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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-39-darwin.so
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í ö$ó ©ï ¿ ë ÊÝ ßï þó © ½ó ÅË ÿÿ6$ )O x ¦¸ Þ" e ðT Ä; ÿA ÿ x x¥ ÿ X Xt ÿÿ ¬ ¬ ê ÉÁ _ ð¿name pyarrow._parquet pyarrow/_parquet.pyx <stringsource>
{0} out of bounds 1.0 2.0 2.4 2.6 ArrowException BIT_PACKED BOOLEAN BROTLI BSON BYTE_ARRAY BYTE_STREAM_SPLIT BufferOutputStream Buffer size must be larger than zero ColumnChunkMetaData ColumnChunkMetaData.__reduce_cython__ ColumnChunkMetaData.__setstate_cython__ ColumnChunkMetaData.equals ColumnChunkMetaData.to_dict ColumnSchema ColumnSchema.__reduce__ ColumnSchema.equals DATE DECIMAL DECIMAL( _DEFAULT_ROW_GROUP_SIZE DELTA_BINARY_PACKED DELTA_BYTE_ARRAY DELTA_LENGTH_BYTE_ARRAY DOUBLE Do not call ENUM FIXED_LEN_BYTE_ARRAY FIXED_LEN_BYTE_ARRAY(length= FLOAT FLOAT16 FileDecryptionProperties FileEncryptionProperties FileMetaData FileMetaData.__reduce__ FileMetaData.append_row_groups FileMetaData.equals FileMetaData.row_group FileMetaData.set_file_path FileMetaData.to_dict FileMetaData.write_metadata_file GZIP GeoStatistics GeoStatistics.__reduce_cython__ GeoStatistics.__setstate_cython__ GeoStatistics._m_valid GeoStatistics._x_valid GeoStatistics._y_valid GeoStatistics._z_valid GeoStatistics.to_dict INT INT32 INT64 INT96 INTERVAL INT_16 INT_32 INT_64 INT_8 Incompatible checksums (0x%x vs (0xc86835a, 0x6aaa2f8, 0x6e1a100) = (column_index, descending, nulls_first)) IndexError Invalid sort key: Invalid sort key direction: Invalid value for coerce_timestamps: JSON KeyError LIST LZ4 LZO MAP MAP_KEY_VALUE _MAX_ROW_GROUP_SIZE MemoryError Missing options in 'use_content_defined_chunking': NONE No value specified for struct attribute 'column_idx' No value specified for struct attribute 'descending' No value specified for struct attribute 'nulls_first' NotImplemented NotImplementedError 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. PLAIN PLAIN_DICTIONARY <ParquetColumnSchema>
name: ParquetLogicalType ParquetLogicalType.__reduce_cython__ ParquetLogicalType.__setstate_cython__ ParquetLogicalType.to_json ParquetReader ParquetReader.__reduce_cython__ ParquetReader.__setstate_cython__ ParquetReader.close ParquetReader.column_name_idx ParquetReader.iter_batches ParquetReader.open ParquetReader.read_all ParquetReader.read_column ParquetReader.read_row_group ParquetReader.read_row_groups ParquetReader.scan_contents ParquetReader.set_batch_size ParquetReader.set_use_threads ParquetSchema ParquetSchema.__reduce__ ParquetSchema.column ParquetSchema.equals ParquetSchema.to_arrow_schema ParquetWriter ParquetWriter.__reduce_cython__ ParquetWriter.__setstate_cython__ ParquetWriter.add_key_value_metadata ParquetWriter.close ParquetWriter.write_table PickleError __Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_.<locals>.wrap RLE RLE_DICTIONARY RowGroupMetaData RowGroupMetaData.__reduce__ RowGroupMetaData.column RowGroupMetaData.equals RowGroupMetaData.to_dict Row group size cannot be 0 RuntimeError SNAPPY STRING Sequence Sort key name ' SortingColumn SortingColumn.__reduce_cython__ SortingColumn.__setstate_cython__ SortingColumn.from_ordering SortingColumn.to_dict SortingColumn.to_ordering Sorting columns have inconsistent null placement Statistics Statistics.__reduce_cython__ Statistics.__setstate_cython__ Statistics.equals Statistics.to_dict TIME TIMESTAMP TIMESTAMP_MICROS TIMESTAMP_MILLIS TIME_MICROS TIME_MILLIS To use 'column_encoding' set 'use_dictionary' to False TypeError UINT_16 UINT_32 UINT_64 UINT_8 UNCOMPRESSED UNDEFINED UNKNOWN UTF8 UUID Unexpected list_type: Unknown options in 'use_content_defined_chunking': Unrecognized file version, assuming 2.6: Unsupported Parquet data page version: Unsupported Parquet format version: Unsupported Writer Engine Version: Unsupported column encoding: Unsupported compression: V1 V1 parquet writer engine is a no-op. Use V2. V2 ValueError ZSTD )
