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empty __class__ cellpyarrow.lib.DataType.fieldpyarrow.lib.UnionType.fieldfield_by_namepyarrow.lib.StructType.fieldpyarrow.lib.Table.join_asofpyarrow.lib.NativeFile.uploadpyarrow.lib.record_batch_reconstructdrop_columnspyarrow.lib.Scalar.castinit_rzinit_schemapyarrow.lib._Tabular.sort_bypyarrow.lib._as_c_pointergenerator already 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import name %Spyarrow.lib.Table._to_pandas__dataframe____pyx_unpickle__Tabular_get_pandas_tz_type__pyx_unpickle___Pyx_EnumMetapyarrow.lib.ChunkedArray.castpyarrow.lib._empty_array_datetime_from_int_detect_compressioncleanuptranscoding_input_streampyarrow.lib._get_pandas_typepyarrow.lib._to_pandas_dtypeNo module named '%U'pyarrow.lib.UnionArray.childpyarrow.lib.Array.formatcython_runtimedoes not match__orig_bases__getinit pyarrow.libpyarrow.lib._pacpyarrow.lib._pcaggregatepyarrow.lib.Array.castpyarrow.lib._is_primitivepyarrow.lib.unionpyarrow.lib.Table.__cinit__infer_dtypepyarrow.lib.Schema.__sizeof__.0genexpr_as_py_tupleto_pydictpyarrow.lib._from_pydict__pyx_unpickle__PandasAPIShimpyarrow.lib.Schema.__eq__pyarrow.lib.Field.__eq__pyarrow.lib._Tabular.__eq__pyarrow.lib.Array.__eq__pyarrow.lib.Scalar.__eq__pyarrow.lib._from_pylistpyarrow.lib.Table.__reduce___have_pandaspyarrow.lib.Table._assert_cpupyarrow.lib.RecordBatch.castget_field_indexstruct_typepyarrow.lib.OSFile.__cinit__pyarrow.lib.primitive_typepyarrow.lib.string_viewpyarrow.lib.binary_viewpyarrow.lib.large_stringpyarrow.lib.large_binarypyarrow.lib.stringpyarrow.lib.float64pyarrow.lib.float32pyarrow.lib.float16pyarrow.lib.date64pyarrow.lib.date32pyarrow.lib.int64pyarrow.lib.uint64pyarrow.lib.int32pyarrow.lib.uint32pyarrow.lib.int16pyarrow.lib.uint16pyarrow.lib.int8pyarrow.lib.uint8pyarrow.lib.bool_pyarrow.lib.nullpyarrow.lib.wrap_datumpep3118_formatpyarrow.lib.map_sp_fieldpyarrow.lib.durationpyarrow.lib.time64pyarrow.lib.time32pyarrow.lib.timestamppyarrow.lib.opaquetype_namevendor_namerandom_accesspyarrow.lib.StopToken.initpyarrow.lib.convert_statuspyarrow.lib.check_statuspyarrow.lib.get_writerget_output_streamwrite_tableresizepyarrow.lib.NativeFile.closesp_sparse_tensorto_scipyto_pydata_sparsesp_tensorpyarrow.lib.Tensor.to_numpypyarrow.lib.Table.validate_export_to_cpyarrow.lib.Array.validate_debug_printpyarrow.lib.Scalar.validate__arrow_c_schema__pyarrow.lib.Schema.to_stringUninitialized Result_download_nothreadspyarrow.lib.NativeFile.readmaximum_compression_levelminimum_compression_leveldefault_compression_levelpyarrow.lib.Array.__dlpack____dlpack_device__get_record_batch_sizepyarrow.lib.get_tensor_sizeset_io_thread_countpyarrow.lib.have_libhdfspyarrow.lib.Array.to_stringpyarrow.lib.string_to_tzinfounregister_extension_typejemalloc_set_decay_msset_timezone_db_pathpyarrow.lib.set_cpu_countrelease_registrypyarrow.lib.Scalar.initpyarrow.lib.Device.initpyarrow.lib.ChunkedArray.initpyarrow.lib.RecordBatch.initsp_tablepyarrow.lib.Table.initread_next_batchpyarrow.lib.Device.unwrappyarrow.lib.Scalar.unwrapfinishsp_memo_export_to_c_devicegetvaluepyarrow.lib.Field.initpyarrow.lib.Array.initpyarrow.lib.DataType.initpyarrow.lib.UnionType.inituuid_ext_typepyarrow.lib.UuidType.initpyarrow.lib.OpaqueType.initbool8_ext_typepyarrow.lib.Bool8Type.initcpy_ext_typefixed_size_binary_typepyarrow.lib.DurationType.initpyarrow.lib.Time64Type.initpyarrow.lib.Time32Type.initpyarrow.lib.StructType.initpyarrow.lib.MapType.initpyarrow.lib.ListViewType.initpyarrow.lib.ListType.initremove_metadatapyarrow.lib.Array.bufferspyarrow.lib.Buffer.initpyarrow.lib.list_pyarrow.lib.decimal256pyarrow.lib.decimal128__arrow_c_device_array____arrow_c_array__pyarrow.lib.Table.to_batchespyarrow.lib.NativeFile.flushpyarrow.lib.binarypyarrow.lib._cb_transformfrom_tensorpyarrow.lib.UnionArray.fieldget_random_access_filepyarrow.lib.NativeFile.sizepyarrow.lib.write_tensordesttypserialize_tosinkdownloadpyarrow.lib.Table.slicepyarrow.lib.NativeFile.tellpyarrow.lib.Array.__reduce__pyarrow.lib.RecordBatch.slicepyarrow.lib.Array.slicewith_nullablepyarrow.lib.Table._columnpyarrow.lib.get_readerpyarrow.lib.foreign_bufferread_atpyarrow.lib.Field.with_typenew_typepyarrow.lib.StructArray.fieldpyarrow.lib.Scalar.equalspyarrow.lib.Tensor.initpyarrow.lib.run_end_encodedreadintopyarrow.lib.Field.with_namepyarrow.lib.large_listpyarrow.lib.NativeFile.seekwrite_batchset_input_streamreplace_schema_metadatawith_metadatapyarrow.lib.Scalar.wrappyarrow.lib.large_list_viewpyarrow.lib.list_viewpyarrow.lib.Field.flattenpyarrow.lib.Schema.initpyarrow.lib._ndarray_to_typepyarrow.lib._ndarray_to_array_ndarray_to_arrow_type_import_from_c_capsule_import_from_cpyarrow.lib.infer_typepyarrow.lib.from_numpy_dtypepyarrow.lib.bool8pyarrow.lib.uuidget_all_field_indices_flattened_field_import_from_c_device_capsulepyarrow.lib.Array.copy_topyarrow.lib.Array.viewpyarrow.lib.repeatpyarrow.lib.nulls bytes, have pyarrow.lib._allocate_bufferpyarrow.lib.allocate_bufferpyarrow.lib.as_c_bufferpybufout_bufpyarrow.lib.Codec.decompresspyarrow.lib.Codec.compresspyarrow.lib.NativeFile.writepyarrow.lib.Buffer.sliceread_bufferpyarrow.lib.Schema.serializepyarrow.lib.py_bufferget_batch_import_from_c_devicepyarrow.lib.read_record_batchdictionary_memoto_tensorpyarrow.lib.read_tensorpyarrow.lib.tzinfo_to_stringpyarrow.lib.Schema.setpyarrow.lib.Schema.removepyarrow.lib.Schema.insertpyarrow.lib.read_schemapyarrow.lib.dictionarypyarrow.lib.Array.getitemunify_dictionariespyarrow.lib.arraypyarrow.lib.scalar_from_arraysread_allpyarrow.lib.Table.set_columnpyarrow.lib.Table.add_columnpyarrow.lib.Table.flattenpyarrow.lib.Table.to_readerdetachget_streamcreatepyarrow.lib.get_input_streampyarrow.lib._get_input_streamopen_streamset_output_streamunicodepyarrow.lib.Codec.__init__read_next_messagepyarrow.lib.read_messagefrom_batchespyarrow.lib.Table.from_pandaspyarrow.lib.fieldpyarrow.lib.Array.to_numpyvector::reservepyarrow.lib.Tensor.from_numpyrename_columnspyarrow.lib.unify_schemaspyarrow.lib.structpyarrow.lib.sparse_unionpyarrow.lib.dense_unionfrom_pydata_sparsevalue_typefrom_scipypyarrow.lib.concat_arrayspyarrow.lib.chunked_arraypyarrow.lib.concat_tablespromote_options_init_signalspyarrow.lib.Table.selectpyarrow.lib.Array._to_pandaspyarrow.lib._restore_arrayfrom_densechildrenfield_namestype_codesfrom_sparsepyarrow.lib.table_to_blockspyarrow.lib._sanitize_arrayspyarrow.lib.Table.from_arrayspyarrow.lib.schema_cython_3_0_11.generator__name__name of the generator__qualname__gi_frameFrame of the generatorgi_runninggi_yieldfromgi_code__module__sendfunc_doc__doc__func_namefunc_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutineCythonUnboundCMethodpyarrow.lib.