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arrow-nightlies / pyarrow python
Repository URL to install this package:
|
Version:
22.0.0.dev96 ▾
|
pyarrow
/
_parquet.cpython-312-aarch64-linux-musl.so
|
|---|
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The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python. __int__ returned non-int (type %.200s) __pyx_capi__ %.200s does not export expected C function %.200s C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s) Interpreter change detected - this module can only be loaded into one interpreter per process. name __loader__ loader __file__ origin __package__ parent __path__ submodule_search_locations base class '%.200s' is not a heap type extension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base type %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %.200s() keywords must be strings %s() got multiple values for keyword argument '%U' invalid vtable found for imported type builtins bool complex datetime date time timedelta tzinfo pyarrow.lib IpcWriteOptions IpcReadOptions Message MemoryPool DataType ListType LargeListType ListViewType LargeListViewType MapType FixedSizeListType StructType DictionaryMemo DictionaryType TimestampType Time32Type Time64Type DurationType FixedSizeBinaryType Decimal32Type Decimal64Type Decimal128Type Decimal256Type RunEndEncodedType BaseExtensionType ExtensionType FixedShapeTensorType Bool8Type OpaqueType UuidType JsonType _Metadata KeyValueMetadata Field Scalar ArrayStatistics _PandasConvertible Tensor SparseCSRMatrix SparseCSCMatrix SparseCOOTensor SparseCSFTensor NullArray BooleanArray NumericArray IntegerArray FloatingPointArray Int8Array UInt8Array Int16Array UInt16Array Int32Array UInt32Array Int64Array UInt64Array HalfFloatArray FloatArray DoubleArray FixedSizeBinaryArray Decimal32Array Decimal64Array Decimal128Array Decimal256Array StructArray BaseListArray ListArray LargeListArray ListViewArray LargeListViewArray MapArray FixedSizeListArray UnionArray StringArray BinaryArray StringViewArray BinaryViewArray DictionaryArray ExtensionArray MonthDayNanoIntervalArray ChunkedArray _Tabular Table RecordBatch Device MemoryManager Buffer ResizableBuffer NativeFile BufferedInputStream BufferedOutputStream CompressedInputStream CompressedOutputStream _CRecordBatchWriter RecordBatchReader CacheOptions Codec StopToken multiple bases have vtable conflict: '%.200s' and '%.200s' join() result is too long for a Python string %.200s() %s takes no keyword arguments takes no arguments %.200s() %s (%zd given) takes exactly one argument keywords must be strings unbound method %.200S() needs an argument __annotations__ must be set to a dict object __name__ must be set to a string object __qualname__ must be set to a string object __defaults__ must be set to a tuple object changes to cyfunction.__defaults__ will not currently affect the values used in function calls __kwdefaults__ must be set to a dict object changes to cyfunction.__kwdefaults__ will not currently affect the values used in function calls function's dictionary may not be deleted setting function's dictionary to a non-dict raise: arg 3 must be a traceback or None instance exception may not have a separate value raise: exception class must be a subclass of BaseException calling %R should have returned an instance of BaseException, not %R while calling a Python object NULL result without error in PyObject_Call Missing type object Argument '%.200s' has incorrect type (expected %.200s, got %.200s) too many values to unpack (expected %zd) an integer is required '%.200s' object is not subscriptable value too large to convert to enum parquet::Encoding::type value too large to convert to int32_t value too large to convert to int cannot fit '%.200s' into an index-sized integer generator already executing generator ignored GeneratorExit cannot import name %S __cinit__ pyarrow/_parquet.pyx pyarrow._parquet.ParquetReader.num_row_groups.__get__ <stringsource> string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_string pyarrow._parquet.wrap_logical_type pyarrow._parquet.ColumnSchema.logical_type.__get__ pyarrow._parquet.Statistics.logical_type.__get__ _x_valid _y_valid _z_valid _m_valid pyarrow._parquet.GeoStatistics.xmin.__get__ pyarrow._parquet.GeoStatistics.xmax.__get__ pyarrow._parquet.GeoStatistics.ymin.__get__ pyarrow._parquet.GeoStatistics.ymax.__get__ pyarrow._parquet.GeoStatistics.zmin.__get__ pyarrow._parquet.GeoStatistics.zmax.__get__ pyarrow._parquet.GeoStatistics.mmin.__get__ pyarrow._parquet.GeoStatistics.mmax.__get__ pyarrow._parquet.Statistics.null_count.__get__ pyarrow._parquet.Statistics.distinct_count.__get__ pyarrow._parquet.Statistics.num_values.__get__ cfunc.to_py.__Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_ vector.to_py.__pyx_convert_vector_to_py_enum____parquet_3a__3a_Encoding_3a__3a_type pyarrow._parquet.ColumnChunkMetaData.encodings.__get__ vector.to_py.__pyx_convert_vector_to_py_int32_t pyarrow._parquet.GeoStatistics.geospatial_types.__get__ pyarrow._parquet.ColumnSchema.max_repetition_level.