, . ? add_note allow_truncated_timestamps arrow_extensions_enabled arrow_props as_py ascending asyncio.coroutines at_end at_start batch_size binary_type buffer buffer_size builder c_batch_size c_binary_type c_column_indices c_metadata c_num_rows c_path c_row_group_size c_row_groups c_sink c_where cfunc.to_py chunk __class__ __class_getitem__ cline_in_traceback cls coerce_int96_timestamp_unit coerce_timestamps col col_bytes col_map collections.abc column_encoding 'column_encoding' should be a dictionary or a string column_idx (column_index= column_indices column_name columns
compression: compression_level _convert_sorting_columns.<locals>.genexpr
converted_type (legacy):
created_by: ctable d
data_page_offset: data_page_size data_page_version , descending= dict _dict
dictionary_page_offset: dictionary_pagesize_limit disable
distinct_count: enable encoding_
encodings: encryption_properties file metadata is only available after writer close
file_offset:
file_path: format
format_version: frombytes __func__ gc
geo_statistics:
geospatial_types: get __getstate__ getvalue
has_dictionary_page:
has_min_max: i indent index _initializing is already used by default.
is_stats_set: is_threading_enabled isenabled iteritems key_value_metadata keys list_type
logical_type: __main__ map
max: max_chunk_size
max_definition_level:
max_repetition_level: metadata_len
min: min_chunk_size , mmax:
mmin: ms __new__ next no default __reduce__ due to non-trivial __cinit__ norm_level ' not found in schema:
not supported in parquet-cpp
null_count: null_placement null_placement must be "at_start" or "at_end" , nulls_first=
num_columns:
num_row_groups:
num_rows:
num_values: object order out out of bounds page_checksum_verification parquet-cpp doesn't return valid values
path:
path_in_schema:
physical_type: pickle pop pre_buffer properties __pyx_PickleError __pyx_checksum __pyx_result __pyx_state __pyx_type __pyx_vtable__ range read_dictionary record_batch recordbatchreader __repr__ result row_group_size 's constructor directly safe self
serialized_size: __set_name__ __setstate__ sink sort_key sort_keys sorting_column
sorting_columns: 'sorting_columns' must be a list of `SortingColumn` sp_arrow_schema __spec__ state staticmethod
statistics:
store_decimal_as_integer 'store_decimal_as_integer' must be a boolean store_schema _stringify_path __test__ textwrap thrift_container_size_limit thrift_container_size_limit must be larger than zero thrift_string_size_limit thrift_string_size_limit must be larger than zero tobytes
total_byte_size:
total_compressed_size:
total_uncompressed_size: update upper us use_byte_stream_split 'use_byte_stream_split' cannot be passedtogether with 'column_encoding' use_compliant_nested_type use_content_defined_chunking 'use_content_defined_chunking' should be either boolean or a dictionary use_deprecated_int96_timestamps use_dictionary use_memory_map use_setstate use_threads value version warn warnings where write_batch_size write_page_checksum write_page_index write_statistics writer_engine_version , xmax:
xmin: , ymax:
ymin: , zmax:
zmin: __module__ __reduce__ __name__ __qualname__ __dict__ _is_coroutine send throw close __reduce_ex__ items parquet-cpp-arrow version 22.0.0-SNAPSHOT N7parquet16WriterProperties7BuilderE NSt3__120__shared_ptr_emplaceIN5arrow4util12CodecOptionsENS_9allocatorIS3_EEEE N5arrow4util12CodecOptionsE genexpr N7parquet16ParquetExceptionE NSt3__120__shared_ptr_pointerIPN7parquet16WriterPropertiesENS_10shared_ptrIS2_E27__shared_ptr_default_deleteIS2_S2_EENS_9allocatorIS2_EEEE NSt3__110shared_ptrIN7parquet16WriterPropertiesEE27__shared_ptr_default_deleteIS2_S2_EE NSt3__120__shared_ptr_pointerIPN7parquet21ArrowWriterPropertiesENS_10shared_ptrIS2_E27__shared_ptr_default_deleteIS2_S2_EENS_9allocatorIS2_EEEE NSt3__110shared_ptrIN7parquet21ArrowWriterPropertiesEE27__shared_ptr_default_deleteIS2_S2_EE pyarrow.lib ParquetSchema.__reduce__(self) to_arrow_schema ParquetSchema.to_arrow_schema(self)
Convert Parquet schema to effective Arrow schema.
