, another for bar. As we visit levels of the partition hierarchy, a PartitionSet tracks the distinct values and assigns categorical codes to use when reading the pieces Parameters ---------- name : str Name of the partition set. Under which key to collect all values. keys : list All possible values that have been collected for that partition set. """def__init__(self,name,keys=None):self.name=nameself.keys=keysor[]self.key_indices={k:ifori,kinenumerate(self.keys)}self._dictionary=Nonedefget_index(self,key):""" Get the index of the partition value if it is known, otherwise assign one Parameters ---------- key : str or int The value for which we want to known the index. """ifkeyinself.key_indices:returnself.key_indices[key]else:index=len(self.key_indices)self.keys.append(key)self.key_indices[key]=indexreturnindex@propertydefdictionary(self):ifself._dictionaryisnotNone:returnself._dictionaryiflen(self.keys)==0:raiseValueError('No known partition keys')# Only integer and string partition types are supported right nowtry:integer_keys=[int(x)forxinself.keys]dictionary=lib.array(integer_keys)exceptValueError:dictionary=lib.array(self.keys)self._dictionary=dictionaryreturndictionary@propertydefis_sorted(self):returnlist(self.keys)==sorted(self.keys)classParquetPartitions:def__init__(self):self.levels=[]self.partition_names=set()def__len__(self):returnlen(self.levels)def__getitem__(self,i):returnself.levels[i]defequals(self,other):ifnotisinstance(other,ParquetPartitions):raiseTypeError('`other` must be an instance of ParquetPartitions')return(self.levels==other.levelsandself.partition_names==other.partition_names)def__eq__(self,other):try:returnself.equals(other)exceptTypeError:returnNotImplementeddefget_index(self,level,name,key):""" Record a partition value at a particular level, returning the distinct code for that value at that level. Examples -------- partitions.get_index(1, 'foo', 'a') returns 0 partitions.get_index(1, 'foo', 'b') returns 1 partitions.get_index(1, 'foo', 'c') returns 2 partitions.get_index(1, 'foo', 'a') returns 0 Parameters ---------- level : int The nesting level of the partition we are observing name : str The partition name key : str or int The partition value """iflevel==len(self.levels):ifnameinself.partition_names:raiseValueError('{} was the name of the partition in ''another level'.format(name))part_set=PartitionSet(name)self.levels.append(part_set)self.partition_names.add(name)returnself.levels[level].get_index(key)deffilter_accepts_partition(self,part_key,filter,level):p_column,p_value_index=part_keyf_column,op,f_value=filterifp_column!=f_column:returnTruef_type=type(f_value)ifopin{'in','not in'}:ifnotisinstance(f_value,Collection):raiseTypeError("'%s' object is not a collection",f_type.__name__)ifnotf_value:raiseValueError("Cannot use empty collection as filter value")iflen({type(item)foriteminf_value})!=1:raiseValueError("All elements of the collection '%s' must be"" of same type",f_value)f_type=type(next(iter(f_value)))elifnotisinstance(f_value,str)andisinstance(f_value,Collection):raiseValueError("Op '%s' not supported with a collection value",op)p_value=f_type(self.levels[level].dictionary[p_value_index].as_py())ifop=="="orop=="==":returnp_value==f_valueelifop=="!=":returnp_value!=f_valueelifop=='<':returnp_value<f_valueelifop=='>':returnp_value>f_valueelifop=='<=':returnp_value<=f_valueelifop=='>=':returnp_value>=f_valueelifop=='in':returnp_valueinf_valueelifop=='not in':returnp_valuenotinf_valueelse:raiseValueError("'%s' is not a valid operator in predicates.",filter[1])classParquetManifest:def__init__(self,dirpath,open_file_func=None,filesystem=None,pathsep='/',partition_scheme='hive',metadata_nthreads=1):filesystem,dirpath=_get_filesystem_and_path(filesystem,dirpath)self.filesystem=filesystemself.open_file_func=open_file_funcself.pathsep=pathsepself.dirpath=_stringify_path(dirpath)self.partition_scheme=partition_schemeself.partitions=ParquetPartitions()self.pieces=[]self._metadata_nthreads=metadata_nthreadsself._thread_pool=futures.ThreadPoolExecutor(max_workers=metadata_nthreads)self.common_metadata_path=Noneself.metadata_path=Noneself._visit_level(0,self.dirpath,[])# Due to concurrency, pieces will potentially by out of order if the# dataset is partitioned so we sort them to yield stable resultsself.pieces.sort(key=lambdapiece:piece.path)ifself.common_metadata_pathisNone:# _common_metadata is a subset of _metadataself.common_metadata_path=self.metadata_pathself._thread_pool.shutdown()def_visit_level(self,level,base_path,part_keys):fs=self.filesystem_,directories,files=next(fs.walk(base_path))filtered_files=[]forpathinfiles:full_path=self.pathsep.join((base_path,path))ifpath.endswith('_common_metadata'):self.common_metadata_path=full_pathelifpath.endswith('_metadata'):self.metadata_path=full_pathelifself._should_silently_exclude(path):continueelse:filtered_files.append(full_path)# ARROW-1079: Filter out "private" directories starting with underscorefiltered_directories=[self.pathsep.join((base_path,x))forxindirectoriesifnot_is_private_directory(x)]filtered_files.sort()filtered_directories.sort()iflen(filtered_files)>0andlen(filtered_directories)>0:raiseValueError('Found files in an intermediate ''directory: {}'.format(base_path))eliflen(filtered_directories)>0:self._visit_directories(level,filtered_directories,part_keys)else:self._push_pieces(filtered_files,part_keys)def_should_silently_exclude(self,file_name):return(file_name.endswith('.crc')or# Checksumsfile_name.endswith('_$folder$')or# HDFS directories in S3file_name.startswith('.')or# Hidden files starting with .file_name.startswith('_')or# Hidden files starting with _file_nameinEXCLUDED_PARQUET_PATHS)def_visit_directories(self,level,directories,part_keys):futures_list=[]forpathindirectories:head,tail=_path_split(path,self.pathsep)name,key=_parse_hive_partition(tail)index=self.partitions.get_index(level,name,key)dir_part_keys=part_keys+[(name,index)]# If you have less threads than levels, the wait call will block# indefinitely due to multiple waits within a