__get__ pyarrow._parquet.ColumnSchema.max_definition_level.__get__ pyarrow._parquet.ParquetSchema.__getitem__ pyarrow._parquet.ParquetSchema.__eq__ pyarrow._parquet.ParquetSchema.names.__get__ pyarrow._parquet.ParquetSchema.__cinit__ container pyarrow._parquet.FileMetaData.__repr__ pyarrow._parquet.FileMetaData.__hash__ pyarrow._parquet.FileMetaData.__eq__ pyarrow._parquet.FileMetaData.schema.__get__ pyarrow._parquet.FileMetaData.serialized_size.__get__ pyarrow._parquet.FileMetaData.num_columns.__get__ pyarrow._parquet.FileMetaData.num_rows.__get__ pyarrow._parquet.FileMetaData.num_row_groups.__get__ __init__ pyarrow._parquet.FileMetaData.__init__ pyarrow._parquet.RowGroupMetaData.__repr__ pyarrow._parquet.RowGroupMetaData.__eq__ pyarrow._parquet.RowGroupMetaData.num_columns.__get__ pyarrow._parquet.RowGroupMetaData.num_rows.__get__ pyarrow._parquet.RowGroupMetaData.total_byte_size.__get__ pyarrow._parquet.RowGroupMetaData.__init__ pyarrow._parquet.ColumnChunkMetaData.__eq__ pyarrow._parquet.ColumnChunkMetaData.file_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.num_values.__get__ pyarrow._parquet.compression_name_from_enum pyarrow._parquet.ColumnChunkMetaData.compression.__get__ wrap cfunc.to_py.__Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_.wrap pyarrow._parquet.encoding_name_from_enum pyarrow._parquet.ColumnChunkMetaData.has_dictionary_page.__get__ pyarrow._parquet.ColumnChunkMetaData.dictionary_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.data_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.total_compressed_size.__get__ pyarrow._parquet.ColumnChunkMetaData.total_uncompressed_size.__get__ pyarrow._parquet.ColumnChunkMetaData.__init__ pyarrow._parquet.Statistics.__eq__ pyarrow._parquet.Statistics.__init__ pyarrow._parquet.GeoStatistics.__init__ pyarrow._parquet.ParquetLogicalType.__repr__ pyarrow._parquet.ParquetLogicalType.__init__ pyarrow._parquet.SortingColumn.__repr__ pyarrow._parquet.SortingColumn.__hash__ other pyarrow._parquet.SortingColumn.column_index.__get__ pyarrow._parquet.ColumnSchema.__eq__ pyarrow._parquet.physical_type_name_from_enum pyarrow._parquet.ColumnChunkMetaData.physical_type.__get__ pyarrow._parquet.ColumnSchema.physical_type.__get__ pyarrow._parquet.Statistics.physical_type.__get__ pyarrow._parquet.converted_type_name_from_enum pyarrow._parquet.ColumnSchema.converted_type.__get__ pyarrow._parquet.Statistics.converted_type.__get__ pyarrow._parquet.ColumnSchema.length.__get__ pyarrow._parquet.ColumnSchema.precision.__get__ pyarrow._parquet.ColumnSchema.scale.__get__ pyarrow._parquet.ParquetReader.__cinit__ memory_pool to_dict pyarrow._parquet.Statistics.to_dict pyarrow._parquet.GeoStatistics.to_dict equals pyarrow._parquet.ColumnChunkMetaData.equals strings are too large to concat pyarrow._parquet.encoding_enum_from_name a mapping Expected %s, got %.200s FromPyStructUtility.__pyx_convert__from_py_struct____parquet_3a__3a_SortingColumn 'NoneType' object is not subscriptable hasattr(): attribute name must be string pyarrow._parquet.__pyx_unpickle_SortingColumn__set_state pyarrow._parquet.compression_from_name pyarrow._parquet.SortingColumn.to_dict __setstate_cython__ tuple pyarrow._parquet.SortingColumn.__setstate_cython__ pyarrow._parquet.RowGroupMetaData.equals pyarrow._parquet.RowGroupMetaData.to_dict pyarrow._parquet.FileMetaData.equals __reduce__ pyarrow._parquet.ParquetSchema.__reduce__ pyarrow._parquet.ColumnSchema.equals pyarrow._parquet.ParquetWriter.__setstate_cython__ __reduce_cython__ pyarrow._parquet.ParquetWriter.__reduce_cython__ pyarrow._parquet.ParquetReader.__setstate_cython__ pyarrow._parquet.ParquetReader.__reduce_cython__ pyarrow._parquet.ColumnChunkMetaData.__setstate_cython__ pyarrow._parquet.ColumnChunkMetaData.__reduce_cython__ pyarrow._parquet.ColumnChunkMetaData.index_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.has_index_page.__get__ pyarrow._parquet.GeoStatistics.__setstate_cython__ pyarrow._parquet.GeoStatistics.__reduce_cython__ pyarrow._parquet.ParquetLogicalType.__setstate_cython__ pyarrow._parquet.ParquetLogicalType.__reduce_cython__ pyarrow._parquet.Statistics.__setstate_cython__ pyarrow._parquet.Statistics.__reduce_cython__ pyarrow._parquet._box_byte_array pyarrow._parquet._box_flba pyarrow._parquet._cast_statistic_raw_min pyarrow._parquet.Statistics.min_raw.__get__ pyarrow._parquet._cast_statistic_raw_max pyarrow._parquet.Statistics.max_raw.__get__ map.to_py.__pyx_convert_unordered_map_to_py_std_3a__3a_string____std_3a__3a_string string.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_string pyarrow._parquet.ParquetSchema.__hash__ pyarrow._parquet.ParquetSchema.equals pyarrow._parquet.Statistics.equals throw pyarrow/_parquet.pxd pyarrow._parquet.RowGroupMetaData.init row_group pyarrow._parquet.FileMetaData.row_group pyarrow._parquet.FileMetaData.to_dict pyarrow._parquet.ColumnSchema.__reduce__ pyarrow._parquet.RowGroupMetaData.__reduce__ pyarrow._parquet.logical_type_name_from_enum pyarrow._parquet.ParquetLogicalType.type.