Returns
-------
schema : Schema equals ParquetSchema.equals(self, ParquetSchema other)
Return whether the two schemas are equal.
Parameters
----------
other : ParquetSchema
Schema to compare against.
Returns
-------
are_equal : bool column ParquetSchema.column(self, i)
Return the schema for a single column.
Parameters
----------
i : int
Index of column in schema.
Returns
-------
column_schema : ColumnSchema other container FileMetaData.__reduce__(self) to_dict FileMetaData.to_dict(self)
Get dictionary representation of the file metadata.
Returns
-------
dict
Dictionary with a key for each attribute of this class. 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 row_group 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 set_file_path 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. append_row_groups FileMetaData.append_row_groups(self, FileMetaData other)
Append row groups from other FileMetaData object.
Parameters
----------
other : FileMetaData
Other metadata to append row groups from. write_metadata_file 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. schema serialized_size num_columns num_rows num_row_groups format_version created_by metadata RowGroupMetaData.__reduce__(self) 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.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.to_dict(self)
Get dictionary representation of the row group metadata.
Returns
-------
dict
Dictionary with a key for each attribute of this class. total_byte_size sorting_columns ColumnChunkMetaData.to_dict(self)
Get dictionary representation of the column chunk metadata.
Returns
-------
dict
Dictionary with a key for each attribute of this class. 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 __reduce_cython__ ColumnChunkMetaData.__reduce_cython__(self) __setstate_cython__ ColumnChunkMetaData.__setstate_cython__(self, __pyx_state) file_offset file_path physical_type num_values path_in_schema is_stats_set statistics is_geo_stats_set geo_statistics compression encodings has_dictionary_page dictionary_page_offset data_page_offset total_compressed_size total_uncompressed_size wrap wrap(encoding_: 'ParquetEncoding') Statistics.to_dict(self)
Get dictionary representation of statistics.
Returns
-------
dict
Dictionary with a key for each attribute of this class. 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.__reduce_cython__(self) Statistics.__setstate_cython__(self, __pyx_state) has_min_max has_null_count has_distinct_count min max null_count distinct_count logical_type converted_type GeoStatistics.to_dict(self)
Dictionary summary of these statistics _x_valid GeoStatistics._x_valid(self) _y_valid GeoStatistics._y_valid(self) _z_valid GeoStatistics._z_valid(self) _m_valid GeoStatistics._m_valid(self) GeoStatistics.__reduce_cython__(self) GeoStatistics.__setstate_cython__(self, __pyx_state) geospatial_types xmin xmax ymin ymax zmin zmax mmin mmax to_json 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. ParquetLogicalType.__reduce_cython__(self) ParquetLogicalType.__setstate_cython__(self, __pyx_state) from_ordering 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 to_ordering 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.to_dict(self)
Get dictionary representation of the SortingColumn.
Returns
-------
dict
Dictionary with a key for each attribute of this class. SortingColumn.__reduce_cython__(self) SortingColumn.__setstate_cython__(self, __pyx_state) column_index descending nulls_first ColumnSchema.__reduce__(self) ColumnSchema.equals(self, ColumnSchema other)
Return whether the two column schemas are equal.
Parameters
----------
other : ColumnSchema
Schema to compare against.