thread.iflevel<self._metadata_nthreads:future=self._thread_pool.submit(self._visit_level,level+1,path,dir_part_keys)futures_list.append(future)else:self._visit_level(level+1,path,dir_part_keys)iffutures_list:futures.wait(futures_list)def_parse_partition(self,dirname):ifself.partition_scheme=='hive':return_parse_hive_partition(dirname)else:raiseNotImplementedError('partition schema: {}'.format(self.partition_scheme))def_push_pieces(self,files,part_keys):self.pieces.extend([ParquetDatasetPiece._create(path,partition_keys=part_keys,open_file_func=self.open_file_func)forpathinfiles])def_parse_hive_partition(value):if'='notinvalue:raiseValueError('Directory name did not appear to be a ''partition: {}'.format(value))returnvalue.split('=',1)def_is_private_directory(x):_,tail=os.path.split(x)return(tail.startswith('_')ortail.startswith('.'))and'='notintaildef_path_split(path,sep):i=path.rfind(sep)+1head,tail=path[:i],path[i:]head=head.rstrip(sep)returnhead,tailEXCLUDED_PARQUET_PATHS={'_SUCCESS'}class_ParquetDatasetMetadata:__slots__=('fs','memory_map','read_dictionary','common_metadata','buffer_size')def_open_dataset_file(dataset,path,meta=None):if(dataset.fsisnotNoneandnotisinstance(dataset.fs,legacyfs.LocalFileSystem)):path=dataset.fs.open(path,mode='rb')returnParquetFile(path,metadata=meta,memory_map=dataset.memory_map,read_dictionary=dataset.read_dictionary,common_metadata=dataset.common_metadata,buffer_size=dataset.buffer_size)_DEPR_MSG=("'{}' attribute is deprecated as of pyarrow 5.0.0 and will be removed ""in a future version.{}")_read_docstring_common="""\read_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."""_parquet_dataset_example="""\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',... partition_cols=['year'],... use_legacy_dataset=False)create a ParquetDataset object from the dataset source:>>> dataset = pq.ParquetDataset('dataset_name/', use_legacy_dataset=False)and read the data:>>> dataset.read().to_pandas() n_legs animal year0 5 Brittle stars 20191 2 Flamingo 20202 4 Dog 20213 100 Centipede 20214 2 Parrot 20225 4 Horse 2022create a ParquetDataset object with filter:>>> dataset = pq.ParquetDataset('dataset_name/', use_legacy_dataset=False,... filters=[('n_legs','=',4)])>>> dataset.read().to_pandas() n_legs animal year0 4 Dog 20211 4 Horse 2022"""
[docs]classParquetDataset:__doc__="""Encapsulates details of reading a complete Parquet dataset possiblyconsisting 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 Use schema obtained elsewhere to validate file schemas. Alternative to metadata parameter.metadata : pyarrow.parquet.FileMetaData Use metadata obtained elsewhere to validate file schemas.split_row_groups : bool, default False Divide files into pieces for each row group in the file.validate_schema : bool, default True Check that individual file schemas are all the same / compatible.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. If `use_legacy_dataset` is True, filters can only reference partition keys and only a hive-style directory structure is supported. When setting `use_legacy_dataset` to False, also within-file level filtering and different partitioning schemes are supported.{1}metadata_nthreads : int, default 1 How many threads to allow the thread pool which is used to read the dataset metadata. Increasing this is helpful to read partitioned datasets.{0}use_legacy_dataset : bool, default False Set to False to enable the new code path (using the new Arrow Dataset API). Among other things, this allows to pass `filters` for all columns and not only the partition keys, enables different partitioning schemes, etc.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. This option is only supported for use_legacy_dataset=False. 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.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.Examples--------{2}""".format(_read_docstring_common,_DNF_filter_doc,_parquet_dataset_example)def__new__(cls,path_or_paths=None,filesystem=None,schema=None,metadata=None,split_row_groups=False,validate_schema=True,filters=None,metadata_nthreads=None,read_dictionary=None,memory_map=False,buffer_size=0,partitioning="hive",use_legacy_dataset=None,pre_buffer=True,coerce_int96_timestamp_unit=None,thrift_string_size_limit=None,thrift_container_size_limit=None):extra_msg=""ifuse_legacy_datasetisNone:# if an old filesystem is passed -> still use to old implementationifisinstance(filesystem,legacyfs.FileSystem):use_legacy_dataset=Trueextra_msg=(" The legacy behaviour was still chosen because a ""deprecated 'pyarrow.filesystem' filesystem was specified ""(use the filesystems from pyarrow.fs instead).")# otherwise the default is already Falseelse:use_legacy_dataset=Falseifnotuse_legacy_dataset:return_ParquetDatasetV2(path_or_paths,filesystem=filesystem,filters=filters,partitioning=partitioning,read_dictionary=read_dictionary,memory_map=memory_map,buffer_size=buffer_size,pre_buffer=pre_buffer,coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,# unsupported keywordsschema=schema,metadata=metadata,split_row_groups=split_row_groups,validate_schema=validate_schema,metadata_nthreads=metadata_nthreads,thrift_string_size_limit=thrift_string_size_limit,thrift_container_size_limit=thrift_container_size_limit,)warnings.warn("Passing 'use_legacy_dataset=True' to get the legacy behaviour is ""deprecated as of pyarrow 11.0.0, and the legacy implementation ""will be removed in a future version."+extra_msg,FutureWarning,stacklevel=2)self=object.__new__(cls)returnself