__get__ set_batch_size pyarrow._parquet.ParquetReader.set_batch_size pyarrow._parquet.ColumnChunkMetaData.to_dict pyarrow._parquet.SortingColumn.__init__ generator raised StopIteration .0 local variable '%s' referenced before assignment genexpr pyarrow._parquet.ColumnSchema.__repr__ pyarrow._parquet.ColumnSchema.__cinit__ column pyarrow._parquet.RowGroupMetaData.column dictionary changed size during iteration need more than %zd value%.1s to unpack 'NoneType' object is not iterable pyarrow._parquet.ParquetSchema.column name '%U' is not defined pyarrow._parquet.ColumnSchema.name.__get__ pyarrow._parquet.ColumnChunkMetaData.file_path.__get__ pyarrow._parquet.FileMetaData.created_by.__get__ to_json pyarrow._parquet.ParquetLogicalType.to_json set_use_threads pyarrow._parquet.ParquetReader.set_use_threads pyarrow._parquet.ParquetLogicalType.__str__ set_file_path pyarrow._parquet.FileMetaData.set_file_path pyarrow._parquet._unwrap_list_type pyarrow._parquet.ParquetSchema.__repr__ pyarrow._parquet.FileMetaData.format_version.__get__ pyarrow._parquet.SortingColumn.__reduce_cython__ pyarrow._parquet.check_compression_name read_row_group pyarrow._parquet.ParquetReader.read_row_group iter_batches pyarrow._parquet.ParquetReader.iter_batches pyarrow._parquet.GeoStatistics.__repr__ pyarrow._parquet.Statistics.__repr__ __pyx_unpickle_SortingColumn pyarrow._parquet.__pyx_unpickle_SortingColumn pyarrow._parquet.ColumnChunkMetaData.__repr__ Unable to initialize pickling for %.200s Module '_parquet' has already been imported. Re-initialisation is not supported. cython_runtime __builtins__ does not match pyarrow._parquet compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d std::shared_ptr<parquet::WriterProperties> (struct __pyx_opt_args_7pyarrow_8_parquet__create_writer_properties *__pyx_optional_args) _create_writer_properties std::shared_ptr<parquet::ArrowWriterProperties> (struct __pyx_opt_args_7pyarrow_8_parquet__create_arrow_writer_properties *__pyx_optional_args) _create_arrow_writer_properties enum arrow::Type::type (PyObject *) _unwrap_list_type _Weakrefable int (arrow::Status const &) check_status arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *) maybe_unbox_memory_pool PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::RandomAccessFile> *) get_reader PyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream> *) get_writer enum arrow::TimeUnit::type (PyObject *) string_to_timeunit std::shared_ptr< arrow::KeyValueMetadata const > (PyObject *) pyarrow_unwrap_metadata PyObject *(std::shared_ptr< arrow::Schema> const &) pyarrow_wrap_schema PyObject *(std::shared_ptr< arrow::Scalar> const &) pyarrow_wrap_scalar PyObject *(std::shared_ptr< arrow::ChunkedArray> const &) pyarrow_wrap_chunked_array PyObject *(std::shared_ptr< arrow::RecordBatch> const &) pyarrow_wrap_batch PyObject *(std::shared_ptr< arrow::Table> const &) pyarrow_wrap_table std::shared_ptr< arrow::DataType> (PyObject *) pyarrow_unwrap_data_type std::shared_ptr< arrow::Schema> (PyObject *) pyarrow_unwrap_schema init pyarrow._parquet close pyarrow._parquet.ParquetWriter.close pyarrow._parquet.ParquetReader.close write_table pyarrow._parquet.ParquetWriter.write_table table pyarrow._parquet.ParquetReader.schema_arrow.__get__ pyarrow._parquet.FileDecryptionProperties.wrap pyarrow._parquet.FileEncryptionProperties.wrap pyarrow._parquet._cast_statistics pyarrow._parquet.Statistics.max.__get__ pyarrow._parquet.Statistics.min.__get__ pyarrow._parquet.ColumnSchema.path.__get__ pyarrow._parquet.ColumnChunkMetaData.path_in_schema.__get__ _reconstruct_filemetadata pyarrow._parquet._reconstruct_filemetadata serialized add_key_value_metadata pyarrow._parquet.ParquetWriter.add_key_value_metadata pyarrow._parquet.ColumnChunkMetaData.statistics.__get__ read_column pyarrow._parquet.ParquetReader.read_column pyarrow._parquet.ColumnChunkMetaData.geo_statistics.__get__ Cannot convert %.200s to %.200s pyarrow._parquet.ParquetReader.column_paths.__get__ pyarrow._parquet.FileMetaData.__reduce__ pyarrow._parquet._create_arrow_writer_properties pyarrow._parquet.ParquetWriter.metadata.__get__ column_name_idx pyarrow._parquet.ParquetReader.column_name_idx append_row_groups pyarrow._parquet.FileMetaData.append_row_groups pyarrow._parquet.RowGroupMetaData.sorting_columns.__get__ to_arrow_schema pyarrow._parquet.ParquetSchema.to_arrow_schema open pyarrow._parquet.ParquetReader.open source Argument '%.200s' must not be None metadata decryption_properties scan_contents pyarrow._parquet.ParquetReader.scan_contents read_all pyarrow._parquet.ParquetReader.read_all read_row_groups pyarrow._parquet.ParquetReader.read_row_groups row_groups can't send non-None value to a just-started generator pyarrow._parquet.FileMetaData.metadata.__get__ pyarrow._parquet.ColumnChunkMetaData.metadata.__get__ pyarrow._parquet.FileMetaData.write_metadata_file write_metadata_file pyarrow._parquet._convert_sorting_columns.genexpr vector.from_py.__pyx_convert_vector_from_py_struct____parquet_3a__3a_SortingColumn vector::reserve pyarrow._parquet._convert_sorting_columns str Can't use dictionary encoding as fallback encoding pyarrow._parquet._create_writer_properties pyarrow._parquet._name_to_index_map to_ordering pyarrow._parquet.SortingColumn.to_ordering items 'NoneType' object has no attribute '%.30s' from_ordering pyarrow._parquet.SortingColumn.from_ordering pyarrow._parquet.ParquetWriter.__cinit__ pyarrow._parquet.ParquetReader._set_read_dictionary _cython_3_1_3.generator __name__ name of the generator __qualname__ qualified name of the generator gi_frame Frame of the generator gi_running gi_yieldfrom object being iterated by 'yield from', or None gi_code __module__ __weaklistoffset__ send send(arg) -> send 'arg' into generator,