Returns
-------
are_equal : bool path max_definition_level max_repetition_level length precision scale open 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 set_use_threads ParquetReader.set_use_threads(self, bool use_threads)
Parameters
----------
use_threads : bool set_batch_size ParquetReader.set_batch_size(self, int64_t batch_size)
Parameters
----------
batch_size : int64 iter_batches 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 read_row_group 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 read_row_groups 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 read_all 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 scan_contents 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 column_name_idx 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. read_column 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.close(self) ParquetReader.__reduce_cython__(self) ParquetReader.__setstate_cython__(self, __pyx_state) source decryption_properties row_groups closed memory_pool ParquetWriter.close(self) write_table ParquetWriter.write_table(self, Table table, row_group_size=None) add_key_value_metadata ParquetWriter.add_key_value_metadata(self, key_value_metadata) ParquetWriter.__reduce_cython__(self) ParquetWriter.__setstate_cython__(self, __pyx_state) table ListType LargeListType NativeFile _reconstruct_filemetadata _reconstruct_filemetadata(Buffer serialized) serialized __pyx_unpickle_SortingColumn __pyx_unpickle_SortingColumn(__pyx_type, long __pyx_checksum, __pyx_state) _parquet loader __loader__ origin __file__ parent __package__ submodule_search_locations __path__ Interpreter change detected - this module can only be loaded into one interpreter per process. Module '_parquet' has already been imported. Re-initialisation is not supported. builtins cython_runtime __builtins__ init pyarrow._parquet pyarrow/_parquet.pxd compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d does not match _cython_3_1_3amsendbackport Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling _cython_3_1_3amsendbackport._common_types_metatype _cython_3_1_3amsendbackport.cython_function_or_method <cyfunction %U at %p> unbound method %.200S() needs an argument keywords must be strings takes no arguments takes exactly one argument Bad call flags for CyFunction takes no keyword arguments %.200s() %s (%zd given) %.200s() %s __dictoffset__ __vectorcalloffset__ __weaklistoffset__ func_doc __doc__ func_name func_dict func_globals __globals__ func_closure __closure__ func_code __code__ func_defaults __defaults__ __kwdefaults__ __annotations__ __name__ must be set to a string object __qualname__ must be set to a string object function's dictionary may not be deleted setting function's dictionary to a non-dict __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 __annotations__ must be set to a dict object _cython_3_1_3amsendbackport.generator generator already executing name '%U' is not defined 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. generator ignored GeneratorExit while calling a Python object NULL result without error in PyObject_Call raise: arg 3 must be a traceback or None instance exception may not have a separate value calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException cannot pickle '%.200s' object gi_yieldfrom object being iterated by 'yield from', or None gi_code name of the generator qualified name of the generator gi_frame Frame of the generator gi_running _create_writer_properties std::shared_ptr<parquet::WriterProperties> (struct __pyx_opt_args_7pyarrow_8_parquet__create_writer_properties *__pyx_optional_args) _create_arrow_writer_properties std::shared_ptr<parquet::ArrowWriterProperties> (struct __pyx_opt_args_7pyarrow_8_parquet__create_arrow_writer_properties *__pyx_optional_args) _unwrap_list_type enum arrow::Type::type (PyObject *) __pyx_capi__ 'NoneType' object has no attribute '%.30s' str pyarrow._parquet._create_writer_properties basic_string pyarrow._parquet.check_compression_name pyarrow._parquet.compression_from_name dictionary changed size during iteration 'NoneType' object is not iterable need more than %zd value%.1s to unpack s too many values to unpack (expected %zd) string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_string Unknown exception value too large to convert to int an integer is required __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) pyarrow._parquet._convert_sorting_columns pyarrow._parquet._convert_sorting_columns.genexpr .0 local variable '%s' referenced before assignment generator raised StopIteration Missing type object Cannot convert %.200s to %.200s vector.from_py.__pyx_convert_vector_from_py_struct____parquet_3a__3a_SortingColumn vector a mapping FromPyStructUtility.__pyx_convert__from_py_struct____parquet_3a__3a_SortingColumn Can't use dictionary encoding as fallback encoding Expected %s, got %.200s pyarrow._parquet.encoding_enum_from_name strings are too large to concat value too large to convert to enum parquet::Encoding::type pyarrow._parquet._create_arrow_writer_properties pyarrow._parquet._unwrap_list_type _Weakrefable pyarrow._parquet.FileEncryptionProperties.wrap pyarrow._parquet.FileEncryptionProperties File-level encryption properties for the low-level API 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 multiple bases have vtable conflict: '%.200s' and '%.200s' invalid vtable found for imported 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 pyarrow._parquet.ParquetSchema A Parquet schema. pyarrow._parquet.ParquetSchema.__repr__ string.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_string join() result is too long for a Python string pyarrow._parquet.ParquetSchema.__getitem__ pyarrow._parquet.ParquetSchema.__hash__ pyarrow._parquet.ParquetSchema.__eq__ at least at most exactly %.200s() takes %.8s %zd positional argument%.1s (%zd given) %s() got an unexpected keyword argument '%U' pyarrow._parquet.ParquetSchema.__reduce__ pyarrow._parquet.ParquetSchema.to_arrow_schema pyarrow._parquet.ParquetSchema.equals %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings Argument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%U pyarrow._parquet.ParquetSchema.column names Name of each field (list of str). pyarrow._parquet.ParquetSchema.names.__get__ __cinit__ pyarrow._parquet.ParquetSchema.__cinit__ pyarrow._parquet.FileMetaData FileMetaData()
Parquet metadata for a single file. pyarrow._parquet.FileMetaData.__repr__ pyarrow._parquet.FileMetaData.__hash__ pyarrow._parquet.FileMetaData.__eq__ pyarrow._parquet.FileMetaData.__reduce__ pyarrow._parquet.FileMetaData.to_dict pyarrow._parquet.FileMetaData.equals pyarrow._parquet.FileMetaData.row_group pyarrow._parquet.FileMetaData.set_file_path pyarrow._parquet.FileMetaData.append_row_groups pyarrow._parquet.FileMetaData.write_metadata_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'.