[docs]def__init__(self,path_or_paths,filesystem=None,schema=None,metadata=None,split_row_groups=False,validate_schema=True,filters=None,metadata_nthreads=None,read_dictionary=None,memory_map=False,buffer_size=0,partitioning="hive",use_legacy_dataset=None,pre_buffer=True,coerce_int96_timestamp_unit=None,thrift_string_size_limit=None,thrift_container_size_limit=None):ifpartitioning!="hive":raiseValueError('Only "hive" for hive-like partitioning is supported when ''using use_legacy_dataset=True')ifmetadata_nthreadsisnotNone:warnings.warn("Specifying the 'metadata_nthreads' argument is deprecated as ""of pyarrow 8.0.0, and the argument will be removed in a ""future version",FutureWarning,stacklevel=2,)else:metadata_nthreads=1self._ds_metadata=_ParquetDatasetMetadata()a_path=path_or_pathsifisinstance(a_path,list):a_path=a_path[0]self._ds_metadata.fs,_=_get_filesystem_and_path(filesystem,a_path)ifisinstance(path_or_paths,list):self.paths=[_parse_uri(path)forpathinpath_or_paths]else:self.paths=_parse_uri(path_or_paths)self._ds_metadata.read_dictionary=read_dictionaryself._ds_metadata.memory_map=memory_mapself._ds_metadata.buffer_size=buffer_size(self._pieces,self._partitions,self._common_metadata_path,self._metadata_path)=_make_manifest(path_or_paths,self._fs,metadata_nthreads=metadata_nthreads,open_file_func=partial(_open_dataset_file,self._ds_metadata))ifself._common_metadata_pathisnotNone:withself._fs.open(self._common_metadata_path)asf:self._ds_metadata.common_metadata=read_metadata(f,memory_map=memory_map)else:self._ds_metadata.common_metadata=NoneifmetadataisnotNone:warnings.warn("Specifying the 'metadata' argument with 'use_legacy_dataset=""True' is deprecated as of pyarrow 8.0.0.",FutureWarning,stacklevel=2)ifmetadataisNoneandself._metadata_pathisnotNone:withself._fs.open(self._metadata_path)asf:self._metadata=read_metadata(f,memory_map=memory_map)else:self._metadata=metadataifschemaisnotNone:warnings.warn("Specifying the 'schema' argument with 'use_legacy_dataset=""True' is deprecated as of pyarrow 8.0.0. You can still ""specify it in combination with 'use_legacy_dataet=False', ""but in that case you need to specify a pyarrow.Schema ""instead of a ParquetSchema.",FutureWarning,stacklevel=2)self._schema=schemaself.split_row_groups=split_row_groupsifsplit_row_groups:raiseNotImplementedError("split_row_groups not yet implemented")iffiltersisnotNone:ifhasattr(filters,"cast"):raiseTypeError("Expressions as filter not supported for legacy dataset")filters=_check_filters(filters)self._filter(filters)ifvalidate_schema:self.validate_schemas()
def__getnewargs_ex__(self):# when creating a new instance while unpickling, force to use the# legacy code path to create a ParquetDataset instance# instead of a _ParquetDatasetV2 instancereturn((),dict(use_legacy_dataset=True))
[docs]defequals(self,other):ifnotisinstance(other,ParquetDataset):raiseTypeError('`other` must be an instance of ParquetDataset')ifself._fs.__class__!=other._fs.__class__:returnFalseforpropin('paths','_pieces','_partitions','_common_metadata_path','_metadata_path','_common_metadata','_metadata','_schema','split_row_groups'):ifgetattr(self,prop)!=getattr(other,prop):returnFalseforpropin('memory_map','buffer_size'):if(getattr(self._ds_metadata,prop)!=getattr(other._ds_metadata,prop)):returnFalsereturnTrue
[docs]defvalidate_schemas(self):ifself._metadataisNoneandself._schemaisNone:ifself._common_metadataisnotNone:self._schema=self._common_metadata.schemaelse:self._schema=self._pieces[0].get_metadata().schemaelifself._schemaisNone:self._schema=self._metadata.schema# Verify schemas are all compatibledataset_schema=self._schema.to_arrow_schema()# Exclude the partition columns from the schema, they are provided# by the path, not the DatasetPieceifself._partitionsisnotNone:forpartition_nameinself._partitions.partition_names:ifdataset_schema.get_field_index(partition_name)!=-1:field_idx=dataset_schema.get_field_index(partition_name)dataset_schema=dataset_schema.remove(field_idx)forpieceinself._pieces:file_metadata=piece.get_metadata()file_schema=file_metadata.schema.to_arrow_schema()ifnotdataset_schema.equals(file_schema,check_metadata=False):raiseValueError('Schema in {!s} was different. \n''{!s}\n\nvs\n\n{!s}'.format(piece,file_schema,dataset_schema))
[docs]defread(self,columns=None,use_threads=True,use_pandas_metadata=False):""" Read multiple Parquet files as a single pyarrow.Table. Parameters ---------- columns : List[str] Names of columns to read from the file. use_threads : bool, default True Perform multi-threaded column reads use_pandas_metadata : bool, default False Passed through to each dataset piece. 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_name_read', ... partition_cols=['year'], ... use_legacy_dataset=False) >>> dataset = pq.ParquetDataset('dataset_name_read/', ... use_legacy_dataset=False) Read multiple Parquet files as a single pyarrow.Table: >>> dataset.read(columns=["n_legs"]) pyarrow.Table n_legs: int64 ---- n_legs: [[5],[2],[4,100],[2,4]] """tables=[]forpieceinself._pieces:table=piece.read(columns=columns,use_threads=use_threads,partitions=self._partitions,use_pandas_metadata=use_pandas_metadata)tables.append(table)all_data=lib.concat_tables(tables)ifuse_pandas_metadata:# We need to ensure that this metadata is set in the Table's schema# so that Table.to_pandas will construct pandas.DataFrame with the# right indexcommon_metadata=self._get_common_pandas_metadata()current_metadata=all_data.schema.metadataor{}ifcommon_metadataandb'pandas'notincurrent_metadata:all_data=all_data.replace_schema_metadata({b'pandas':common_metadata})returnall_data
[docs]defread_pandas(self,**kwargs):""" Read dataset including pandas metadata, if any. Other arguments passed through to ParquetDataset.read, see docstring for further details. Parameters ---------- **kwargs : optional All additional options to pass to the reader. Returns ------- pyarrow.Table Content of the file as a table (of columns). Examples -------- Generate an example PyArrow Table and write it to a partitioned dataset: >>> 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.parquet') >>> dataset = pq.ParquetDataset('table.parquet', ... use_legacy_dataset=False) Read dataset including pandas metadata: >>> dataset.read_pandas(columns=["n_legs"]) pyarrow.Table n_legs: int64 ---- n_legs: [[2,2,4,4,5,100]] Select pandas metadata: >>> dataset.read_pandas(columns=["n_legs"]).schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, ...} """returnself.read(use_pandas_metadata=True,**kwargs)
