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arrow-nightlies / pyarrow python
Repository URL to install this package:
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Version:
22.0.0.dev96 ▾
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pyarrow
/
_parquet.cpython-311-x86_64-linux-musl.so
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|---|
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Name of field (str). max_definition_level max_repetition_level physical_type Name of physical type (str). logical_type converted_type length precision scale descending nulls_first geospatial_types xmin xmax ymin ymax zmin zmax mmin mmax pyarrow._parquet.Statistics has_min_max has_null_count has_distinct_count min_raw max_raw num_values file_offset Total number of values (int). path_in_schema is_stats_set is_geo_stats_set geo_statistics compression encodings has_dictionary_page dictionary_page_offset data_page_offset has_index_page Not yet supported. index_page_offset total_compressed_size total_uncompressed_size has_offset_index has_column_index num_columns num_rows total_byte_size sorting_columns pyarrow._parquet.FileMetaData serialized_size format_version created_by A Parquet schema. names vector::_M_realloc_insert %.200s() takes %.8s %zd positional argument%.1s (%zd given) Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling strings are too large to concat %.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. 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 multiple bases have vtable conflict: '%.200s' and '%.200s' %s() got an unexpected keyword argument '%U' Argument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%U __annotations__ must be set to a dict object __qualname__ must be set to a string object __name__ 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 join() result is too long for a Python string __int__ returned non-int (type %.200s). 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) while calling a Python object NULL result without error in PyObject_Call too many values to unpack (expected %zd) 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 '%.200s' object is not subscriptable 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 generator ignored GeneratorExit cannot fit '%.200s' into an index-sized integer pyarrow._parquet.ParquetReader.num_row_groups.__get__ pyarrow._parquet.RowGroupMetaData.init string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_string 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__ pyarrow._parquet.ParquetSchema.names.__get__ pyarrow._parquet.FileMetaData.__hash__ pyarrow._parquet.FileMetaData.schema.__get__ pyarrow._parquet.FileMetaData.__init__ pyarrow._parquet.RowGroupMetaData.__repr__ pyarrow._parquet.RowGroupMetaData.__init__ pyarrow._parquet.ColumnChunkMetaData.dictionary_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.__init__ pyarrow._parquet.Statistics.__init__ pyarrow._parquet.GeoStatistics.__init__ pyarrow._parquet.ParquetLogicalType.__repr__ pyarrow._parquet.ParquetLogicalType.__init__ pyarrow._parquet.SortingColumn.__repr__ pyarrow._parquet.SortingColumn.__hash__ pyarrow._parquet.compression_from_name pyarrow._parquet.SortingColumn.to_dict pyarrow._parquet.ParquetSchema.__getitem__ pyarrow._parquet._box_byte_array pyarrow._parquet._cast_statistic_raw_min pyarrow._parquet.Statistics.min_raw.__get__ pyarrow._parquet.ParquetWriter.__reduce_cython__ pyarrow._parquet.ParquetReader.__reduce_cython__ pyarrow._parquet.ColumnSchema.scale.__get__ pyarrow._parquet.ColumnSchema.precision.__get__ pyarrow._parquet.ColumnSchema.length.__get__ pyarrow._parquet.ColumnSchema.max_repetition_level.__get__ pyarrow._parquet.ColumnSchema.max_definition_level.__get__ pyarrow._parquet.FileMetaData.num_row_groups.__get__ pyarrow._parquet.FileMetaData.num_rows.__get__ pyarrow._parquet.FileMetaData.num_columns.__get__ pyarrow._parquet.FileMetaData.serialized_size.__get__ pyarrow._parquet.RowGroupMetaData.total_byte_size.__get__ pyarrow._parquet.RowGroupMetaData.num_rows.__get__ pyarrow._parquet.RowGroupMetaData.num_columns.__get__ pyarrow._parquet.SortingColumn.column_index.__get__ pyarrow._parquet.ColumnChunkMetaData.__reduce_cython__ pyarrow._parquet.ColumnChunkMetaData.total_uncompressed_size.__get__ pyarrow._parquet.ColumnChunkMetaData.total_compressed_size.__get__ pyarrow._parquet.ColumnChunkMetaData.index_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.has_index_page.