Additional metadata as key value pairs (dict[bytes, bytes]). 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__ pyarrow._parquet.FileMetaData.format_version.__get__ 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 pyarrow._parquet.FileMetaData.created_by.__get__ pyarrow._parquet.FileMetaData.metadata.__get__ map.to_py.__pyx_convert_unordered_map_to_py_std_3a__3a_string____std_3a__3a_string __init__ pyarrow._parquet.FileMetaData.__init__ pyarrow._parquet.RowGroupMetaData.init pyarrow._parquet.RowGroupMetaData RowGroupMetaData()
Metadata for a single row group. pyarrow._parquet.RowGroupMetaData.__repr__ pyarrow._parquet.RowGroupMetaData.__eq__ pyarrow._parquet.RowGroupMetaData.__reduce__ pyarrow._parquet.RowGroupMetaData.equals pyarrow._parquet.RowGroupMetaData.column pyarrow._parquet.RowGroupMetaData.to_dict 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`)). pyarrow._parquet.RowGroupMetaData.num_columns.__get__ pyarrow._parquet.RowGroupMetaData.num_rows.__get__ pyarrow._parquet.RowGroupMetaData.total_byte_size.__get__ pyarrow._parquet.RowGroupMetaData.sorting_columns.__get__ pyarrow._parquet.RowGroupMetaData.__init__ pyarrow._parquet.ColumnChunkMetaData ColumnChunkMetaData()
Column metadata for a single row group. pyarrow._parquet.ColumnChunkMetaData.__repr__ pyarrow._parquet.ColumnChunkMetaData.__eq__ pyarrow._parquet.ColumnChunkMetaData.to_dict pyarrow._parquet.ColumnChunkMetaData.equals pyarrow._parquet.ColumnChunkMetaData.__reduce_cython__ pyarrow._parquet.ColumnChunkMetaData.__setstate_cython__ Offset into file where column chunk is located (int). Optional file path if set (str or None). Physical type of column (str). Total number of values (int). Nested path to field, separated by periods (str). 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). has_index_page Not yet supported. index_page_offset Compressed size in bytes (int). 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 pyarrow._parquet.ColumnChunkMetaData.file_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.file_path.__get__ pyarrow._parquet.ColumnChunkMetaData.physical_type.__get__ pyarrow._parquet.physical_type_name_from_enum pyarrow._parquet.ColumnChunkMetaData.num_values.__get__ pyarrow._parquet.ColumnChunkMetaData.path_in_schema.__get__ pyarrow._parquet.ColumnChunkMetaData.statistics.__get__ pyarrow._parquet.ColumnChunkMetaData.geo_statistics.__get__ pyarrow._parquet.ColumnChunkMetaData.compression.__get__ pyarrow._parquet.compression_name_from_enum pyarrow._parquet.ColumnChunkMetaData.encodings.__get__ cfunc.to_py.__Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_ cfunc.to_py.__Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_.wrap pyarrow._parquet.encoding_name_from_enum vector.to_py.__pyx_convert_vector_to_py_enum____parquet_3a__3a_Encoding_3a__3a_type pyarrow._parquet.ColumnChunkMetaData.has_dictionary_page.__get__ pyarrow._parquet.ColumnChunkMetaData.dictionary_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.data_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.has_index_page.__get__ pyarrow._parquet.ColumnChunkMetaData.index_page_offset.__get__ pyarrow._parquet.ColumnChunkMetaData.total_compressed_size.__get__ pyarrow._parquet.ColumnChunkMetaData.total_uncompressed_size.__get__ pyarrow._parquet.ColumnChunkMetaData.metadata.__get__ pyarrow._parquet.ColumnChunkMetaData.__init__ Unable to initialize pickling for %.200s pyarrow._parquet.Statistics Statistics()