def_get_common_pandas_metadata(self):ifself._common_metadataisNone:returnNonekeyvalues=self._common_metadata.metadatareturnkeyvalues.get(b'pandas',None)def_filter(self,filters):accepts_filter=self._partitions.filter_accepts_partitiondefone_filter_accepts(piece,filter):returnall(accepts_filter(part_key,filter,level)forlevel,part_keyinenumerate(piece.partition_keys))defall_filters_accept(piece):returnany(all(one_filter_accepts(piece,f)forfinconjunction)forconjunctioninfilters)self._pieces=[pforpinself._piecesifall_filters_accept(p)]@propertydefpieces(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.pieces"," Specify 'use_legacy_dataset=False' while constructing the ""ParquetDataset, and then use the '.fragments' attribute ""instead."),FutureWarning,stacklevel=2)returnself._pieces@propertydefpartitions(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.partitions"," Specify 'use_legacy_dataset=False' while constructing the ""ParquetDataset, and then use the '.partitioning' attribute ""instead."),FutureWarning,stacklevel=2)returnself._partitions@propertydefschema(self):warnings.warn(_DEPR_MSG.format("ParquetDataset.schema"," Specify 'use_legacy_dataset=False' while constructing the ""ParquetDataset, and then use the '.schema' attribute ""instead (which will return an Arrow schema instead of a ""Parquet schema)."),FutureWarning,stacklevel=2)returnself._schema@propertydefmemory_map(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.memory_map",""),FutureWarning,stacklevel=2)returnself._ds_metadata.memory_map@propertydefread_dictionary(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.read_dictionary",""),FutureWarning,stacklevel=2)returnself._ds_metadata.read_dictionary@propertydefbuffer_size(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.buffer_size",""),FutureWarning,stacklevel=2)returnself._ds_metadata.buffer_size_fs=property(operator.attrgetter('_ds_metadata.fs'))@propertydeffs(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.fs"," Specify 'use_legacy_dataset=False' while constructing the ""ParquetDataset, and then use the '.filesystem' attribute ""instead."),FutureWarning,stacklevel=2)returnself._ds_metadata.fs@propertydefmetadata(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.metadata",""),FutureWarning,stacklevel=2)returnself._metadata@propertydefmetadata_path(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.metadata_path",""),FutureWarning,stacklevel=2)returnself._metadata_path@propertydefcommon_metadata_path(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.common_metadata_path",""),FutureWarning,stacklevel=2)returnself._common_metadata_path_common_metadata=property(operator.attrgetter('_ds_metadata.common_metadata'))@propertydefcommon_metadata(self):""" DEPRECATED """warnings.warn(_DEPR_MSG.format("ParquetDataset.common_metadata",""),FutureWarning,stacklevel=2)returnself._ds_metadata.common_metadata@propertydeffragments(self):""" A list of the Dataset source fragments or pieces with absolute file paths. To use this property set 'use_legacy_dataset=False' while constructing ParquetDataset object. 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_name_fragments', ... partition_cols=['year'], ... use_legacy_dataset=False) >>> dataset = pq.ParquetDataset('dataset_name_fragments/', ... use_legacy_dataset=False) List the fragments: >>> dataset.fragments [<pyarrow.dataset.ParquetFileFragment path=dataset_name_fragments/... """raiseNotImplementedError("To use this property set 'use_legacy_dataset=False' while ""constructing the ParquetDataset")@propertydeffiles(self):""" A list of absolute Parquet file paths in the Dataset source. To use this property set 'use_legacy_dataset=False' while constructing ParquetDataset object. 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_name_files', ... partition_cols=['year'], ... use_legacy_dataset=False) >>> dataset = pq.ParquetDataset('dataset_name_files/', ... use_legacy_dataset=False) List the files: >>> dataset.files ['dataset_name_files/year=2019/...-0.parquet', ... """raiseNotImplementedError("To use this property set 'use_legacy_dataset=False' while ""constructing the ParquetDataset")@propertydeffilesystem(self):""" The filesystem type of the Dataset source. To use this property set 'use_legacy_dataset=False' while constructing ParquetDataset object. """raiseNotImplementedError("To use this property set 'use_legacy_dataset=False' while ""constructing the ParquetDataset")@propertydefpartitioning(self):""" The partitioning of the Dataset source, if discovered. To use this property set 'use_legacy_dataset=False' while constructing ParquetDataset object. """raiseNotImplementedError("To use this property set 'use_legacy_dataset=False' while ""constructing the ParquetDataset")
def_make_manifest(path_or_paths,fs,pathsep='/',metadata_nthreads=1,open_file_func=None):partitions=Nonecommon_metadata_path=Nonemetadata_path=Noneifisinstance(path_or_paths,list)andlen(path_or_paths)==1:# Dask passes a directory as a list of length 1path_or_paths=path_or_paths[0]if_is_path_like(path_or_paths)andfs.isdir(path_or_paths):manifest=ParquetManifest(path_or_paths,filesystem=fs,open_file_func=open_file_func,pathsep=getattr(fs,"pathsep","/"),metadata_nthreads=metadata_nthreads)common_metadata_path=manifest.common_metadata_pathmetadata_path=manifest.metadata_pathpieces=manifest.piecespartitions=manifest.partitionselse:ifnotisinstance(path_or_paths,list):path_or_paths=[path_or_paths]# List of pathsiflen(path_or_paths)==0:raiseValueError('Must pass at least one file path')pieces=[]forpathinpath_or_paths:ifnotfs.isfile(path):raiseOSError('Passed non-file path: {}'.format(path))piece=ParquetDatasetPiece._create(path,open_file_func=open_file_func)pieces.append(piece)returnpieces,partitions,common_metadata_path,metadata_pathdef_is_local_file_system(fs):returnisinstance(fs,LocalFileSystem)orisinstance(fs,legacyfs.LocalFileSystem)class_ParquetDatasetV2:""" ParquetDataset shim using the Dataset API under the hood. 