return next yielded value or raise StopIteration. throw(typ[,val[,tb]]) -> raise exception in generator,
return next yielded value or raise StopIteration. close() -> raise GeneratorExit inside generator. __reduce_ex__ _cython_3_1_3.cython_function_or_method __dictoffset__ __vectorcalloffset__ func_doc __doc__ func_name func_dict __dict__ func_globals __globals__ func_closure __closure__ func_code __code__ func_defaults __defaults__ __kwdefaults__ __annotations__ _is_coroutine _cython_3_1_3._common_types_metatype pyarrow._parquet.__pyx_scope_struct____Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_ pyarrow._parquet.__pyx_scope_struct_1_genexpr pyarrow._parquet.__pyx_scope_struct__iter_batches pyarrow._parquet.ParquetWriter pyarrow._parquet.ParquetReader column_paths schema_arrow num_row_groups closed _column_idx_map _column_idx_map: object pyarrow._parquet.ColumnSchema Schema for a single column. Name of field (str). path Nested path to field, separated by periods (str). max_definition_level Maximum definition level (int). max_repetition_level Maximum repetition level (int). physical_type Name of physical type (str). logical_type Logical type of column (:class:`ParquetLogicalType`). converted_type Legacy converted type (str or None). length Array length if fixed length byte array type, None otherwise (int or None). precision Precision if decimal type, None otherwise (int or None). scale Scale if decimal type, None otherwise (int or None). pyarrow._parquet.SortingColumn SortingColumn(int column_index, bool descending=False, bool nulls_first=False)
Sorting specification for a single column.
Returned by :meth:`RowGroupMetaData.sorting_columns` and used in
:class:`ParquetWriter` to specify the sort order of the data.
Parameters
----------
column_index : int
Index of column that data is sorted by.
descending : bool, default False
Whether column is sorted in descending order.
nulls_first : bool, default False
Whether null values appear before valid values.
Notes
-----
Column indices are zero-based, refer only to leaf fields, and are in
depth-first order. This may make the column indices for nested schemas
different from what you expect. In most cases, it will be easier to
specify the sort order using column names instead of column indices
and converting using the ``from_ordering`` method.
Examples
--------
In other APIs, sort order is specified by names, such as:
>>> sort_order = [('id', 'ascending'), ('timestamp', 'descending')]
For Parquet, the column index must be used instead:
>>> import pyarrow.parquet as pq
>>> [pq.SortingColumn(0), pq.SortingColumn(1, descending=True)]
[SortingColumn(column_index=0, descending=False, nulls_first=False), SortingColumn(column_index=1, descending=True, nulls_first=False)]
Convert the sort_order into the list of sorting columns with
``from_ordering`` (note that the schema must be provided as well):
>>> import pyarrow as pa
>>> schema = pa.schema([('id', pa.int64()), ('timestamp', pa.timestamp('ms'))])
>>> sorting_columns = pq.SortingColumn.from_ordering(schema, sort_order)
>>> sorting_columns
(SortingColumn(column_index=0, descending=False, nulls_first=False), SortingColumn(column_index=1, descending=True, nulls_first=False))
Convert back to the sort order with ``to_ordering``:
>>> pq.SortingColumn.to_ordering(schema, sorting_columns)
((('id', 'ascending'), ('timestamp', 'descending')), 'at_end')
See Also
--------
RowGroupMetaData.sorting_columns column_index "Index of column data is sorted by (int). descending Whether column is sorted in descending order (bool). nulls_first Whether null values appear before valid values (bool). pyarrow._parquet.ParquetLogicalType ParquetLogicalType()
Logical type of parquet type. Name of the logical type (str). pyarrow._parquet.FileDecryptionProperties File-level decryption properties for the low-level API pyarrow._parquet.GeoStatistics GeoStatistics()
Statistics for columns with geospatial data types (experimental)
These statistics provide a bounding box and list of geometry types for
geospatial columns (GEOMETRY or GEOGRAPHY). All components may be None
if a file does not provide information about a particular component. geospatial_types Unique geometry types
Contains an integer code for each geometry type code encountered in the
geometries represented by these statistics. The geometry type codes are
ISO WKB geometry type codes returned in sorted order without duplicates.