__Pyx_EnumMetanum_record_batches_use_legacy_format_metadata_versionpyarrow.lib.MessageReader__next__pyarrow.lib.BufferReaderpyarrow.lib.MockOutputStreampyarrow.lib.OSFilepyarrow.lib.MemoryMappedFilepyarrow.lib.PythonFilepyarrow.lib.StringViewBuildernull_countpyarrow.lib.StringBuilderpyarrow.lib.Bool8Arraypyarrow.lib.OpaqueArrayrun_endspyarrow.lib.LargeBinaryArraytotal_values_lengthpyarrow.lib.LargeStringArraypyarrow.lib.DurationArraypyarrow.lib.Time64Arraypyarrow.lib.Time32Arraypyarrow.lib.TimestampArraypyarrow.lib.Date64Arraypyarrow.lib.Date32Arraypyarrow.lib.Bool8Scalarpyarrow.lib.OpaqueScalarpyarrow.lib.ExtensionScalarpyarrow.lib.UnionScalartype_codepyarrow.lib.DictionaryScalarpyarrow.lib.MapScalarpyarrow.lib.StructScalarpyarrow.lib.ListViewScalarpyarrow.lib.LargeListScalarpyarrow.lib.ListScalarpyarrow.lib.StringViewScalarpyarrow.lib.BinaryViewScalarpyarrow.lib.LargeStringScalarpyarrow.lib.StringScalarpyarrow.lib.LargeBinaryScalarpyarrow.lib.BinaryScalarpyarrow.lib.DurationScalarpyarrow.lib.TimestampScalarpyarrow.lib.Time64Scalarpyarrow.lib.Time32Scalarpyarrow.lib.Date64Scalarpyarrow.lib.Date32Scalarpyarrow.lib.Decimal256Scalarpyarrow.lib.Decimal128Scalarpyarrow.lib.DoubleScalarpyarrow.lib.FloatScalarpyarrow.lib.HalfFloatScalarpyarrow.lib.Int64Scalarpyarrow.lib.UInt64Scalarpyarrow.lib.Int32Scalarpyarrow.lib.UInt32Scalarpyarrow.lib.Int16Scalarpyarrow.lib.UInt16Scalarpyarrow.lib.Int8Scalarpyarrow.lib.UInt8Scalarpyarrow.lib.BooleanScalarpyarrow.lib.NullScalarpyarrow.lib.DenseUnionTypepyarrow.lib.SparseUnionTypepyarrow.lib.UnionTypemodepyarrow.lib.ProxyMemoryPoolpyarrow.lib.LoggingMemoryPoolLoggingMemoryPool()pyarrow.lib._PandasAPIShimcompatpyarrow.lib.SignalStopHandlerpyarrow.lib.StopTokenpyarrow.lib.CodecReturns the name of the codecpyarrow.lib.CacheOptionshole_size_limitrange_size_limitlazyprefetch_limitpyarrow.lib.RecordBatchReaderpyarrow.lib.NativeFileclosedpyarrow.lib.ResizableBufferpyarrow.lib.Bufferaddressis_mutabledevice_typepyarrow.lib.MemoryManagerpyarrow.lib.Devicedevice_idpyarrow.lib.RecordBatchnum_columnsnum_rowsnbytespyarrow.lib.Tablepyarrow.lib._Tabularcolumn_namespyarrow.lib.ChunkedArraynum_chunkspyarrow.lib.ExtensionArraypyarrow.lib.DictionaryArraypyarrow.lib.BinaryViewArraypyarrow.lib.StringViewArraypyarrow.lib.BinaryArraypyarrow.lib.StringArraypyarrow.lib.UnionArrayGet the type codes array.pyarrow.lib.MapArraysizespyarrow.lib.ListViewArraypyarrow.lib.LargeListArraypyarrow.lib.ListArraypyarrow.lib.BaseListArraypyarrow.lib.StructArraypyarrow.lib.Decimal256Arraypyarrow.lib.Decimal128Arraypyarrow.lib.DoubleArraypyarrow.lib.FloatArraypyarrow.lib.HalfFloatArraypyarrow.lib.UInt64Arraypyarrow.lib.Int64Arraypyarrow.lib.UInt32Arraypyarrow.lib.Int32Arraypyarrow.lib.UInt16Arraypyarrow.lib.Int16Arraypyarrow.lib.UInt8Arraypyarrow.lib.Int8Arraypyarrow.lib.IntegerArraypyarrow.lib.NumericArraypyarrow.lib.BooleanArrayfalse_counttrue_countpyarrow.lib.NullArraypyarrow.lib.SparseCSFTensorndimdim_namesnon_zero_lengthpyarrow.lib.SparseCOOTensorhas_canonical_formatpyarrow.lib.SparseCSCMatrixpyarrow.lib.SparseCSRMatrixpyarrow.lib.Tensoris_contiguouspyarrow.lib.Arraypyarrow.lib.Scalarpyarrow.lib.Schemapandas_metadatapyarrow.lib.Fieldpyarrow.lib.KeyValueMetadatapyarrow.lib._Metadatapyarrow.lib.PyExtensionTypepyarrow.lib.UuidTypepyarrow.lib.OpaqueTypepyarrow.lib.Bool8Typepermutationpyarrow.lib.ExtensionTypepyarrow.lib.BaseExtensionTypeextension_namebyte_widthbit_widthpyarrow.lib.RunEndEncodedTyperun_end_typepyarrow.lib.Decimal256Typeprecisionscalepyarrow.lib.Decimal128Typepyarrow.lib.DurationTypepyarrow.lib.Time64Typepyarrow.lib.Time32Typepyarrow.lib.TimestampTypepyarrow.lib.DictionaryTypeorderedindex_typepyarrow.lib.DictionaryMemopyarrow.lib.StructTypepyarrow.lib.FixedSizeListTypevalue_fieldlist_sizepyarrow.lib.MapTypekey_fieldkey_typeitem_fielditem_typekeys_sortedpyarrow.lib.LargeListViewTypepyarrow.lib.ListViewTypepyarrow.lib.LargeListTypepyarrow.lib.ListTypepyarrow.lib.DataTypenum_fieldsnum_bufferspyarrow.lib.MemoryPoolbackend_namepyarrow.lib.Messagebodypyarrow.lib.IpcReadOptionsensure_native_endianuse_threadspyarrow.lib.IpcWriteOptionsallow_64bitemit_dictionary_deltaspyarrow.lib._Weakrefablebg_write_register_py_extension_typemonth_day_nano_interval_get_pandas_type_mapdefault_cpu_memory_managersupported_memory_backendstotal_allocated_bytesmimalloc_memory_pooljemalloc_memory_poolsystem_memory_pooldefault_memory_pool_gdb_test_session_ensure_cuda_loadedis_threading_enabled%.200s() takes %.8s %zd positional argument%.1s (%zd given)need more than %zd value%.1s to unpack%.200s() takes no keyword arguments%.200s() takes exactly one argument (%zd given)%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes no arguments (%zd given) while calling a Python objectNULL result without error in PyObject_Call__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.__%.4s__ returned non-%.4s (type %.200s)metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesPyObject *(arrow::Status const &) arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *)PyObject *( arrow::MemoryPool *)PyObject *( arrow::Datum const &)PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::InputStream> *)PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::RandomAccessFile> *)PyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream> *)struct __pyx_obj_7pyarrow_3lib_NativeFile *(PyObject *, bool)std::shared_ptr< arrow::io::InputStream> (std::shared_ptr< arrow::io::InputStream> , PyObject *, PyObject *)native_transcoding_input_streamstd::shared_ptr > (PyObject *, PyObject *)struct __pyx_obj_7pyarrow_3lib_DataType *(PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7pyarrow_3lib_ensure_type *__pyx_optional_args)PyObject *(enum arrow::TimeUnit::type)enum arrow::TimeUnit::type (PyObject *)std::shared_ptr< arrow::KeyValueMetadata const > (PyObject *)PyObject *(std::shared_ptr< arrow::KeyValueMetadata const > const &)PyObject *(std::shared_ptr< arrow::Buffer> const &)PyObject *(std::shared_ptr< arrow::ResizableBuffer> const &)PyObject *(std::shared_ptr< arrow::DataType> const &)PyObject *(std::shared_ptr< arrow::Field> const &)PyObject *(std::shared_ptr< arrow::Schema> const &)PyObject *(std::shared_ptr< arrow::Scalar> const &)PyObject *(std::shared_ptr< arrow::Array> const &)PyObject *(std::shared_ptr< arrow::ChunkedArray> const &)PyObject *(std::shared_ptr< arrow::SparseCOOTensor> const &)pyarrow_wrap_sparse_coo_tensorPyObject *(std::shared_ptr< arrow::SparseCSCMatrix> const &)pyarrow_wrap_sparse_csc_matrixPyObject *(std::shared_ptr< arrow::SparseCSFTensor> const &)pyarrow_wrap_sparse_csf_tensorPyObject *(std::shared_ptr< arrow::SparseCSRMatrix> const &)pyarrow_wrap_sparse_csr_matrixPyObject *(std::shared_ptr< arrow::Tensor> const &)PyObject *(std::shared_ptr< arrow::RecordBatch> const &)PyObject *(std::shared_ptr< arrow::Table> const &)std::shared_ptr< arrow::Buffer> (PyObject *)std::shared_ptr< arrow::DataType> (PyObject *)std::shared_ptr< arrow::Field> (PyObject *)std::shared_ptr< arrow::Schema> (PyObject *)std::shared_ptr< arrow::Scalar> (PyObject *)std::shared_ptr< arrow::Array> (PyObject *)std::shared_ptr< arrow::ChunkedArray> (PyObject *)std::shared_ptr< arrow::SparseCOOTensor> (PyObject *)pyarrow_unwrap_sparse_coo_tensorstd::shared_ptr< arrow::SparseCSCMatrix> (PyObject *)pyarrow_unwrap_sparse_csc_matrixstd::shared_ptr< arrow::SparseCSFTensor> (PyObject *)pyarrow_unwrap_sparse_csf_tensorstd::shared_ptr< arrow::SparseCSRMatrix> (PyObject *)pyarrow_unwrap_sparse_csr_matrixstd::shared_ptr< arrow::Tensor> (PyObject *)std::shared_ptr< arrow::RecordBatch> (PyObject *)std::shared_ptr< arrow::Table> (PyObject *)pyarrow_internal_convert_statusbase class '%.200s' is not a heap typeextension 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 typeInterpreter change detected - this module can only be loaded into one interpreter per process.unbound method %.200S() needs an argumentif _cython_generator_type is not None: try: Generator = _module.Generator except AttributeError: pass else: Generator.register(_cython_generator_type) if _cython_coroutine_type is not None: try: Coroutine = _module.Coroutine except AttributeError: pass else: Coroutine.register(_cython_coroutine_type) Cython module failed to patch module with custom typeCython module failed to register with collections.abc module%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectcan't send non-None value to a just-started generatorShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilinginvalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'join() result is too long for a Python string__annotations__ must be set to a dict object__name__ must be set to a string object__qualname__ must be set to a string object__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function callsfunction's dictionary may not be deletedsetting function's dictionary to a non-dictCannot convert %.200s to %.200shasattr(): attribute name must be stringArgument '%.200s' has incorrect type (expected %.200s, got %.200s)raise: arg 3 must be a traceback or Noneinstance exception may not have a separate valueraise: exception class must be a subclass of BaseExceptioncalling %R should have returned an instance of BaseException, not %Rexception causes must derive from BaseExceptiontoo many values to unpack (expected %zd)'NoneType' object is not iterabledictionary changed size during iteration'%.200s' object is unsliceable'NoneType' object has no attribute '%.30s'pyarrow.lib.SignalStopHandler.__dealloc__pyarrow.lib.dlpack_pycapsule_deleterpyarrow.lib.pycapsule_array_deleterpyarrow.lib.pycapsule_device_array_deleterpyarrow.lib.pycapsule_schema_deleterpyarrow.lib.pycapsule_stream_deleter%s() got multiple values for keyword argument '%U'cannot fit '%.200s' into an index-sized 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view==NULL argument is 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construction from null is not validpyarrow.lib.OpaqueType.type_name.__get__pyarrow.lib.KeyValueMetadata.to_dictpyarrow.lib.TimestampType.tz.__get__pyarrow.lib.ChunkedArray.__reduce__pyarrow.lib.MemoryManager.__repr__pyarrow.lib._Tabular.drop_nullpyarrow.lib.Array.value_countsstrings are too large to concatpyarrow.lib.Tensor._make_shape_or_strides_bufferpyarrow.lib.ChunkedArray.is_nanpyarrow.lib.ChunkedArray.drop_nullpyarrow.lib.ChunkedArray.is_validpyarrow.lib.ChunkedArray.fill_nullpyarrow.lib.ChunkedArray.filterpyarrow.lib.ChunkedArray.value_countspyarrow.lib.ChunkedArray.uniquepyarrow.lib.log_memory_allocationspyarrow.lib.BaseListArray.flattenpyarrow.lib._Tabular.__getitem__pyarrow.lib.Decimal256Scalar.as_pypyarrow.lib.Decimal128Scalar.as_pypyarrow.lib.Array.dictionary_encodepyarrow.lib.ChunkedArray.dictionary_encodepyarrow.lib._Tabular.__array__pyarrow.lib.NativeFile._upload_nothreadspyarrow.lib.ChunkedArray.__getitem__pyarrow.lib.KeyValueMetadata.__eq__pyarrow.lib.RunEndEncodedArray.from_arrayspyarrow.lib.ArrowCancelled.__init__value too large to convert to int8_tpyarrow.lib.Time64Scalar.as_pypyarrow.lib.Time32Scalar.as_pypyarrow.lib.Field.name.__get__pyarrow.lib.Buffer.__getitem__pyarrow.lib.RecordBatch._assert_cpupyarrow.lib._unregister_py_extension_typesargument after ** must be a mapping, not NoneTypepyarrow.lib.CacheOptions._reconstructpyarrow.lib._Tabular.drop_columnspyarrow.lib.MockOutputStream.sizepyarrow.lib.ChunkedArray.lengthpyarrow.lib.pyarrow_wrap_bufferpyarrow.lib.UnionArray.offsets.__get__pyarrow.lib.UnionArray.type_codes.__get__pyarrow.lib.pyarrow_wrap_resizable_bufferpyarrow.lib.pyarrow_wrap_schemapyarrow.lib.RecordBatch.schema.__get__pyarrow.lib.Table.schema.__get__can't convert negative value to size_tpyarrow.lib.Table.is_cpu.__get__.genexprpyarrow.lib.Table.is_cpu.__get__pyarrow.lib.ChunkedArray.combine_chunkspyarrow.lib.UnknownExtensionType.__init__can't convert negative value to uint64_tpyarrow.lib._PandasAPIShim._check_importpyarrow.lib._PandasAPIShim._have_pandas_internalpyarrow.lib._PandasAPIShim.get_rangeindex_attributepyarrow.lib._PandasAPIShim.is_extension_array_dtypepyarrow.lib._PandasAPIShim.is_array_likepyarrow.lib._PandasAPIShim.is_ge_v3pyarrow.lib._PandasAPIShim.is_ge_v21pyarrow.lib._PandasAPIShim.is_v1pyarrow.lib._PandasAPIShim.data_framepyarrow.lib._PandasAPIShim.seriespyarrow.lib._PandasAPIShim.extension_dtype.__get__pyarrow.lib._PandasAPIShim.datetimetz_type.__get__pyarrow.lib._PandasAPIShim.categorical_type.__get__pyarrow.lib._PandasAPIShim.version.__get__pyarrow.lib._PandasAPIShim.loose_version.__get__pyarrow.lib._PandasAPIShim.pd.__get__pyarrow.lib._PandasAPIShim.compat.__get__pyarrow.lib._PandasAPIShim.have_pandas.