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_v2', ... partition_cols=['year'], ... use_legacy_dataset=False) create a ParquetDataset object from the dataset source: >>> dataset = pq.ParquetDataset('dataset_v2/', use_legacy_dataset=False) 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)], ... use_legacy_dataset=False) >>> dataset.read().to_pandas() n_legs animal year 0 4 Dog 2021 1 4 Horse 2022 """def__init__(self,path_or_paths,filesystem=None,*,filters=None,partitioning="hive",read_dictionary=None,buffer_size=None,memory_map=False,ignore_prefixes=None,pre_buffer=True,coerce_int96_timestamp_unit=None,schema=None,decryption_properties=None,thrift_string_size_limit=None,thrift_container_size_limit=None,**kwargs):importpyarrow.datasetasds# Raise error for not supported keywordsforkeyword,defaultin[("metadata",None),("split_row_groups",False),("validate_schema",True),("metadata_nthreads",None)]:ifkeywordinkwargsandkwargs[keyword]isnotdefault:raiseValueError("Keyword '{0}' is not yet supported with the new ""Dataset API".format(keyword))# map format argumentsread_options={"pre_buffer":pre_buffer,"coerce_int96_timestamp_unit":coerce_int96_timestamp_unit,"thrift_string_size_limit":thrift_string_size_limit,"thrift_container_size_limit":thrift_container_size_limit,}ifbuffer_size:read_options.update(use_buffered_stream=True,buffer_size=buffer_size)ifread_dictionaryisnotNone:read_options.update(dictionary_columns=read_dictionary)ifdecryption_propertiesisnotNone:read_options.update(decryption_properties=decryption_properties)self._filter_expression=NoneiffiltersisnotNone:self._filter_expression=filters_to_expression(filters)# map old filesystems to new oneiffilesystemisnotNone:filesystem=_ensure_filesystem(filesystem,use_mmap=memory_map)eliffilesystemisNoneandmemory_map:# if memory_map is specified, assume local file system (string# path can in principle be URI for any filesystem)filesystem=LocalFileSystem(use_mmap=memory_map)# This needs to be checked after _ensure_filesystem, because that# handles the case of an fsspec LocalFileSystemif(hasattr(path_or_paths,"__fspath__")andfilesystemisnotNoneandnot_is_local_file_system(filesystem)):raiseTypeError("Path-like objects with __fspath__ must only be used with "f"local file systems, not {type(filesystem)}")# check for single fragment datasetsingle_file=Noneself._base_dir=Noneifnotisinstance(path_or_paths,list):if_is_path_like(path_or_paths):path_or_paths=_stringify_path(path_or_paths)iffilesystemisNone:# path might be a URI describing the FileSystem as welltry:filesystem,path_or_paths=FileSystem.from_uri(path_or_paths)exceptValueError:filesystem=LocalFileSystem(use_mmap=memory_map)finfo=filesystem.get_file_info(path_or_paths)iffinfo.is_file:single_file=path_or_pathsiffinfo.type==FileType.Directory:self._base_dir=path_or_pathselse:single_file=path_or_pathsparquet_format=ds.ParquetFileFormat(**read_options)ifsingle_fileisnotNone:fragment=parquet_format.make_fragment(single_file,filesystem)self._dataset=ds.FileSystemDataset([fragment],schema=schemaorfragment.physical_schema,format=parquet_format,filesystem=fragment.filesystem)return# check partitioning to enable dictionary encodingifpartitioning=="hive":partitioning=ds.HivePartitioning.discover(infer_dictionary=True)self._dataset=ds.dataset(path_or_paths,filesystem=filesystem,schema=schema,format=parquet_format,partitioning=partitioning,ignore_prefixes=ignore_prefixes)defequals(self,other):ifisinstance(other,ParquetDataset):raiseTypeError("`other` must be an instance of ParquetDataset constructed ""with `use_legacy_dataset=False`")ifnotisinstance(other,_ParquetDatasetV2):raiseTypeError('`other` must be an instance of ParquetDataset')return(self.schema==other.schemaandself._dataset.format==other._dataset.formatandself.filesystem==other.filesystemand# self.fragments == other.fragments andself.files==other.files)def__eq__(self,other):try:returnself.equals(other)exceptTypeError:returnNotImplemented@propertydefschema(self):""" 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'], ... use_legacy_dataset=False) >>> dataset = pq.ParquetDataset('dataset_v2_schema/', ... use_legacy_dataset=False) Read the schema: >>> dataset.schema n_legs: int64 animal: string year: dictionary<values=int32, indices=int32, ordered=0> """returnself._dataset.schemadefread(self,columns=None,use_threads=True,use_pandas_metadata=False):""" 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 (in contrast to when setting ``use_legacy_dataset=True``). 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'], ... use_legacy_dataset=False) >>> dataset = pq.ParquetDataset('dataset_v2_read/', ... use_legacy_dataset=False) Read the dataset: >>> dataset.read(columns=["n_legs"]) pyarrow.Table n_legs: int64 ---- n_legs: [[5],[2],[4,100],[2,4]] """# if use_pandas_metadata, we need to include index columns in the# column selection, to be able to restore those in the pandas DataFramemetadata=self.schema.metadataor{}ifuse_pandas_metadata:# if the dataset schema metadata itself doesn't have pandas# then try to get this from common file (for backwards compat)ifb"pandas"notinmetadata:common_metadata=self._get_common_pandas_metadata()ifcommon_metadata:metadata=common_metadataifcolumnsisnotNoneanduse_pandas_metadata:ifmetadataandb'pandas'inmetadata:# RangeIndex can be represented as dict instead of column nameindex_columns=[colforcolin_get_pandas_index_columns(metadata)ifnotisinstance(col,dict)]columns=(list(columns)+list(set(index_columns)-set(columns)))table=self._dataset.to_table(columns=columns,filter=self._filter_expression,use_threads=use_threads)# if use_pandas_metadata, restore the pandas metadata (which gets# lost if doing a specific `columns` selection in to_table)ifuse_pandas_metadata:ifmetadataandb"pandas"inmetadata:new_metadata=table.schema.metadataor{}new_metadata.update({b"pandas":metadata[b"pandas"]})table=table.replace_schema_metadata(new_metadata)returntabledef_get_common_pandas_metadata(self):ifnotself._base_dir:returnNonemetadata=Nonefornamein["_common_metadata","_metadata"]:metadata_path=os.path.join(str(self._base_dir),name)finfo=self.filesystem.get_file_info(metadata_path)iffinfo.is_file:pq_meta=read_metadata(metadata_path,filesystem=self.filesystem)metadata=pq_meta.metadataifmetadataandb'pandas'inmetadata:breakreturnmetadatadefread_pandas(self,**kwargs):""" Read dataset including pandas metadata, if any. Other arguments passed through to ParquetDataset.read, see docstring for further details. Examples -------- Generate an example parquet file: >>> 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', ... use_legacy_dataset=False) 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, ...