This property may be None if geospatial types are not available.
xmin Minimum X value or None if not available
Note that Parquet permits "wraparound" bounds in the X direction only
to more compactly represent bounds for geometries with components on
both sides of the antimeridian. This case is denoted by xmin > xmax.
xmax Maximum X value or None if not available
Note that Parquet permits "wraparound" bounds in the X direction only
(see xmin).
ymin Minimum Y value or None if not available ymax Maximum Y value or None if not available zmin Minimum Z value or None if not available zmax Maximum Z value or None if not available mmin Minimum M value or None if not available mmax Maximum M value or None if not available pyarrow._parquet.Statistics Statistics()
Statistics for a single column in a single row group. has_min_max Whether min and max are present (bool). has_null_count Whether null count is present (bool). has_distinct_count Whether distinct count is preset (bool). min_raw Min value as physical type (bool, int, float, or bytes). max_raw Max value as physical type (bool, int, float, or bytes). min
Min value as logical type.
Returned as the Python equivalent of logical type, such as datetime.date
for dates and decimal.Decimal for decimals.
Max value as logical type.
Returned as the Python equivalent of logical type, such as datetime.date
for dates and decimal.Decimal for decimals.
null_count Number of null values in chunk (int). distinct_count Distinct number of values in chunk (int). num_values Number of non-null values (int). Physical type of column (str). pyarrow._parquet.ColumnChunkMetaData ColumnChunkMetaData()
Column metadata for a single row group. file_offset Offset into file where column chunk is located (int). file_path Optional file path if set (str or None). Total number of values (int). path_in_schema is_stats_set Whether or not statistics are present in metadata (bool). statistics Statistics for column chunk (:class:`Statistics`). is_geo_stats_set Whether or not geometry statistics are present in metadata (bool). geo_statistics Statistics for column chunk (:class:`GeoStatistics`). compression
Type of compression used for column (str).
One of 'UNCOMPRESSED', 'SNAPPY', 'GZIP', 'LZO', 'BROTLI', 'LZ4', 'ZSTD',
or 'UNKNOWN'.
encodings
Encodings used for column (tuple of str).
One of 'PLAIN', 'BIT_PACKED', 'RLE', 'BYTE_STREAM_SPLIT', 'DELTA_BINARY_PACKED',
'DELTA_LENGTH_BYTE_ARRAY', 'DELTA_BYTE_ARRAY'.
has_dictionary_page Whether there is dictionary data present in the column chunk (bool). dictionary_page_offset Offset of dictionary page relative to beginning of the file (int). data_page_offset Offset of data page relative to beginning of the file (int). has_index_page Not yet supported. index_page_offset total_compressed_size Compressed size in bytes (int). total_uncompressed_size Uncompressed size in bytes (int). has_offset_index Whether the column chunk has an offset index has_column_index Whether the column chunk has a column index Additional metadata as key value pairs (dict[bytes, bytes]). pyarrow._parquet.RowGroupMetaData RowGroupMetaData()
Metadata for a single row group. num_columns Number of columns in this row group (int). num_rows Number of rows in this row group (int). total_byte_size Total byte size of all the uncompressed column data in this row group (int). sorting_columns Columns the row group is sorted by (tuple of :class:`SortingColumn`)). pyarrow._parquet.FileMetaData FileMetaData()
Parquet metadata for a single file. Schema of the file (:class:`ParquetSchema`). serialized_size Size of the original thrift encoded metadata footer (int). Number of columns in file (int). Total number of rows in file (int). Number of row groups in file (int). format_version
Parquet format version used in file (str, such as '1.0', '2.4').
If version is missing or unparsable, will default to assuming '2.6'.
created_by
String describing source of the parquet file (str).
This typically includes library name and version number. For example, Arrow 7.0's
writer returns 'parquet-cpp-arrow version 7.0.0'.
pyarrow._parquet.ParquetSchema A Parquet schema. names Name of each field (list of str). pyarrow._parquet.FileEncryptionProperties File-level encryption properties for the low-level API vector::_M_realloc_insert
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^1ØÐ!ð "Qð QSð STØ= ¨¨Ø|7!Ø/¨qÐ0@ÀÈaØ 1 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 parquet-cpp-arrow version 22.0.0-SNAPSHOT __pyx_unpickle_SortingColumn(__pyx_type, long __pyx_checksum, __pyx_state) ParquetWriter.__setstate_cython__(self, __pyx_state) ParquetWriter.__reduce_cython__(self) ParquetWriter.add_key_value_metadata(self, key_value_metadata) ParquetWriter.write_table(self, Table table, row_group_size=None) ParquetWriter.close(self) ParquetReader.__setstate_cython__(self, __pyx_state) ParquetReader.__reduce_cython__(self) ParquetReader.close(self) ParquetReader.read_column(self, int column_index)
Read the column at the specified index.
Parameters
----------
column_index : int
Index of the column.
Returns
-------
column : pyarrow.ChunkedArray ParquetReader.column_name_idx(self, column_name)
Find the index of a column by its name.
Parameters
----------
column_name : str
Name of the column; separation of nesting levels is done via ".".