__get__pyarrow.lib._PandasAPIShim.is_seriespyarrow.lib._PandasAPIShim.is_categoricalpyarrow.lib._PandasAPIShim.is_indexpyarrow.lib._PandasAPIShim.is_data_framepyarrow.lib._PandasAPIShim.is_datetimetzpyarrow.lib._PandasAPIShim.is_sparsepyarrow.lib._Tabular.from_pydictpyarrow.lib._Tabular.from_pylistEnumBase.__Pyx_FlagBase.__new__pyarrow.lib.TimestampScalar.__repr__pyarrow.lib.RunEndEncodedArray.find_physical_lengthpyarrow.lib.RunEndEncodedArray.find_physical_offsetpyarrow.lib.Message.type.__get__pyarrow.lib.MemoryPool.backend_name.__get__pyarrow.lib.BaseExtensionType.extension_name.__get__pyarrow.lib._PandasConvertible.to_pandaspyarrow.lib._RecordBatchStreamReader.stats.__get__pyarrow.lib._RecordBatchFileReader.stats.__get__pyarrow.lib._PandasAPIShim.pandas_dtypepyarrow.lib.CacheOptions.from_network_metricspyarrow.lib.MemoryPool.bytes_allocatedpyarrow.lib._CRecordBatchWriter.stats.__get__pyarrow.lib.NativeFile.upload.bg_writepyarrow.lib.BinaryScalar.as_bufferpyarrow.lib.KeyValueMetadata.valuepyarrow.lib.KeyValueMetadata.keypyarrow.lib.DataType.bit_width.__get__pyarrow.lib.UInt64Scalar.as_pypyarrow.lib.HalfFloatScalar.as_pypyarrow.lib.UInt16Scalar.as_pypyarrow.lib.DoubleScalar.as_pypyarrow.lib.UInt32Scalar.as_pygenerator ignored GeneratorExitpyarrow.lib.pyarrow_wrap_data_typepyarrow.lib.ListType.value_type.__get__pyarrow.lib.LargeListType.value_type.__get__pyarrow.lib.ListViewType.value_type.__get__pyarrow.lib.LargeListViewType.value_type.__get__pyarrow.lib.FixedSizeListType.value_type.__get__pyarrow.lib.DictionaryType.index_type.__get__pyarrow.lib.DictionaryType.value_type.__get__pyarrow.lib.RunEndEncodedType.run_end_type.__get__pyarrow.lib.RunEndEncodedType.value_type.__get__pyarrow.lib.BaseExtensionType.storage_type.__get__pyarrow.lib.FixedShapeTensorType.value_type.__get__pyarrow.lib.Scalar.type.__get__pyarrow.lib.ChunkedArray.type.__get__pyarrow.lib.get_scalar_class_from_typepyarrow.lib.pyarrow_wrap_scalarpyarrow.lib.pyarrow_wrap_arraypyarrow.lib.get_array_class_from_typepyarrow.lib.ListArray.values.__get__pyarrow.lib.LargeListArray.values.__get__pyarrow.lib.ListViewArray.values.__get__pyarrow.lib.LargeListViewArray.values.__get__pyarrow.lib.MapArray.keys.__get__pyarrow.lib.MapArray.items.__get__pyarrow.lib.FixedSizeListArray.values.__get__pyarrow.lib.DictionaryArray.dictionary.__get__pyarrow.lib.DictionaryArray.indices.__get__pyarrow.lib.ExtensionArray.storage.__get__pyarrow.lib.ListScalar.values.__get__pyarrow.lib.DictionaryScalar.dictionary.__get__pyarrow.lib.RunEndEncodedArray.run_ends.__get__pyarrow.lib.RunEndEncodedArray.values.__get__pyarrow.lib.Codec.name.__get__pyarrow.lib.RecordBatch._is_initializedpyarrow.lib.Table._is_initializedpyarrow.lib.FixedShapeTensorArray.from_numpy_ndarrayEnumBase.__Pyx_EnumBase.__str__EnumBase.__Pyx_FlagBase.__str__pyarrow.lib.CacheOptions.__eq__pyarrow.lib.ChunkedArray.indexpyarrow.lib.Table.to_struct_arraypyarrow.lib.Bool8Array.from_numpypyarrow.lib.KeyValueMetadata.__str__pyarrow.lib.pyarrow_wrap_tensorpyarrow.lib.UuidType.__arrow_ext_class__pyarrow.lib.UuidType.__arrow_ext_scalar_class__pyarrow.lib.RecordBatch.get_total_buffer_sizepyarrow.lib.Array.get_total_buffer_sizepyarrow.lib.Table.get_total_buffer_sizepyarrow.lib.RecordBatchReader.from_streamvalue too large to convert to intpyarrow.lib._Tabular.remove_columnpyarrow.lib.FixedSizeBufferWriter.set_memcopy_threadspyarrow.lib.SparseCSCMatrix.dim_namepyarrow.lib.SparseCSFTensor.dim_namepyarrow.lib.SparseCOOTensor.dim_namepyarrow.lib.SparseCSRMatrix.dim_namepyarrow.lib._Tabular.add_columnpyarrow.lib.ChunkedArray.chunkpyarrow.lib.StringArray.from_bufferspyarrow.lib.LargeStringArray.from_buffersvalue too large to convert to int32_tvalue too large to convert to enum __pyx_t_7pyarrow_3lib_MetadataVersionpyarrow.lib._unwrap_metadata_versionpyarrow.lib.IpcWriteOptions.metadata_version.__set__value too large to convert to enum arrow::TimeUnit::typevalue too large to convert to enum arrow::Type::typepyarrow.lib.RunEndEncodedArray.from_bufferspyarrow.lib._RecordBatchFileReader.__exit__pyarrow.lib.MemoryPool.max_memorypyarrow.lib.KeyValueMetadata.get_allpyarrow.lib.Buffer.__reduce_ex__pyarrow.lib.ChunkedArray.is_nullpyarrow.lib._Tabular.__dataframe__pyarrow.lib.__pyx_unpickle__Tabular__pyx_unpickle__PandasConvertiblepyarrow.lib.__pyx_unpickle__PandasConvertiblepyarrow.lib.Schema.from_pandaspyarrow.lib._get_pandas_tz_typeEnumBase.__pyx_unpickle___Pyx_EnumMetapyarrow.lib._datetime_from_intpyarrow.lib._detect_compressionpyarrow.lib.DurationScalar.as_pypyarrow.lib.TimestampScalar.as_pypyarrow.lib.BaseExtensionType.byte_width.__get__pyarrow.lib.BaseExtensionType.bit_width.__get__pyarrow.lib.BooleanScalar.as_pypyarrow.lib.IpcWriteOptions.compression.__get__pyarrow.lib.NativeFile.download.bg_writepyarrow.lib.PyExtensionType.__arrow_ext_deserialize__pyarrow.lib.pyarrow_wrap_sparse_csr_matrixpyarrow.lib.pyarrow_wrap_sparse_coo_tensorpyarrow.lib.pyarrow_wrap_sparse_csc_matrixpyarrow.lib.pyarrow_wrap_sparse_csf_tensorEnumBase.__Pyx_EnumBase.__new__pyarrow.lib._CRecordBatchWriter.__exit__pyarrow.lib.NativeFile.__exit__pyarrow.lib.transcoding_input_streampyarrow.lib.RecordBatchReader.__exit__pyarrow.lib.Date32Scalar.as_pypyarrow.lib.ChunkedArray.formatpyarrow.lib.ChunkedArray.data.__get__Module 'lib' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%dbest base '%.200s' must be equal to first base '%.200s'pyarrow.lib.TableGroupBy.aggregatepyarrow.lib._Tabular.to_stringpyarrow.lib.Date64Scalar.as_pypyarrow.lib.ChunkedArray.__array__pyarrow.lib.pyarrow_wrap_tablepyarrow.lib._PandasAPIShim.infer_dtypegenerator raised StopIterationlocal variable '%s' referenced before assignmentpyarrow.lib.Bool8Array.to_numpypyarrow.lib.NativeFile.__cinit__'%.200s' object is not subscriptableEnumBase.__Pyx_EnumMeta.__getitem__pyarrow.lib.StructScalar._as_py_tuplepyarrow.lib.StructScalar.as_pypyarrow.lib._Tabular.to_pydictpyarrow.lib._Tabular.to_pylistpyarrow.lib.Schema.pandas_metadata.__get__pyarrow.lib.MapScalar.__getitem__pyarrow.lib.__pyx_unpickle__PandasAPIShim__set_statepyarrow.lib._PandasAPIShim.__setstate_cython__pyarrow.lib.__pyx_unpickle__PandasAPIShimpyarrow.lib.ChunkedArray.__eq__pyarrow.lib._PandasAPIShim.get_valuespyarrow.lib.ChunkedArray.__cinit__pyarrow.lib.pyarrow_wrap_chunked_arraypyarrow.lib._PandasAPIShim._import_pandaspyarrow.lib._ensure_cuda_loadedpyarrow.lib._PandasConvertible.__reduce_cython__EnumBase.__Pyx_EnumMeta.__reduce_cython__pyarrow.lib._Tabular.__reduce_cython__pyarrow.lib.OpaqueType.vendor_name.