} """returnself.read(use_pandas_metadata=True,**kwargs)@propertydefpieces(self):warnings.warn(_DEPR_MSG.format("ParquetDataset.pieces"," Use the '.fragments' attribute instead"),FutureWarning,stacklevel=2)returnlist(self._dataset.get_fragments())@propertydeffragments(self):""" A list of the Dataset source fragments or pieces with absolute file paths. 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_fragments', ... partition_cols=['year'], ... use_legacy_dataset=False) >>> dataset = pq.ParquetDataset('dataset_v2_fragments/', ... use_legacy_dataset=False) List the fragments: >>> dataset.fragments [<pyarrow.dataset.ParquetFileFragment path=dataset_v2_fragments/... """returnlist(self._dataset.get_fragments())@propertydeffiles(self):""" A list of absolute Parquet file paths in the Dataset source. 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_files', ... partition_cols=['year'], ... use_legacy_dataset=False) >>> dataset = pq.ParquetDataset('dataset_v2_files/', ... use_legacy_dataset=False) List the files: >>> dataset.files ['dataset_v2_files/year=2019/...-0.parquet', ... """returnself._dataset.files@propertydeffilesystem(self):""" The filesystem type of the Dataset source. """returnself._dataset.filesystem@propertydefpartitioning(self):""" The partitioning of the Dataset source, if discovered. """returnself._dataset.partitioning_read_table_docstring="""{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.metadata : FileMetaData If separately computedschema : 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. If `use_legacy_dataset` is True, filters can only reference partition keys and only a hive-style directory structure is supported. When setting `use_legacy_dataset` to False, also within-file level filtering and different partitioning schemes are supported.{3}use_legacy_dataset : bool, default False By default, `read_table` uses the new Arrow Datasets API since pyarrow 1.0.0. Among other things, this allows to pass `filters` for all columns and not only the partition keys, enables different partitioning schemes, etc. Set to True to use the legacy behaviour (this option is deprecated, and the legacy implementation will be removed in a future version).ignore_prefixes : list, optional Files matching any of these prefixes will be ignored by the discovery process if use_legacy_dataset=False. 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. This option is only supported for use_legacy_dataset=False. 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.Returns-------{2}{4}"""_read_table_example="""\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 year0 5 Brittle stars 20191 2 Flamingo 20202 4 Dog 20213 100 Centipede 20214 2 Parrot 20225 4 Horse 2022Read only a subset of columns:>>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"])pyarrow.Tablen_legs: int64animal: 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.Tablen_legs: int64animal: dictionary<values=string, indices=int32, ordered=0>----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 animal0 2 Flamingo1 2 ParrotRead data from a single Parquet file:>>> pq.write_table(table, 'example.parquet')>>> pq.read_table('dataset_name_2').to_pandas() n_legs animal year0 5 Brittle stars 20191 2 Flamingo 20202 4 Dog 20213 100 Centipede 20214 2 Parrot 20225 4 Horse 2022"""
[docs]defread_table(source,*,columns=None,use_threads=True,metadata=None,schema=None,use_pandas_metadata=False,read_dictionary=None,memory_map=False,buffer_size=0,partitioning="hive",filesystem=None,filters=None,use_legacy_dataset=False,ignore_prefixes=None,pre_buffer=True,coerce_int96_timestamp_unit=None,decryption_properties=None,thrift_string_size_limit=None,thrift_container_size_limit=None):ifnotuse_legacy_dataset:ifmetadataisnotNone:raiseValueError("The 'metadata' keyword is no longer supported with the new ""datasets-based implementation. Specify ""'use_legacy_dataset=True' to temporarily recover the old ""behaviour.")try:dataset=_ParquetDatasetV2(source,schema=schema,filesystem=filesystem,partitioning=partitioning,memory_map=memory_map,read_dictionary=read_dictionary,buffer_size=buffer_size,filters=filters,ignore_prefixes=ignore_prefixes,pre_buffer=pre_buffer,coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,thrift_string_size_limit=thrift_string_size_limit,thrift_container_size_limit=thrift_container_size_limit,)exceptImportError:# fall back on ParquetFile for simple cases when pyarrow.dataset# module is not availableiffiltersisnotNone:raiseValueError("the 'filters' keyword is not supported when the ""pyarrow.dataset module is not available")ifpartitioning!="hive":raiseValueError("the 'partitioning' keyword is not supported when the ""pyarrow.dataset module is not available")ifschemaisnotNone:raiseValueError("the 'schema' argument is not supported when the ""pyarrow.dataset module is not available")filesystem,path=_resolve_filesystem_and_path(source,filesystem)iffilesystemisnotNone:source=filesystem.open_input_file(path)# TODO test that source is not a directory or a listdataset=ParquetFile(source,metadata=metadata,read_dictionary=read_dictionary,memory_map=memory_map,buffer_size=buffer_size,pre_buffer=pre_buffer,coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,decryption_properties=decryption_properties,thrift_string_size_limit=thrift_string_size_limit,thrift_container_size_limit=thrift_container_size_limit,)returndataset.read(columns=columns,use_threads=use_threads,use_pandas_metadata=use_pandas_metadata)warnings.warn("Passing 'use_legacy_dataset=True' to get the legacy behaviour is ""deprecated as of pyarrow 8.0.0, and the legacy implementation will ""be removed in a future version.",FutureWarning,stacklevel=2)ifignore_prefixesisnotNone:raiseValueError("The 