Statistics for a single column in a single row group. pyarrow._parquet.Statistics.__repr__ pyarrow._parquet.Statistics.__eq__ pyarrow._parquet.Statistics.to_dict pyarrow._parquet.Statistics.equals pyarrow._parquet.Statistics.__reduce_cython__ pyarrow._parquet.Statistics.__setstate_cython__ Whether min and max are present (bool). Whether null count is present (bool). 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 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). Logical type of column (:class:`ParquetLogicalType`). Legacy converted type (str or None). pyarrow._parquet.Statistics.min_raw.__get__ pyarrow._parquet._cast_statistic_raw_min pyarrow._parquet._box_byte_array pyarrow._parquet._box_flba pyarrow._parquet.Statistics.max_raw.__get__ pyarrow._parquet._cast_statistic_raw_max pyarrow._parquet.Statistics.min.__get__ pyarrow._parquet._cast_statistics pyarrow._parquet.Statistics.max.__get__ pyarrow._parquet.Statistics.null_count.__get__ pyarrow._parquet.Statistics.distinct_count.__get__ pyarrow._parquet.Statistics.num_values.__get__ pyarrow._parquet.Statistics.physical_type.__get__ pyarrow._parquet.Statistics.logical_type.__get__ pyarrow._parquet.wrap_logical_type pyarrow._parquet.Statistics.converted_type.__get__ pyarrow._parquet.converted_type_name_from_enum pyarrow._parquet.Statistics.__init__ 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. pyarrow._parquet.GeoStatistics.__repr__ pyarrow._parquet.GeoStatistics.to_dict pyarrow._parquet.GeoStatistics.__reduce_cython__ pyarrow._parquet.GeoStatistics.__setstate_cython__ 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 pyarrow._parquet.GeoStatistics.geospatial_types.__get__ vector.to_py.__pyx_convert_vector_to_py_int32_t 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.GeoStatistics.__init__ pyarrow._parquet.FileDecryptionProperties.wrap pyarrow._parquet.FileDecryptionProperties File-level decryption properties for the low-level API pyarrow._parquet.ParquetLogicalType ParquetLogicalType()
Logical type of parquet type. pyarrow._parquet.ParquetLogicalType.__repr__ pyarrow._parquet.ParquetLogicalType.__str__ pyarrow._parquet.ParquetLogicalType.to_json pyarrow._parquet.ParquetLogicalType.__reduce_cython__ pyarrow._parquet.ParquetLogicalType.__setstate_cython__ type Name of the logical type (str). pyarrow._parquet.ParquetLogicalType.type.__get__ pyarrow._parquet.logical_type_name_from_enum pyarrow._parquet.ParquetLogicalType.__init__ 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 pyarrow._parquet.SortingColumn.__repr__ pyarrow._parquet.SortingColumn.__hash__ pyarrow._parquet.SortingColumn.from_ordering pyarrow._parquet._name_to_index_map cannot fit '%.200s' into an index-sized integer '%.200s' object is not subscriptable pyarrow._parquet.SortingColumn.to_ordering pyarrow._parquet.SortingColumn.to_dict pyarrow._parquet.SortingColumn.__reduce_cython__ pyarrow._parquet.SortingColumn.__setstate_cython__ tuple 'NoneType' object is not subscriptable pyarrow._parquet.__pyx_unpickle_SortingColumn__set_state hasattr(): attribute name must be string "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.SortingColumn.column_index.__get__ pyarrow._parquet.SortingColumn.__init__ pyarrow._parquet.ColumnSchema Schema for a single column. pyarrow._parquet.ColumnSchema.__repr__ pyarrow._parquet.ColumnSchema.__eq__ pyarrow._parquet.ColumnSchema.__reduce__ pyarrow._parquet.ColumnSchema.equals Name of field (str). Maximum definition level (int). Maximum repetition level (int). Name of physical type (str). 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.ColumnSchema.name.__get__ pyarrow._parquet.ColumnSchema.path.__get__ pyarrow._parquet.ColumnSchema.max_definition_level.__get__ pyarrow._parquet.ColumnSchema.max_repetition_level.