__get__string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_stringpyarrow.lib.Schema.get_field_indexpyarrow.lib.KeyValueMetadata.__contains__pyarrow.lib.StructType.get_field_indexpyarrow.lib.month_day_nano_intervalpyarrow.lib.BufferReader.__cinit__pyarrow.lib.PythonFile.__cinit__pyarrow.lib.Tensor.__getbuffer__pyarrow.lib.make_streamwrap_funcpyarrow.lib.NativeFile.get_random_access_filepyarrow.lib.default_cpu_memory_managerpyarrow.lib.BaseExtensionType.wrap_arraypyarrow.lib.pyarrow_internal_check_statuspyarrow.lib.pyarrow_internal_convert_statuspyarrow.lib.Message.metadata.__get__pyarrow.lib.LargeListViewArray.offsets.__get__pyarrow.lib.LargeListViewArray.sizes.__get__pyarrow.lib.ListViewArray.sizes.__get__pyarrow.lib.ListViewArray.offsets.__get__pyarrow.lib.ListArray.offsets.__get__pyarrow.lib.LargeListArray.offsets.__get__pyarrow.lib.ResizableBuffer.init_rzpyarrow.lib.RecordBatchReader.__arrow_c_stream__pyarrow.lib.RecordBatchReader.closepyarrow.lib._CRecordBatchWriter.closepyarrow.lib._CRecordBatchWriter.write_tablepyarrow.lib.ResizableBuffer.resizepyarrow.lib.MemoryMappedFile.resizepyarrow.lib.SparseCSFTensor.to_numpypyarrow.lib.SparseCSCMatrix.to_scipypyarrow.lib.SparseCSCMatrix.to_numpypyarrow.lib.SparseCSRMatrix.to_scipypyarrow.lib.SparseCSRMatrix.to_numpypyarrow.lib.SparseCOOTensor.to_pydata_sparsepyarrow.lib.SparseCOOTensor.to_scipypyarrow.lib.SparseCOOTensor.to_numpypyarrow.lib.RecordBatch._export_to_cpyarrow.lib.RecordBatch.validatepyarrow.lib.ChunkedArray.__arrow_c_stream__pyarrow.lib.ChunkedArray.validatepyarrow.lib.Array._export_to_cpyarrow.lib.Array._debug_printpyarrow.lib.Schema.__arrow_c_schema__pyarrow.lib.Schema._export_to_cpyarrow.lib.Field.__arrow_c_schema__pyarrow.lib.Field._export_to_cpyarrow.lib.DataType.__arrow_c_schema__pyarrow.lib.DataType._export_to_cpyarrow.lib.SignalStopHandler.__enter__pyarrow.lib.ChunkedArray.to_stringpyarrow.lib.SignalStopHandler.__cinit__pyarrow.lib.RecordBatch.nbytes.__get__pyarrow.lib.Table.nbytes.__get__pyarrow.lib.ChunkedArray.nbytes.__get__pyarrow.lib.Array.nbytes.__get__pyarrow.lib.MonthDayNanoIntervalScalar.as_pypyarrow.lib.MonthDayNanoIntervalArray.to_pylistpyarrow.lib.NativeFile._download_nothreadspyarrow.lib.Codec.maximum_compression_levelpyarrow.lib.Codec.minimum_compression_levelpyarrow.lib.Codec.default_compression_levelpyarrow.lib.Array.__dlpack_device__pyarrow.lib.get_record_batch_sizepyarrow.lib.set_io_thread_countpyarrow.lib._register_py_extension_typepyarrow.lib.unregister_extension_typepyarrow.lib.jemalloc_set_decay_mspyarrow.lib.mimalloc_memory_poolpyarrow.lib.jemalloc_memory_poolpyarrow.lib.set_timezone_db_pathpyarrow.lib.SignalStopHandler.__exit__pyarrow.lib._ExtensionRegistryNanny.release_registrypyarrow.lib.MapType.key_field.__get__pyarrow.lib.MapType.item_field.__get__pyarrow.lib.MemoryManager.initpyarrow.lib.Schema.init_schemapyarrow.lib.KeyValueMetadata.initpyarrow.lib.NativeFile.set_output_streampyarrow.lib.RecordBatchReader.read_next_batchpyarrow.lib.MemoryManager.unwrappyarrow.lib.KeyValueMetadata.unwrappyarrow.lib.StringViewBuilder.finishpyarrow.lib.StringBuilder.finishpyarrow.lib.DictionaryMemo.__cinit__pyarrow.lib.RecordBatchReader._export_to_cpyarrow.lib.Array._export_to_c_devicepyarrow.lib.RecordBatch._export_to_c_devicepyarrow.lib.MapType.item_type.__get__pyarrow.lib.MapType.key_type.__get__pyarrow.lib.NativeFile.__dealloc__pyarrow.lib.BufferOutputStream.getvaluepyarrow.lib.NativeFile.set_input_streampyarrow.lib.MockOutputStream.__cinit__pyarrow.lib._datatype_to_pep3118pyarrow.lib.BaseExtensionType.initpyarrow.lib.FixedShapeTensorType.initpyarrow.lib.ExtensionType.initpyarrow.lib.RunEndEncodedType.initpyarrow.lib.FixedSizeBinaryType.initpyarrow.lib.Decimal256Type.initpyarrow.lib.Decimal128Type.initpyarrow.lib.TimestampType.initpyarrow.lib.DictionaryType.initpyarrow.lib.FixedSizeListType.initpyarrow.lib.LargeListViewType.initpyarrow.lib.LargeListType.initpyarrow.lib.Message.body.__get__pyarrow.lib.RecordBatchReader.schema.__get__pyarrow.lib._ExtensionRegistryNanny.__cinit__pyarrow.lib.Buffer.parent.__get__pyarrow.lib.FixedSizeBufferWriter.__cinit__pyarrow.lib.Field.metadata.__get__pyarrow.lib.Field.remove_metadatapyarrow.lib.Schema.remove_metadatapyarrow.lib._append_array_bufferspyarrow.lib.NativeFile.set_random_access_filepyarrow.lib.SparseCOOTensor.initpyarrow.lib.SparseCSRMatrix.initpyarrow.lib.SparseCSCMatrix.initpyarrow.lib.SparseCSFTensor.initcannot create std::vector larger than max_size()pyarrow.lib.pyarrow_unwrap_sparse_csr_matrixpyarrow.lib.pyarrow_unwrap_chunked_arraypyarrow.lib.pyarrow_unwrap_data_typepyarrow.lib.pyarrow_unwrap_sparse_csf_tensorpyarrow.lib.pyarrow_unwrap_bufferpyarrow.lib.c_mask_inverted_from_objpyarrow.lib.pyarrow_unwrap_batchpyarrow.lib.RecordBatch.__arrow_c_device_array__pyarrow.lib.RecordBatch.__arrow_c_array__pyarrow.lib.pyarrow_unwrap_schemapyarrow.lib.pyarrow_unwrap_tablepyarrow.lib.pyarrow_unwrap_tensorpyarrow.lib.pyarrow_unwrap_arraypyarrow.lib.Array.__arrow_c_device_array__pyarrow.lib.Array.__arrow_c_array__pyarrow.lib.pyarrow_unwrap_sparse_csc_matrixpyarrow.lib.pyarrow_unwrap_sparse_coo_tensorpyarrow.lib.pyarrow_unwrap_fieldpyarrow.lib._wrap_record_batch_with_metadatapyarrow.lib.SparseCSFTensor.from_tensorpyarrow.lib.SparseCSRMatrix.from_tensorpyarrow.lib.SparseCSCMatrix.from_tensorpyarrow.lib.SparseCOOTensor.from_tensorpyarrow.lib.NullScalar.__cinit__pyarrow.lib.pyarrow_unwrap_scalarpyarrow.lib.ExtensionScalar.from_storagepyarrow.lib.ChunkedArray.slicepyarrow.lib.NativeFile.get_output_streampyarrow.lib.NativeFile.get_input_streampyarrow.lib.Message.serialize_topyarrow.lib.NativeFile.downloadpyarrow.lib.register_extension_typepyarrow.lib.RecordBatch.equalspyarrow.lib._reduce_array_datapyarrow.lib.IpcWriteOptions.__init__pyarrow.lib.ExtensionType.__init__pyarrow.lib.Field.with_nullablepyarrow.lib.RecordBatch._columnpyarrow.lib.NativeFile.read_atpyarrow.lib.NativeFile.readintopyarrow.lib.pyarrow_unwrap_metadatapyarrow.lib._CRecordBatchWriter.write_batchpyarrow.lib.TransformInputStream.__init__pyarrow.lib.Table.replace_schema_metadatapyarrow.lib.RecordBatch.replace_schema_metadatapyarrow.lib.Field.with_metadatapyarrow.lib.Schema.with_metadatapyarrow.lib.ExtensionScalar.value.__get__pyarrow.lib.UnionScalar.value.__get__pyarrow.lib.RunEndEncodedScalar.value.__get__pyarrow.lib.DictionaryScalar.index.