Returns
-------
column_idx : int
Integer index of the column in the schema. ParquetReader.scan_contents(self, column_indices=None, batch_size=65536)
Parameters
----------
column_indices : list[int], optional
batch_size : int32, default 65536
Returns
-------
num_rows : int64 ParquetReader.read_all(self, column_indices=None, bool use_threads=True)
Parameters
----------
column_indices : list[int], optional
use_threads : bool, default True
Returns
-------
table : pyarrow.Table ParquetReader.read_row_groups(self, row_groups, column_indices=None, bool use_threads=True)
Parameters
----------
row_groups : list[int]
column_indices : list[int], optional
use_threads : bool, default True
Returns
-------
table : pyarrow.Table ParquetReader.read_row_group(self, int i, column_indices=None, bool use_threads=True)
Parameters
----------
i : int
column_indices : list[int], optional
use_threads : bool, default True
Returns
-------
table : pyarrow.Table ParquetReader.iter_batches(self, int64_t batch_size, row_groups, column_indices=None, bool use_threads=True)
Parameters
----------
batch_size : int64
row_groups : list[int]
column_indices : list[int], optional
use_threads : bool, default True
Yields
------
next : RecordBatch ParquetReader.set_batch_size(self, int64_t batch_size)
Parameters
----------
batch_size : int64 ParquetReader.set_use_threads(self, bool use_threads)
Parameters
----------
use_threads : bool ParquetReader.open(self, source, *, bool use_memory_map=False, read_dictionary=None, binary_type=None, list_type=None, FileMetaData metadata=None, int buffer_size=0, bool pre_buffer=False, coerce_int96_timestamp_unit=None, FileDecryptionProperties decryption_properties=None, thrift_string_size_limit=None, thrift_container_size_limit=None, page_checksum_verification=False, arrow_extensions_enabled=False)
Open a parquet file for reading.
Parameters
----------
source : str, pathlib.Path, pyarrow.NativeFile, or file-like object
use_memory_map : bool, default False
read_dictionary : iterable[int or str], optional
binary_type : pyarrow.DataType, optional
list_type : subclass of pyarrow.DataType, optional
metadata : FileMetaData, optional
buffer_size : int, default 0
pre_buffer : bool, default False
coerce_int96_timestamp_unit : str, optional
decryption_properties : FileDecryptionProperties, optional
thrift_string_size_limit : int, optional
thrift_container_size_limit : int, optional
page_checksum_verification : bool, default False
arrow_extensions_enabled : bool, default False ColumnSchema.equals(self, ColumnSchema other)
Return whether the two column schemas are equal.
Parameters
----------
other : ColumnSchema
Schema to compare against.
Returns
-------
are_equal : bool ColumnSchema.__reduce__(self) ParquetSchema.column(self, i)
Return the schema for a single column.
Parameters
----------
i : int
Index of column in schema.
Returns
-------
column_schema : ColumnSchema ParquetSchema.equals(self, ParquetSchema other)
Return whether the two schemas are equal.
Parameters
----------
other : ParquetSchema
Schema to compare against.
Returns
-------
are_equal : bool ParquetSchema.to_arrow_schema(self)
Convert Parquet schema to effective Arrow schema.
Returns
-------
schema : Schema ParquetSchema.__reduce__(self) FileMetaData.write_metadata_file(self, where)
Write the metadata to a metadata-only Parquet file.
Parameters
----------
where : path or file-like object
Where to write the metadata. Should be a writable path on
the local filesystem, or a writable file-like object. FileMetaData.append_row_groups(self, FileMetaData other)
Append row groups from other FileMetaData object.
Parameters
----------
other : FileMetaData
Other metadata to append row groups from. FileMetaData.set_file_path(self, path)
Set ColumnChunk file paths to the given value.
This method modifies the ``file_path`` field of each ColumnChunk
in the FileMetaData to be a particular value.
Parameters
----------
path : str
The file path to set on all ColumnChunks. FileMetaData.row_group(self, int i)
Get metadata for row group at index i.
Parameters
----------
i : int
Row group index to get.
Returns
-------
row_group_metadata : RowGroupMetaData FileMetaData.equals(self, FileMetaData other)
Return whether the two file metadata objects are equal.
Parameters
----------
other : FileMetaData
Metadata to compare against.
Returns
-------
are_equal : bool FileMetaData.to_dict(self)
Get dictionary representation of the file metadata.
Returns
-------
dict
Dictionary with a key for each attribute of this class. FileMetaData.__reduce__(self) _reconstruct_filemetadata(Buffer serialized) RowGroupMetaData.to_dict(self)
Get dictionary representation of the row group metadata.
Returns
-------
dict
Dictionary with a key for each attribute of this class. RowGroupMetaData.column(self, int i)
Get column metadata at given index.
Parameters
----------
i : int
Index of column to get metadata for.
Returns
-------
ColumnChunkMetaData
Metadata for column within this chunk. RowGroupMetaData.equals(self, RowGroupMetaData other)
Return whether the two row group metadata objects are equal.
Parameters
----------
other : RowGroupMetaData
Metadata to compare against.
Returns
-------
are_equal : bool RowGroupMetaData.__reduce__(self) SortingColumn.__setstate_cython__(self, __pyx_state) SortingColumn.__reduce_cython__(self) SortingColumn.to_dict(self)
Get dictionary representation of the SortingColumn.
Returns
-------
dict
Dictionary with a key for each attribute of this class. SortingColumn.to_ordering(Schema schema, sorting_columns)
Convert a tuple of SortingColumn objects to the same format as
:class:`pyarrow.compute.SortOptions`.
Parameters
----------
schema : Schema
Schema of the input data.
sorting_columns : tuple of SortingColumn
Columns to sort the input on.
Returns
-------
sort_keys : tuple of (name, order) tuples
null_placement : {'at_start', 'at_end'} SortingColumn.from_ordering(cls, Schema schema, sort_keys, null_placement='at_end')
Create a tuple of SortingColumn objects from the same arguments as
:class:`pyarrow.compute.SortOptions`.