__get__ pyarrow._parquet.ColumnSchema.physical_type.__get__ pyarrow._parquet.ColumnSchema.logical_type.__get__ pyarrow._parquet.ColumnSchema.converted_type.__get__ pyarrow._parquet.ColumnSchema.length.__get__ pyarrow._parquet.ColumnSchema.precision.__get__ pyarrow._parquet.ColumnSchema.scale.__get__ pyarrow._parquet.ColumnSchema.__cinit__ pyarrow._parquet.ParquetReader._set_read_dictionary pyarrow._parquet.ParquetReader pyarrow._parquet.ParquetReader.open Argument '%.200s' must not be None value too large to convert to int32_t pyarrow._parquet.ParquetReader.set_use_threads pyarrow._parquet.ParquetReader.set_batch_size pyarrow._parquet.ParquetReader.iter_batches can't send non-None value to a just-started generator pyarrow._parquet.ParquetReader.read_row_group pyarrow._parquet.ParquetReader.read_row_groups pyarrow._parquet.ParquetReader.read_all pyarrow._parquet.ParquetReader.scan_contents pyarrow._parquet.ParquetReader.column_name_idx pyarrow._parquet.ParquetReader.read_column pyarrow._parquet.ParquetReader.close pyarrow._parquet.ParquetReader.__reduce_cython__ pyarrow._parquet.ParquetReader.__setstate_cython__ column_paths schema_arrow _column_idx_map _column_idx_map: object pyarrow._parquet.ParquetReader.column_paths.__get__ pyarrow._parquet.ParquetReader.schema_arrow.__get__ pyarrow._parquet.ParquetReader.num_row_groups.__get__ pyarrow._parquet.ParquetReader.__cinit__ pyarrow._parquet.ParquetWriter pyarrow._parquet.ParquetWriter.close pyarrow._parquet.ParquetWriter.write_table pyarrow._parquet.ParquetWriter.add_key_value_metadata pyarrow._parquet.ParquetWriter.__reduce_cython__ pyarrow._parquet.ParquetWriter.__setstate_cython__ pyarrow._parquet.ParquetWriter.metadata.__get__ pyarrow._parquet.ParquetWriter.__cinit__ pyarrow._parquet.__pyx_scope_struct__iter_batches pyarrow._parquet.__pyx_scope_struct_1_genexpr pyarrow._parquet.__pyx_scope_struct____Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_ bool complex datetime date time timedelta tzinfo IpcWriteOptions IpcReadOptions Message MemoryPool DataType 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 Schema Scalar ArrayStatistics _PandasConvertible Array 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 BufferedInputStream BufferedOutputStream CompressedInputStream CompressedOutputStream _CRecordBatchWriter RecordBatchReader CacheOptions Codec StopToken check_status int (arrow::Status const &) maybe_unbox_memory_pool arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *) get_reader PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::RandomAccessFile> *) get_writer PyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream> *) string_to_timeunit enum arrow::TimeUnit::type (PyObject *) pyarrow_unwrap_metadata std::shared_ptr< arrow::KeyValueMetadata const > (PyObject *) pyarrow_wrap_schema PyObject *(std::shared_ptr< arrow::Schema> const &) pyarrow_wrap_scalar PyObject *(std::shared_ptr< arrow::Scalar> const &) pyarrow_wrap_chunked_array PyObject *(std::shared_ptr< arrow::ChunkedArray> const &) pyarrow_wrap_batch PyObject *(std::shared_ptr< arrow::RecordBatch> const &) pyarrow_wrap_table PyObject *(std::shared_ptr< arrow::Table> const &) pyarrow_unwrap_data_type std::shared_ptr< arrow::DataType> (PyObject *) pyarrow_unwrap_schema std::shared_ptr< arrow::Schema> (PyObject *) %.200s does not export expected C function %.200s C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s) cannot import name %S needs an argument pyarrow._parquet._reconstruct_filemetadata pyarrow._parquet.__pyx_unpickle_SortingColumn
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