__get__pyarrow.lib.ExtensionArray.from_storagepyarrow.lib.Schema.field_by_namepyarrow.lib.StructType.field_by_namepyarrow.lib._ndarray_to_arrow_typepyarrow.lib.DataType._import_from_c_capsulepyarrow.lib.DataType._import_from_cpyarrow.lib.Schema.get_all_field_indicespyarrow.lib.StructType.get_all_field_indicespyarrow.lib.FixedShapeTensorType.permutation.__get__pyarrow.lib.StructArray._flattened_fieldpyarrow.lib.Array._import_from_c_device_capsulepyarrow.lib.Array._import_from_c_capsulepyarrow.lib.supported_memory_backendsarray cannot contain more than pyarrow.lib.StringBuilder.appendpyarrow.lib.BufferOutputStream.__cinit__pyarrow.lib.NativeFile.read_bufferpyarrow.lib.RecordBatch.serializepyarrow.lib._RecordBatchFileReader.get_batchpyarrow.lib.RecordBatch._import_from_c_device_capsulepyarrow.lib.RecordBatch._import_from_c_devicepyarrow.lib.RecordBatch._import_from_c_capsulepyarrow.lib.RecordBatch._import_from_cpyarrow.lib.RecordBatch.from_struct_arraypyarrow.lib.RecordBatch.set_columnpyarrow.lib.RecordBatch.remove_columnpyarrow.lib.RecordBatch.add_columnpyarrow.lib.RecordBatch.copy_topyarrow.lib.SparseCSFTensor.to_tensorpyarrow.lib.SparseCSCMatrix.to_tensorpyarrow.lib.SparseCSRMatrix.to_tensorpyarrow.lib.SparseCOOTensor.to_tensorpyarrow.lib.RecordBatch.to_tensorpyarrow.lib.FixedShapeTensorScalar.to_tensorpyarrow.lib.FixedShapeTensorArray.to_tensorpyarrow.lib.KeyValueMetadata.__getitem__pyarrow.lib.Field._import_from_c_capsulepyarrow.lib.Field._import_from_cpyarrow.lib.Schema._import_from_c_capsulepyarrow.lib.Schema._import_from_cpyarrow.lib.DictionaryScalar.value.__get__pyarrow.lib.ChunkedArray.getitempyarrow.lib.ChunkedArray._import_from_c_capsulepyarrow.lib.ChunkedArray.unify_dictionariespyarrow.lib._sequence_to_arraypyarrow.lib.Array._import_from_c_devicepyarrow.lib.Array._import_from_cpyarrow.lib.FixedSizeListArray.from_arrayspyarrow.lib.DictionaryArray.from_arrayspyarrow.lib.StructArray.flattenpyarrow.lib.RecordBatch.to_struct_arraypyarrow.lib.RunEndEncodedArray._from_arrayspyarrow.lib.ChunkedArray.flattenpyarrow.lib.RecordBatchReader.read_allpyarrow.lib.Table.remove_columnpyarrow.lib.Table.unify_dictionariespyarrow.lib.Table.combine_chunkspyarrow.lib.NativeFile.metadatapyarrow.lib.BufferedInputStream.detachpyarrow.lib.NativeFile.get_streampyarrow.lib.MemoryMappedFile._openpyarrow.lib.MemoryMappedFile.createpyarrow.lib.OSFile._open_readablepyarrow.lib.BufferedOutputStream.detachpyarrow.lib.OSFile._open_writablepyarrow.lib.MessageReader.open_streampyarrow.lib.CompressedInputStream.__init__pyarrow.lib.CompressedOutputStream.__init__pyarrow.lib.BufferedInputStream.__init__pyarrow.lib.BufferedOutputStream.__init__pyarrow.lib.IpcWriteOptions.compression.__set__pyarrow.lib.MessageReader.read_next_messagepyarrow.lib._RecordBatchFileWriter._openpyarrow.lib._RecordBatchStreamWriter._openget_batch_with_custom_metadatapyarrow.lib._RecordBatchFileReader.get_batch_with_custom_metadatapyarrow.lib.RecordBatchReader.read_next_batch_with_custom_metadataread_next_batch_with_custom_metadatapyarrow.lib.RecordBatchReader.from_batchespyarrow.lib.RecordBatchReader._import_from_c_capsulepyarrow.lib.RecordBatchReader._import_from_cpyarrow.lib.RecordBatchReader.castpyarrow.lib._RecordBatchStreamReader._openpyarrow.lib._RecordBatchFileReader._openpyarrow.lib.MapArray.from_arrayspyarrow.lib.native_transcoding_input_streampyarrow.lib.ListArray.from_arrayspyarrow.lib.LargeListArray.from_arrayspyarrow.lib.ListViewArray.from_arrayspyarrow.lib.LargeListViewArray.from_arrayspyarrow.lib.RecordBatch.from_pandaspyarrow.lib.DictionaryScalar._reconstructpyarrow.lib.StringViewBuilder.appendpyarrow.lib.ChunkedArray.to_numpypyarrow.lib.KeyValueMetadata.__init__.genexprpyarrow.lib.KeyValueMetadata.__init__pyarrow.lib.RecordBatch.rename_columnspyarrow.lib.Table.rename_columnsvector.from_py.__pyx_convert_vector_from_py_int8_tpyarrow.lib._extract_union_paramspyarrow.lib.SparseCOOTensor.from_numpypyarrow.lib.SparseCSCMatrix.from_numpypyarrow.lib.SparseCSRMatrix.from_numpypyarrow.lib.SparseCOOTensor.from_pydata_sparsepyarrow.lib.fixed_shape_tensorpyarrow.lib.SparseCSRMatrix.from_scipypyarrow.lib.SparseCSCMatrix.from_scipypyarrow.lib.SparseCSFTensor.from_numpypyarrow.lib.SparseCOOTensor.from_scipypyarrow.lib.DictionaryArray.from_bufferspyarrow.lib.Array.from_bufferspyarrow.lib.StructArray.from_arrayspyarrow.lib.Table.from_batchespyarrow.lib._RecordBatchFileReader.read_allpyarrow.lib.RecordBatch.selectvector.from_py.__pyx_convert_vector_from_py_intpyarrow.lib.SignalStopHandler._init_signalspyarrow.lib.IpcReadOptions.included_fields.__set__pyarrow.lib._convert_pandas_optionspyarrow.lib._array_like_to_pandaspyarrow.lib.ChunkedArray._to_pandaspyarrow.lib._reconstruct_array_datapyarrow.lib.FixedShapeTensorType.dim_names.__get__pyarrow.lib.UnionArray.from_densepyarrow.lib.UnionArray.from_sparseset.from_py.__pyx_convert_unordered_set_from_py_std_3a__3a_stringpyarrow.lib.StructScalar.__getitem__pyarrow.lib._schema_from_arrayspyarrow.lib.RecordBatch.from_arraysqualified name of the generatorobject being iterated by 'yield from', or Nonesend(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_0_11.cython_function_or_methodpyarrow.lib.__pyx_scope_struct_23_iter_batches_with_custom_metadatapyarrow.lib.__pyx_scope_struct_22_uploadpyarrow.lib.__pyx_scope_struct_21__download_nothreadspyarrow.lib.__pyx_scope_struct_20_downloadpyarrow.lib.__pyx_scope_struct_19_genexprpyarrow.lib.__pyx_scope_struct_18_genexprpyarrow.lib.__pyx_scope_struct_17_genexprpyarrow.lib.__pyx_scope_struct_16_genexprpyarrow.lib.__pyx_scope_struct_15_genexprpyarrow.lib.__pyx_scope_struct_14_itercolumnspyarrow.lib.__pyx_scope_struct_13_iterchunkspyarrow.lib.__pyx_scope_struct_12___iter__pyarrow.lib.__pyx_scope_struct_11___iter__pyarrow.lib.__pyx_scope_struct_10___iter__pyarrow.lib.__pyx_scope_struct_9_genexprpyarrow.lib.__pyx_scope_struct_8_itemspyarrow.lib.__pyx_scope_struct_7___iter__pyarrow.lib.__pyx_scope_struct_6___iter__pyarrow.lib.__pyx_scope_struct_5_itemspyarrow.lib.__pyx_scope_struct_4_valuespyarrow.lib.__pyx_scope_struct_3_keyspyarrow.lib.__pyx_scope_struct_2_genexprpyarrow.lib.__pyx_scope_struct_1___iter__pyarrow.lib.