Parameters
----------
schema : Schema
Schema of the input data.
sort_keys : Sequence of (name, order) tuples
Names of field/column keys (str) to sort the input on,
along with the order each field/column is sorted in.
Accepted values for `order` are "ascending", "descending".
null_placement : {'at_start', 'at_end'}, default 'at_end'
Where null values should appear in the sort order.
Returns
-------
sorting_columns : tuple of SortingColumn ColumnChunkMetaData.__setstate_cython__(self, __pyx_state) ColumnChunkMetaData.__reduce_cython__(self) ColumnChunkMetaData.equals(self, ColumnChunkMetaData other)
Return whether the two column chunk metadata objects are equal.
Parameters
----------
other : ColumnChunkMetaData
Metadata to compare against.
Returns
-------
are_equal : bool ColumnChunkMetaData.to_dict(self)
Get dictionary representation of the column chunk metadata.
Returns
-------
dict
Dictionary with a key for each attribute of this class. GeoStatistics.__setstate_cython__(self, __pyx_state) GeoStatistics.__reduce_cython__(self) GeoStatistics._m_valid(self) GeoStatistics._z_valid(self) GeoStatistics._y_valid(self) GeoStatistics._x_valid(self) GeoStatistics.to_dict(self)
Dictionary summary of these statistics ParquetLogicalType.__setstate_cython__(self, __pyx_state) ParquetLogicalType.__reduce_cython__(self) ParquetLogicalType.to_json(self)
Get a JSON string containing type and type parameters.
Returns
-------
json : str
JSON representation of type, with at least a field called 'Type'
which contains the type name. If the type is parameterized, such
as a decimal with scale and precision, will contain those as fields
as well. Statistics.__setstate_cython__(self, __pyx_state) Statistics.__reduce_cython__(self) Statistics.equals(self, Statistics other)
Return whether the two column statistics objects are equal.
Parameters
----------
other : Statistics
Statistics to compare against.
Returns
-------
are_equal : bool Statistics.to_dict(self)
Get dictionary representation of statistics.
Returns
-------
dict
Dictionary with a key for each attribute of this class. wrap(encoding_: 'ParquetEncoding') No value specified for struct attribute 'nulls_first' No value specified for struct attribute 'descending' thrift_string_size_limit must be larger than zero thrift_container_size_limit must be larger than zero parquet-cpp doesn't return valid values no default __reduce__ due to non-trivial __cinit__ V1 parquet writer engine is a no-op. Use V2. Unsupported Writer Engine Version: Unsupported Parquet format version: Unsupported Parquet data page version: Unrecognized file version, assuming 2.6: Unknown options in 'use_content_defined_chunking': To use 'column_encoding' set 'use_dictionary' to False Sorting columns have inconsistent null placement ParquetWriter.add_key_value_metadata ParquetLogicalType.__setstate_cython__ ParquetLogicalType.__reduce_cython__ Note that Cython is deliberately stricter than PEP-484 and rejects subclasses of builtin types. If you need to pass subclasses then set the 'annotation_typing' directive to False. No value specified for struct attribute 'column_idx' Missing options in 'use_content_defined_chunking': Invalid value for coerce_timestamps: Incompatible checksums (0x%x vs (0xc86835a, 0x6aaa2f8, 0x6e1a100) = (column_index, descending, nulls_first)) FileMetaData.write_metadata_file ColumnChunkMetaData.__setstate_cython__ ColumnChunkMetaData.__reduce_cython__ use_deprecated_int96_timestamps 'use_content_defined_chunking' should be either boolean or a dictionary 'use_byte_stream_split' cannot be passedtogether with 'column_encoding' 'store_decimal_as_integer' must be a boolean null_placement must be "at_start" or "at_end" file metadata is only available after writer close 'column_encoding' should be a dictionary or a string SortingColumn.__setstate_cython__ ParquetWriter.__setstate_cython__ ParquetReader.__setstate_cython__ GeoStatistics.__setstate_cython__ Buffer size must be larger than zero 'sorting_columns' must be a list of `SortingColumn` _convert_sorting_columns.<locals>.genexpr __Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_.<locals>.wrap FileMetaData.append_row_groups SortingColumn.__reduce_cython__ ParquetWriter.__reduce_cython__ ParquetSchema.to_arrow_schema ParquetReader.set_use_threads ParquetReader.read_row_groups ParquetReader.column_name_idx ParquetReader.__reduce_cython__ GeoStatistics.__reduce_cython__ use_content_defined_chunking not supported in parquet-cpp Statistics.__setstate_cython__ ParquetReader.set_batch_size ParquetReader.read_row_group thrift_container_size_limit coerce_int96_timestamp_unit Unsupported column encoding: SortingColumn.from_ordering ParquetReader.scan_contents FIXED_LEN_BYTE_ARRAY(length= ColumnChunkMetaData.to_dict __pyx_unpickle_SortingColumn page_checksum_verification is already used by default. allow_truncated_timestamps Statistics.__reduce_cython__ Row group size cannot be 0 ParquetReader.iter_batches ParquetLogicalType.to_json Invalid sort key direction: FileMetaData.set_file_path ColumnChunkMetaData.equals use_compliant_nested_type total_uncompressed_size dictionary_pagesize_limit SortingColumn.to_ordering RowGroupMetaData.__reduce__ ParquetWriter.write_table ParquetReader.read_column thrift_string_size_limit store_decimal_as_integer _reconstruct_filemetadata dictionary_page_offset arrow_extensions_enabled RowGroupMetaData.to_dict <ParquetColumnSchema>