'ignore_prefixes' keyword is only supported when ""use_legacy_dataset=False")ifschemaisnotNone:raiseValueError("The 'schema' argument is only supported when ""use_legacy_dataset=False")if_is_path_like(source):withwarnings.catch_warnings():# Suppress second warning from ParquetDataset constructorwarnings.filterwarnings("ignore","Passing 'use_legacy_dataset",FutureWarning)pf=ParquetDataset(source,metadata=metadata,memory_map=memory_map,read_dictionary=read_dictionary,buffer_size=buffer_size,filesystem=filesystem,filters=filters,partitioning=partitioning,coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,use_legacy_dataset=True,)else:pf=ParquetFile(source,metadata=metadata,read_dictionary=read_dictionary,memory_map=memory_map,buffer_size=buffer_size,coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,decryption_properties=decryption_properties)returnpf.read(columns=columns,use_threads=use_threads,use_pandas_metadata=use_pandas_metadata)
read_table.__doc__=_read_table_docstring.format("""Read a Table from Parquet formatNote: starting with pyarrow 1.0, the default for `use_legacy_dataset` isswitched to False.""","\n".join(("""use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded.""",_read_docstring_common)),"""pyarrow.Table Content of the file as a table (of columns)""",_DNF_filter_doc,_read_table_example)
read_pandas.__doc__=_read_table_docstring.format('Read a Table from Parquet format, also reading DataFrame\n''index values if known in the file metadata',"\n".join((_read_docstring_common,"""**kwargs additional options for :func:`read_table`""")),"""pyarrow.Table Content of the file as a Table of Columns, including DataFrame indexes as columns""",_DNF_filter_doc,"")
_write_table_example="""\Generate 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)"""write_table.__doc__="""Write a Table to Parquet format.Parameters----------table : pyarrow.Tablewhere : string or pyarrow.NativeFilerow_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 ParquetWriterExamples--------{}""".format(_parquet_writer_arg_docs,_write_table_example)def_mkdir_if_not_exists(fs,path):iffs._isfilestore()andnotfs.exists(path):try:fs.mkdir(path)exceptOSError:assertfs.exists(path)
[docs]defwrite_to_dataset(table,root_path,partition_cols=None,partition_filename_cb=None,filesystem=None,use_legacy_dataset=None,schema=None,partitioning=None,basename_template=None,use_threads=None,file_visitor=None,existing_data_behavior=None,**kwargs):"""Wrapper around dataset.write_dataset (when use_legacy_dataset=False) or parquet.write_table (when use_legacy_dataset=True) 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 <uuid>.parquet group2=value2 <uuid>.parquet group1=valueN group2=value1 <uuid>.parquet group2=valueN <uuid>.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 partition_filename_cb : callable, A callback function that takes the partition key(s) as an argument and allow you to override the partition filename. If nothing is passed, the filename will consist of a uuid. This option is only supported for use_legacy_dataset=True. When use_legacy_dataset=None and this option is specified, use_legacy_datase will be set to True. 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. use_legacy_dataset : bool Default is False. Set to True to use the the legacy behaviour (this option is deprecated, and the legacy implementation will be removed in a future version). The legacy implementation still supports the `partition_filename_cb` keyword but is less efficient when using partition columns. schema : Schema, optional This option is only supported for use_legacy_dataset=False. 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. This option is only supported for use_legacy_dataset=False. 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". This option is only supported for use_legacy_dataset=False. 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. This option is only supported for use_legacy_dataset=False. 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) This option is only supported for use_legacy_dataset=False. 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. This option is only supported for use_legacy_dataset=False. **kwargs : dict, When use_legacy_dataset=False, used as additional kwargs for `dataset.write_dataset` function for matching kwargs, and remainder to `ParquetFileFormat.make_write_options`. See the docstring of `write_table` and `dataset.write_dataset` for the available options. When use_legacy_dataset=True, used as additional kwargs for `parquet.write_table` function (See docstring for `write_table` or `ParquetWriter` for more information). 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', use_legacy_dataset=False).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/', use_legacy_dataset=False).files ['dataset_name_4/...-0.parquet'] """# Choose the implementationifuse_legacy_datasetisNone:# if partition_filename_cb is specified -># default to the old implementationifpartition_filename_cb:use_legacy_dataset=True# otherwise the default is Falseelse:use_legacy_dataset=False# Check for conflicting kewordsmsg_confl_0=("The '{0}' argument is not supported by use_legacy_dataset={2}. ""Use only '{1}' instead.")msg_confl_1=("The '{1}' argument is not supported by use_legacy_dataset={2}. ""Use only '{0}' instead.")msg_confl=msg_confl_0ifuse_legacy_datasetelsemsg_confl_1ifpartition_filename_cbisnotNoneandbasename_templateisnotNone:raiseValueError(msg_confl.format("basename_template","partition_filename_cb",use_legacy_dataset))ifpartition_colsisnotNoneandpartitioningisnotNone:raiseValueError(msg_confl.format("partitioning","partition_cols",use_legacy_dataset))metadata_collector=kwargs.pop('metadata_collector',None)ifmetadata_collectorisnotNoneandfile_visitorisnotNone:raiseValueError(msg_confl.format("file_visitor","metadata_collector",use_legacy_dataset))# New dataset implementationifnotuse_legacy_dataset:importpyarrow.datasetasds# extract write_dataset specific options# reset assumed to go to