__get__ pyarrow._parquet.ColumnChunkMetaData.data_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.has_dictionary_page.__get__ pyarrow._parquet.compression_name_from_enum pyarrow._parquet.ColumnChunkMetaData.compression.__get__ pyarrow._parquet.ColumnChunkMetaData.num_values.__get__ pyarrow._parquet.ColumnChunkMetaData.file_offset.__get__ pyarrow._parquet.GeoStatistics.__reduce_cython__ pyarrow._parquet.ParquetLogicalType.__reduce_cython__ pyarrow._parquet.Statistics.__reduce_cython__ 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.column pyarrow._parquet.ParquetReader.__cinit__ vector.to_py.__pyx_convert_vector_to_py_int32_t pyarrow._parquet.GeoStatistics.geospatial_types.__get__ pyarrow._parquet.Statistics.__setstate_cython__ pyarrow._parquet.ParquetReader.__setstate_cython__ pyarrow._parquet.ParquetWriter.__setstate_cython__ pyarrow._parquet.ParquetLogicalType.__setstate_cython__ pyarrow._parquet.ColumnChunkMetaData.__setstate_cython__ pyarrow._parquet.GeoStatistics.__setstate_cython__ cfunc.to_py.__Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_.wrap pyarrow._parquet.FileMetaData.__repr__ unbound method %.200S() needs an argument pyarrow._parquet.Statistics.to_dict dictionary changed size during iteration need more than %zd value%.1s to unpack 'NoneType' object is not iterable pyarrow._parquet.physical_type_name_from_enum pyarrow._parquet.Statistics.physical_type.__get__ pyarrow._parquet.ColumnSchema.physical_type.__get__ pyarrow._parquet.ColumnChunkMetaData.physical_type.__get__ pyarrow._parquet.RowGroupMetaData.to_dict pyarrow._parquet.wrap_logical_type pyarrow._parquet.Statistics.logical_type.__get__ pyarrow._parquet.ColumnSchema.logical_type.__get__ pyarrow._parquet.ParquetSchema.__cinit__ pyarrow._parquet.ParquetSchema.__reduce__ pyarrow._parquet.FileMetaData.equals pyarrow._parquet.ColumnSchema.equals pyarrow._parquet.RowGroupMetaData.equals pyarrow._parquet.ColumnChunkMetaData.equals pyarrow._parquet.RowGroupMetaData.column pyarrow._parquet.ParquetSchema.equals pyarrow._parquet.FileMetaData.row_group pyarrow._parquet.Statistics.equals pyarrow._parquet.RowGroupMetaData.__reduce__ pyarrow._parquet.ColumnSchema.__reduce__ 'NoneType' object is not subscriptable hasattr(): attribute name must be string pyarrow._parquet.__pyx_unpickle_SortingColumn__set_state pyarrow._parquet.SortingColumn.__setstate_cython__ pyarrow._parquet._unwrap_list_type pyarrow._parquet.SortingColumn.__reduce_cython__ pyarrow._parquet.check_compression_name pyarrow._parquet.ColumnChunkMetaData.file_path.__get__ pyarrow._parquet.FileMetaData.created_by.__get__ pyarrow._parquet.ColumnSchema.name.__get__ pyarrow._parquet.ParquetLogicalType.to_json pyarrow._parquet.ParquetLogicalType.__str__ pyarrow._parquet.ParquetSchema.__repr__ pyarrow._parquet.FileMetaData.set_file_path pyarrow._parquet.ParquetReader.set_use_threads pyarrow._parquet.__pyx_unpickle_SortingColumn pyarrow._parquet.SortingColumn.__init__ pyarrow._parquet.encoding_name_from_enum pyarrow._parquet.ParquetReader.set_batch_size generator raised StopIteration local variable '%s' referenced before assignment pyarrow._parquet.converted_type_name_from_enum pyarrow._parquet.Statistics.converted_type.__get__ pyarrow._parquet.ColumnSchema.converted_type.__get__ pyarrow._parquet.RowGroupMetaData.__eq__ pyarrow._parquet.ColumnSchema.__eq__ pyarrow._parquet.FileMetaData.__eq__ pyarrow._parquet.ColumnChunkMetaData.__eq__ pyarrow._parquet.Statistics.__eq__ pyarrow._parquet.ParquetSchema.__eq__ pyarrow._parquet.ColumnSchema.__cinit__ pyarrow._parquet.GeoStatistics.to_dict pyarrow._parquet.ParquetReader.iter_batches pyarrow._parquet.logical_type_name_from_enum pyarrow._parquet.ParquetLogicalType.type.__get__ pyarrow._parquet.FileMetaData.to_dict pyarrow._parquet.ColumnChunkMetaData.to_dict pyarrow._parquet.ParquetReader.read_row_group pyarrow._parquet.encoding_enum_from_name pyarrow._parquet.GeoStatistics.__repr__ pyarrow._parquet.ColumnChunkMetaData.__repr__ pyarrow._parquet.Statistics.__repr__ pyarrow._parquet.ColumnSchema.__repr__ pyarrow._parquet.FileMetaData.format_version.__get__ Unable to initialize pickling for %.200s Module '_parquet' has already been imported. Re-initialisation is not supported. 