name: FileEncryptionProperties FileDecryptionProperties
total_uncompressed_size: total_compressed_size Unsupported compression: RowGroupMetaData.equals RowGroupMetaData.column DELTA_LENGTH_BYTE_ARRAY 's constructor directly max_repetition_level max_definition_level
dictionary_page_offset: add_key_value_metadata ParquetSchema.__reduce__ ParquetReader.read_all GeoStatistics._z_valid GeoStatistics._y_valid GeoStatistics._x_valid GeoStatistics._m_valid FileMetaData.row_group _DEFAULT_ROW_GROUP_SIZE writer_engine_version use_byte_stream_split
total_compressed_size: has_dictionary_page encryption_properties decryption_properties
converted_type (legacy): SortingColumn.to_dict GeoStatistics.to_dict FileMetaData.__reduce__ ColumnSchema.__reduce__ pyarrow/_parquet.pyx
max_repetition_level:
max_definition_level: is_threading_enabled Unexpected list_type: ParquetSchema.equals ParquetSchema.column FileMetaData.to_dict FIXED_LEN_BYTE_ARRAY write_page_checksum write_metadata_file ' not found in schema:
has_dictionary_page: ParquetWriter.close ParquetReader.close NotImplementedError FileMetaData.equals DELTA_BINARY_PACKED ColumnSchema.equals ColumnChunkMetaData key_value_metadata has_distinct_count geospatial_types data_page_offset cline_in_traceback asyncio.coroutines Statistics.to_dict ParquetReader.open ParquetLogicalType _MAX_ROW_GROUP_SIZE BufferOutputStream total_byte_size
sorting_columns:
serialized_size: recordbatchreader data_page_version compression_level coerce_timestamps append_row_groups Statistics.equals BYTE_STREAM_SPLIT write_statistics write_page_index write_batch_size pyarrow._parquet path_in_schema
num_row_groups: is_geo_stats_set
geospatial_types:
geo_statistics:
format_version: distinct_count
data_page_offset: c_row_group_size c_column_indices TIMESTAMP_MILLIS TIMESTAMP_MICROS RowGroupMetaData PLAIN_DICTIONARY Invalid sort key: DELTA_BYTE_ARRAY
total_byte_size: to_arrow_schema sp_arrow_schema sorting_columns __setstate_cython__ set_use_threads serialized_size read_row_groups read_dictionary __pyx_PickleError physical_type column_name_idx column_encoding collections.abc {0} out of bounds use_memory_map use_dictionary _stringify_path sorting_column set_batch_size row_group_size read_row_group
path_in_schema: num_row_groups null_placement min_chunk_size max_chunk_size logical_type is_stats_set has_null_count geo_statistics format_version
distinct_count: data_page_size converted_type column_indices (column_index= RLE_DICTIONARY NotImplemented ArrowException set_file_path scan_contents __reduce_cython__
physical_type: out of bounds
num_columns: , nulls_first= has_min_max from_ordering file_offset compression __class_getitem__ c_binary_type SortingColumn Sort key name ' ParquetWriter ParquetSchema ParquetReader MAP_KEY_VALUE LargeListType GeoStatistics use_setstate <stringsource> store_schema
statistics:
staticmethod record_batch __pyx_checksum num_values null_count metadata_len
logical_type: iter_batches
is_stats_set: _is_coroutine _initializing , descending= created_by column_index c_row_groups c_batch_size UNCOMPRESSED RuntimeError FileMetaData ColumnSchema write_table use_threads to_ordering read_column pyarrow.lib num_columns nulls_first memory_pool
has_min_max: file_path
file_offset: encodings
compression: column_name cfunc.to_py buffer_size binary_type arrow_props TIME_MILLIS TIME_MICROS PickleError MemoryError Do not call statistics serialized row_groups __pyx_vtable__ __pyx_result properties pre_buffer
num_values: num_rows
null_count: norm_level descending
created_by: column_idx c_num_rows c_metadata batch_size ValueError Statistics NativeFile IndexError BYTE_ARRAY BIT_PACKED sort_keys row_group __reduce_ex__ __pyx_state precision list_type iteritems isenabled frombytes
file_path:
encodings: container col_bytes ascending UNDEFINED TypeError TIMESTAMP DECIMAL( warnings textwrap sort_key __setstate__ __set_name__ read_all __qualname__ __pyx_type
num_rows: metadata getvalue __getstate__ encoding_ at_start add_note Sequence ListType KeyError INTERVAL _z_valid _y_valid _x_valid version tobytes to_json to_dict _m_valid genexpr disable columns col_map c_where builder UNKNOWN UINT_64 UINT_32 UINT_16 FLOAT16 DECIMAL BOOLEAN zmin zmax ymin ymax xmin xmax update source schema result __reduce__ pickle
path: object name __module__ mmin mmax length indent format equals enable _dict dict ctable column closed c_sink c_path buffer at_end UINT_8 STRING SNAPPY INT_64 INT_32 INT_16 DOUBLE BROTLI where value upper throw table state scale range other order min max items index close __class__ chunk as_py PLAIN INT_8 INT96 INT64 INT32 FLOAT
zmin: , zmax:
ymin: , ymax:
xmin: , xmax: wrap warn __test__ __spec__ sink send self safe __repr__ path open next __name__
mmin: , mmax: __main__ keys __func__ __dict__ ZSTD UUID UTF8 TIME NONE LIST JSON GZIP ENUM DATE BSON pop out __new__
min:
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