make_write_optionswrite_dataset_kwargs=dict()forkeyininspect.signature(ds.write_dataset).parameters:ifkeyinkwargs:write_dataset_kwargs[key]=kwargs.pop(key)write_dataset_kwargs['max_rows_per_group']=kwargs.pop('row_group_size',kwargs.pop("chunk_size",None))# raise for unsupported keywordsmsg=("The '{}' argument is not supported with the new dataset ""implementation.")ifmetadata_collectorisnotNone:deffile_visitor(written_file):metadata_collector.append(written_file.metadata)ifpartition_filename_cbisnotNone:raiseValueError(msg.format("partition_filename_cb"))# map format argumentsparquet_format=ds.ParquetFileFormat()write_options=parquet_format.make_write_options(**kwargs)# map old filesystems to new oneiffilesystemisnotNone:filesystem=_ensure_filesystem(filesystem)ifpartition_cols:part_schema=table.select(partition_cols).schemapartitioning=ds.partitioning(part_schema,flavor="hive")ifbasename_templateisNone:basename_template=guid()+'-{i}.parquet'ifexisting_data_behaviorisNone:existing_data_behavior='overwrite_or_ignore'ds.write_dataset(table,root_path,filesystem=filesystem,format=parquet_format,file_options=write_options,schema=schema,partitioning=partitioning,use_threads=use_threads,file_visitor=file_visitor,basename_template=basename_template,existing_data_behavior=existing_data_behavior,**write_dataset_kwargs)return# warnings and errors when using legacy implementationifuse_legacy_dataset:warnings.warn("Passing 'use_legacy_dataset=True' to get the legacy behaviour is ""deprecated as of pyarrow 8.0.0, and the legacy implementation ""will be removed in a future version.",FutureWarning,stacklevel=2)msg2=("The '{}' argument is not supported with the legacy ""implementation. To use this argument specify ""'use_legacy_dataset=False' while constructing the ""ParquetDataset.")ifschemaisnotNone:raiseValueError(msg2.format("schema"))ifpartitioningisnotNone:raiseValueError(msg2.format("partitioning"))ifuse_threadsisnotNone:raiseValueError(msg2.format("use_threads"))iffile_visitorisnotNone:raiseValueError(msg2.format("file_visitor"))ifexisting_data_behaviorisnotNone:raiseValueError(msg2.format("existing_data_behavior"))ifbasename_templateisnotNone:raiseValueError(msg2.format("basename_template"))ifpartition_filename_cbisnotNone:warnings.warn(_DEPR_MSG.format("partition_filename_cb"," Specify ""'use_legacy_dataset=False' while constructing ""the ParquetDataset, and then use the ""'basename_template' parameter instead. For ""usage see `pyarrow.dataset.write_dataset`"),FutureWarning,stacklevel=2)# Legacy implementationfs,root_path=legacyfs.resolve_filesystem_and_path(root_path,filesystem)_mkdir_if_not_exists(fs,root_path)ifpartition_colsisnotNoneandlen(partition_cols)>0:df=table.to_pandas()partition_keys=[df[col]forcolinpartition_cols]data_df=df.drop(partition_cols,axis='columns')data_cols=df.columns.drop(partition_cols)iflen(data_cols)==0:raiseValueError('No data left to save outside partition columns')subschema=table.schema# ARROW-2891: Ensure the output_schema is preserved when writing a# partitioned datasetforcolintable.schema.names:ifcolinpartition_cols:subschema=subschema.remove(subschema.get_field_index(col))# ARROW-17829: avoid deprecation warnings for df.groupby# https://github.com/pandas-dev/pandas/issues/42795iflen(partition_keys)==1:partition_keys=partition_keys[0]forkeys,subgroupindata_df.groupby(partition_keys):ifnotisinstance(keys,tuple):keys=(keys,)subdir='/'.join(['{colname}={value}'.format(colname=name,value=val)forname,valinzip(partition_cols,keys)])subtable=pa.Table.from_pandas(subgroup,schema=subschema,safe=False)_mkdir_if_not_exists(fs,'/'.join([root_path,subdir]))ifpartition_filename_cb:outfile=partition_filename_cb(keys)else:outfile=guid()+'.parquet'relative_path='/'.join([subdir,outfile])full_path='/'.join([root_path,relative_path])withfs.open(full_path,'wb')asf:write_table(subtable,f,metadata_collector=metadata_collector,**kwargs)ifmetadata_collectorisnotNone:metadata_collector[-1].set_file_path(relative_path)else:ifpartition_filename_cb:outfile=partition_filename_cb(None)else:outfile=guid()+'.parquet'full_path='/'.join([root_path,outfile])withfs.open(full_path,'wb')asf:write_table(table,f,metadata_collector=metadata_collector,**kwargs)ifmetadata_collectorisnotNone:metadata_collector[-1].set_file_path(outfile)
[docs]defwrite_metadata(schema,where,metadata_collector=None,filesystem=None,**kwargs):""" 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) """filesystem,where=_resolve_filesystem_and_path(where,filesystem)ifhasattr(where,"seek"):# file-likecursor_position=where.tell()writer=ParquetWriter(where,schema,filesystem,**kwargs)writer.close()ifmetadata_collectorisnotNone:# ParquetWriter doesn't expose the metadata until it's written. Write# it and read it again.metadata=read_metadata(where,filesystem=filesystem)ifhasattr(where,"seek"):where.seek(cursor_position)# file-like, set cursor back.forminmetadata_collector:metadata.append_row_groups(m)iffilesystemisnotNone:withfilesystem.open_output_stream(where)asf:metadata.write_metadata_file(f)else:metadata.write_metadata_file(where)
[docs]defread_metadata(where,memory_map=False,decryption_properties=None,filesystem=None):""" 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') <pyarrow._parquet.FileMetaData object at ...> created_by: parquet-cpp-arrow version ... num_columns: 2 num_rows: 3 num_row_groups: 1 format_version: 2.6 serialized_size: ... """filesystem,where=_resolve_filesystem_and_path(where,filesystem)file_ctx=nullcontext()iffilesystemisnotNone:file_ctx=where=filesystem.open_input_file(where)withfile_ctx:file=ParquetFile(where,memory_map=memory_map,decryption_properties=decryption_properties)returnfile.metadata
[docs]defread_schema(where,memory_map=False,decryption_properties=None,filesystem=None):""" 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 """filesystem,where=_resolve_filesystem_and_path(where,filesystem)file_ctx=nullcontext()iffilesystemisnotNone:file_ctx=where=filesystem.open_input_file(where)withfile_ctx:file=ParquetFile(where,memory_map=memory_map,decryption_properties=decryption_properties)returnfile.schema.to_arrow_schema()