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) 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 *) arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *) PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::RandomAccessFile> *) PyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream> *) enum arrow::TimeUnit::type (PyObject *) std::shared_ptr< arrow::KeyValueMetadata const > (PyObject *) PyObject *(std::shared_ptr< arrow::Schema> const &) PyObject *(std::shared_ptr< arrow::Scalar> const &) PyObject *(std::shared_ptr< arrow::ChunkedArray> const &) PyObject *(std::shared_ptr< arrow::RecordBatch> const &) PyObject *(std::shared_ptr< arrow::Table> const &) std::shared_ptr< arrow::DataType> (PyObject *) std::shared_ptr< arrow::Schema> (PyObject *) pyarrow._parquet.ParquetReader.column_name_idx Cannot convert %.200s to %.200s pyarrow._parquet.ParquetWriter.close pyarrow._parquet.ParquetReader.close pyarrow._parquet.ParquetWriter.write_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__ pyarrow._parquet._reconstruct_filemetadata pyarrow._parquet.ParquetWriter.add_key_value_metadata pyarrow._parquet.ParquetReader.read_column pyarrow._parquet.ColumnChunkMetaData.statistics.__get__ pyarrow._parquet.ParquetReader.column_paths.__get__ pyarrow._parquet.ParquetWriter.metadata.__get__ pyarrow._parquet._create_arrow_writer_properties pyarrow._parquet.FileMetaData.__reduce__ pyarrow._parquet.FileMetaData.append_row_groups pyarrow._parquet.ColumnChunkMetaData.geo_statistics.__get__ pyarrow._parquet.RowGroupMetaData.sorting_columns.__get__ pyarrow._parquet.ParquetSchema.to_arrow_schema pyarrow._parquet.ParquetReader.open Argument '%.200s' must not be None pyarrow._parquet.ParquetReader.scan_contents pyarrow._parquet.ParquetReader.read_all pyarrow._parquet.ParquetReader.read_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 pyarrow._parquet._convert_sorting_columns.genexpr vector.from_py.__pyx_convert_vector_from_py_struct____parquet_3a__3a_SortingColumn FromPyStructUtility.__pyx_convert__from_py_struct____parquet_3a__3a_SortingColumn pyarrow._parquet._convert_sorting_columns Can't use dictionary encoding as fallback encoding pyarrow._parquet._create_writer_properties pyarrow._parquet._name_to_index_map pyarrow._parquet.SortingColumn.to_ordering 'NoneType' object has no attribute '%.30s' pyarrow._parquet.SortingColumn.from_ordering pyarrow._parquet.ParquetWriter.__cinit__ pyarrow._parquet.ParquetReader._set_read_dictionary qualified name of the generator object being iterated by 'yield from', or None 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. _cython_3_1_3.cython_function_or_method _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 Nested path to field, separated by periods (str). Maximum definition level (int). Maximum repetition level (int). Logical type of column (:class:`ParquetLogicalType`). Legacy converted type (str or None). Array length if fixed length byte array type, None otherwise (int or None). Precision if decimal type, None otherwise (int or None). 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 "Index of column data is sorted by (int). Whether column is sorted in descending order (bool). 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. 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.
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.
Maximum X value or None if not available
Note that Parquet permits "wraparound" bounds in the X direction only
(see xmin).
Minimum Y value or None if not available Maximum Y value or None if not available Minimum Z value or None if not available Maximum Z value or None if not available Minimum M value or None if not available Maximum M value or None if not available Statistics()
Statistics for a single column in a single row group. Whether min and max are present (bool). Whether null count is present (bool). Whether distinct count is preset (bool). Min value as physical type (bool, int, float, or bytes). Max value as physical type (bool, int, float, or bytes).
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.
Number of null values in chunk (int). Distinct number of values in chunk (int). Number of non-null values (int). Physical type of column (str). pyarrow._parquet.ColumnChunkMetaData ColumnChunkMetaData()
Column metadata for a single row group. Offset into file where column chunk is located (int). Optional file path if set (str or None). Whether or not statistics are present in metadata (bool). Statistics for column chunk (:class:`Statistics`). Whether or not geometry statistics are present in metadata (bool). Statistics for column chunk (:class:`GeoStatistics`).
Type of compression used for column (str).
One of 'UNCOMPRESSED', 'SNAPPY', 'GZIP', 'LZO', 'BROTLI', 'LZ4', 'ZSTD',
or 'UNKNOWN'.
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'.
Whether there is dictionary data present in the column chunk (bool). Offset of dictionary page relative to beginning of the file (int). Offset of data page relative to beginning of the file (int). Compressed size in bytes (int). Uncompressed size in bytes (int). Whether the column chunk has an offset 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. Number of columns in this row group (int). Number of rows in this row group (int). Total byte size of all the uncompressed column data in this row group (int). Columns the row group is sorted by (tuple of :class:`SortingColumn`)). FileMetaData()
Parquet metadata for a single file. Schema of the file (:class:`ParquetSchema`). 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).
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'.
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 Name of each field (list of str). pyarrow._parquet.FileEncryptionProperties File-level encryption properties for the low-level API ´«úÿܫúÿþ«úÿ ¬úÿB¬úÿg¬úÿ¬úÿ±¬úÿ֬úÿû¬úÿ úÿ üÿ¼ üÿì üÿ»
üÿ$ üÿ\ üÿ üÿ$ üÿô!üÿ "üÿL"üÿ!üÿ"üÿ¼"üÿô"üÿ!üÿ#ýÿýÿ ýÿ ýÿôýÿ7)ÿÿ()ÿÿ)ÿÿ
)ÿÿû(ÿÿì(ÿÿÝ(ÿÿÎ(ÿÿ¿(ÿÿ°(ÿÿ¡(ÿÿ(ÿÿ(ÿÿt(ÿÿb(ÿÿP(ÿÿ>(ÿÿ,(ÿÿ(ÿÿ(ÿÿö'ÿÿä'ÿÿÒ'ÿÿÀ'ÿÿ®'ÿÿ'ÿÿ'ÿÿ±Nÿÿ±Nÿÿ9ÿÿ³LÿÿÝJÿÿ<JÿÿHÿÿeGÿÿLÿÿKÿÿÐFÿÿ>Fÿÿ¯Eÿÿ#EÿÿDÿÿ Dÿÿ©CÿÿACÿÿäBÿÿ AÿÿõIÿÿ¹IÿÿIÿÿbIÿÿGIÿÿ¡HÿÿXMÿÿ 0123456789abcdef0123456789ABCDEF 00010203040506071011121314151617202122232425262730313233343536374041424344454647505152535455565760616263646566677071727374757677 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 __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__ Að
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name: FileEncryptionProperties FileDecryptionProperties
total_uncompressed_size: total_compressed_size Unsupported compression: RowGroupMetaData.equals RowGroupMetaData.column DELTA_LENGTH_BYTE_ARRAY Að
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total_compressed_size: has_dictionary_page encryption_properties decryption_properties
converted_type (legacy): SortingColumn.to_dict GeoStatistics.to_dict FileMetaData.__reduce__ ColumnSchema.__reduce__ Aà Ø AØ DØ DØ DØ DØ DØ DØ DØ Dð q pyarrow/_parquet.pyx
max_repetition_level:
max_definition_level: is_threading_enabled Unexpected list_type: ParquetSchema.equals ParquetSchema.column à ¨H°A°QØ* G¨4¨qØ
¨&°°Qà EÐ$ A V¨5°°A°Qà 1 FileMetaData.to_dict FIXED_LEN_BYTE_ARRAY AØt;d "Ð$4°B°a°qØ D 4 rÐ)9¸¸1¸A Að Ø AØ AØ AØ t1Ø 4qØ t1Ø $aàq 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 Að 2RrBcAQØ *ARt 1à|1F ! 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 Að Ð qØ t2Ð -¨Q¨aà t2Ð -¨Q¨a {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 AØ
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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 AØ 4t1ØØ AT ¨4¨r°°q Aà Ð 0°Ø $ DÐ(:¸"¸DÀØ
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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 Að Ð,¨AÐ-=¸Q¸aØ
Ø W D¨Ø-¨Q¨a 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 Að t;d " G¨1¨F°%°{À$Àa Að Ø Ð*¨!ØIÐ <¸AØG:Ð%8¸ØàÐ" ! 1 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 Að
g Q aØ Jn A Q Að Ø QØ t1Ø Aàq Ð3°1Ø()ð tÐ# 1 A TÐ)9¸ sort_keys row_group __reduce_ex__ __pyx_state precision list_type iteritems isenabled frombytes
file_path:
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