__pyx_scope_struct____iter__pyarrow.lib._RecordBatchFileReader The number of record batches in the IPC file. Current IPC read statistics. pyarrow.lib._RecordBatchFileWriterpyarrow.lib._RecordBatchStreamReaderpyarrow.lib._RecordBatchStreamWriterMessageReader() Interface for reading Message objects from some source (like an InputStream) pyarrow.lib.TransformInputStreamTransformInputStream(NativeFile stream, transform_func) Transform an input stream. Parameters ---------- stream : NativeFile The stream to transform. transform_func : callable The transformation to apply. BufferReader(obj) Zero-copy reader from objects convertible to Arrow buffer. Parameters ---------- obj : Python bytes or pyarrow.Buffer Examples -------- Create an Arrow input stream and inspect it: >>> import pyarrow as pa >>> data = b'reader data' >>> buf = memoryview(data) >>> with pa.input_stream(buf) as stream: ... stream.size() ... stream.read(6) ... stream.seek(7) ... stream.read(15) ... 11 b'reader' 7 b'data' pyarrow.lib.BufferOutputStream An output stream that writes to a resizable buffer. The buffer is produced as a result when ``getvalue()`` is called. Examples -------- Create an output stream, write data to it and finalize it with ``getvalue()``: >>> import pyarrow as pa >>> f = pa.BufferOutputStream() >>> f.write(b'pyarrow.Buffer') 14 >>> f.closed False >>> f.getvalue() >>> f.closed True pyarrow.lib.FixedSizeBufferWriter A stream writing to a Arrow buffer. Examples -------- Create a stream to write to ``pyarrow.Buffer``: >>> import pyarrow as pa >>> buf = pa.allocate_buffer(5) >>> with pa.output_stream(buf) as stream: ... stream.write(b'abcde') ... stream ... 5 Inspect the buffer: >>> buf.to_pybytes() b'abcde' >>> buf A stream backed by a regular file descriptor. Examples -------- Create a new file to write to: >>> import pyarrow as pa >>> with pa.OSFile('example_osfile.arrow', mode='w') as f: ... f.writable() ... f.write(b'OSFile') ... f.seekable() ... True 6 False Open the file to read: >>> with pa.OSFile('example_osfile.arrow', mode='r') as f: ... f.mode ... f.read() ... 'rb' b'OSFile' Open the file to append: >>> with pa.OSFile('example_osfile.arrow', mode='ab') as f: ... f.mode ... f.write(b' is super!') ... 'ab' 10 >>> with pa.OSFile('example_osfile.arrow') as f: ... f.read() ... b'OSFile is super!' Inspect created OSFile: >>> pa.OSFile('example_osfile.arrow') A stream that represents a memory-mapped file. Supports 'r', 'r+', 'w' modes. Examples -------- Create a new file with memory map: >>> import pyarrow as pa >>> mmap = pa.create_memory_map('example_mmap.dat', 10) >>> mmap >>> mmap.close() Open an existing file with memory map: >>> with pa.memory_map('example_mmap.dat') as mmap: ... mmap ... A stream backed by a Python file object. This class allows using Python file objects with arbitrary Arrow functions, including functions written in another language than Python. As a downside, there is a non-zero redirection cost in translating Arrow stream calls to Python method calls. Furthermore, Python's Global Interpreter Lock may limit parallelism in some situations. Examples -------- >>> import io >>> import pyarrow as pa >>> pa.PythonFile(io.BytesIO()) Create a stream for writing: >>> buf = io.BytesIO() >>> f = pa.PythonFile(buf, mode = 'w') >>> f.writable() True >>> f.write(b'PythonFile') 10 >>> buf.getvalue() b'PythonFile' >>> f.close() >>> f Create a stream for reading: >>> buf = io.BytesIO(b'PythonFile') >>> f = pa.PythonFile(buf, mode = 'r') >>> f.mode 'rb' >>> f.read() b'PythonFile' >>> f >>> f.close() >>> f Builder class for UTF8 string views. This class exposes facilities for incrementally adding string values and building the null bitmap for a pyarrow.Array (type='string_view'). Builder class for UTF8 strings. This class exposes facilities for incrementally adding string values and building the null bitmap for a pyarrow.Array (type='string'). Concrete class for bool8 extension arrays. Examples -------- Define the extension type for an bool8 array >>> import pyarrow as pa >>> bool8_type = pa.bool8() Create an extension array >>> arr = [-1, 0, 1, 2, None] >>> storage = pa.array(arr, pa.int8()) >>> pa.ExtensionArray.from_storage(bool8_type, storage) [ -1, 0, 1, 2, null ] Concrete class for opaque extension arrays. Examples -------- Define the extension type for an opaque array >>> import pyarrow as pa >>> opaque_type = pa.opaque( ... pa.binary(), ... type_name="geometry", ... vendor_name="postgis", ... ) Create an extension array >>> arr = [None, b"data"] >>> storage = pa.array(arr, pa.binary()) >>> pa.ExtensionArray.from_storage(opaque_type, storage) [ null, 64617461 ] pyarrow.lib.FixedShapeTensorArray Concrete class for fixed shape tensor extension arrays. Examples -------- Define the extension type for tensor array >>> import pyarrow as pa >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2]) Create an extension array >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]] >>> storage = pa.array(arr, pa.list_(pa.int32(), 4)) >>> pa.ExtensionArray.from_storage(tensor_type, storage) [ [ 1, 2, 3, 4 ], [ 10, 20, 30, 40 ], [ 100, 200, 300, 400 ] ] pyarrow.lib.RunEndEncodedArray Concrete class for Arrow run-end encoded arrays. An array holding the logical indexes of each run-end. The physical offset to the array is applied. An array holding the values of each run. The physical offset to the array is applied. Concrete class for Arrow arrays of large variable-sized binary data type. The number of bytes from beginning to end of the data buffer addressed by the offsets of this LargeBinaryArray. Concrete class for Arrow arrays of large string (or utf8) data type. Concrete class for Arrow arrays of duration data type. Concrete class for Arrow arrays of time64 data type. Concrete class for Arrow arrays of time32 data type. Concrete class for Arrow arrays of timestamp data type. Concrete class for Arrow arrays of date64 data type. Concrete class for Arrow arrays of date32 data type. Concrete class for bool8 extension scalar. Concrete class for opaque extension scalar. pyarrow.lib.FixedShapeTensorScalar Concrete class for fixed shape tensor extension scalar. Concrete class for Extension scalars. Return storage value as a scalar. Concrete class for Union scalars. Return underlying value as a scalar. Return the union type code for this scalar. pyarrow.lib.RunEndEncodedScalar Concrete class for RunEndEncoded scalars. Concrete class for dictionary-encoded scalars. Return this value's underlying index as a scalar. Return the encoded value as a scalar. Concrete class for map scalars. Concrete class for struct scalars. pyarrow.lib.LargeListViewScalarpyarrow.lib.FixedSizeListScalar Concrete class for list-like scalars. Concrete class for string-like (utf8) scalars. pyarrow.lib.FixedSizeBinaryScalar Concrete class for binary-like scalars. pyarrow.lib.MonthDayNanoIntervalScalar Concrete class for month, day, nanosecond interval scalars. Same as self.as_py() Concrete class for duration scalars. Concrete class for timestamp scalars. Concrete class for time64 scalars. Concrete class for time32 scalars. Concrete class for date64 scalars. Concrete class for date32 scalars. Concrete class for decimal256 scalars. Concrete class for decimal128 scalars. Concrete class for double scalars. Concrete class for float scalars. Concrete class for int64 scalars. Concrete class for uint64 scalars. Concrete class for int32 scalars. Concrete class for uint32 scalars. Concrete class for int16 scalars. Concrete class for uint16 scalars. Concrete class for int8 scalars. Concrete class for uint8 scalars. Concrete class for boolean scalars. NullScalar() Concrete class for null scalars. pyarrow.lib._ExtensionRegistryNannypyarrow.lib.UnknownExtensionTypeUnknownExtensionType(DataType storage_type, serialized) A concrete class for Python-defined extension types that refer to an unknown Python implementation. Parameters ---------- storage_type : DataType The storage type for which the extension is built. serialized : bytes The serialised output. Concrete class for dense union types. Examples -------- Create an instance of a dense UnionType using ``pa.union``: >>> import pyarrow as pa >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_DENSE), (DenseUnionType(dense_union),) Create an instance of a dense UnionType using ``pa.dense_union``: >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) DenseUnionType(dense_union) Concrete class for sparse union types. Examples -------- Create an instance of a sparse UnionType using ``pa.union``: >>> import pyarrow as pa >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_SPARSE), (SparseUnionType(sparse_union),) Create an instance of a sparse UnionType using ``pa.sparse_union``: >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) SparseUnionType(sparse_union) Base class for union data types. Examples -------- Create an instance of a dense UnionType using ``pa.union``: >>> import pyarrow as pa >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_DENSE), (DenseUnionType(dense_union),) Create an instance of a dense UnionType using ``pa.dense_union``: >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) DenseUnionType(dense_union) Create an instance of a sparse UnionType using ``pa.union``: >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_SPARSE), (SparseUnionType(sparse_union),) Create an instance of a sparse UnionType using ``pa.sparse_union``: >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) SparseUnionType(sparse_union) The mode of the union ("dense" or "sparse"). Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.mode 'sparse' The type code to indicate each data type in this union. Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.type_codes [0, 1] ProxyMemoryPool() Memory pool implementation that tracks the number of bytes and maximum memory allocated through its direct calls, while redirecting to another memory pool. _PandasAPIShim() Lazy pandas importer that isolates usages of pandas APIs and avoids importing pandas until it's actually needed Codec(unicode compression, compression_level=None) Compression codec. Parameters ---------- compression : str Type of compression codec to initialize, valid values are: 'gzip', 'bz2', 'brotli', 'lz4' (or 'lz4_frame'), 'lz4_raw', 'zstd' and 'snappy'. compression_level : int, None Optional parameter specifying how aggressively to compress. The possible ranges and effect of this parameter depend on the specific codec chosen. Higher values compress more but typically use more resources (CPU/RAM). Some codecs support negative values. gzip The compression_level maps to the memlevel parameter of deflateInit2. Higher levels use more RAM but are faster and should have higher compression ratios. bz2 The compression level maps to the blockSize100k parameter of the BZ2_bzCompressInit function. Higher levels use more RAM but are faster and should have higher compression ratios. brotli The compression level maps to the BROTLI_PARAM_QUALITY parameter. Higher values are slower and should have higher compression ratios. lz4/lz4_frame/lz4_raw The compression level parameter is not supported and must be None zstd The compression level maps to the compressionLevel parameter of ZSTD_initCStream. Negative values are supported. Higher values are slower and should have higher compression ratios. snappy The compression level parameter is not supported and must be None Raises ------ ValueError If invalid compression value is passed. Examples -------- >>> import pyarrow as pa >>> pa.Codec.is_available('gzip') True >>> codec = pa.Codec('gzip') >>> codec.name 'gzip' >>> codec.compression_level 9 Returns the compression level parameter of the codecCacheOptions(hole_size_limit=None, *, range_size_limit=None, lazy=None, prefetch_limit=None) Cache options for a pre-buffered fragment scan. Parameters ---------- hole_size_limit : int, default 8KiB The maximum distance in bytes between two consecutive ranges; beyond this value, ranges are not combined. range_size_limit : int, default 32MiB The maximum size in bytes of a combined range; if combining two consecutive ranges would produce a range of a size greater than this, they are not combined lazy : bool, default True lazy = false: request all byte ranges when PreBuffer or WillNeed is called. lazy = True, prefetch_limit = 0: request merged byte ranges only after the reader needs them. lazy = True, prefetch_limit = k: prefetch up to k merged byte ranges ahead of the range that is currently being read. prefetch_limit : int, default 0 The maximum number of ranges to be prefetched. This is only used for lazy cache to asynchronously read some ranges after reading the target range. RecordBatchReader() Base class for reading stream of record batches. Record batch readers function as iterators of record batches that also provide the schema (without the need to get any batches). Warnings -------- Do not call this class's constructor directly, use one of the ``RecordBatchReader.from_*`` functions instead. Notes ----- To import and export using the Arrow C stream interface, use the ``_import_from_c`` and ``_export_to_c`` methods. However, keep in mind this interface is intended for expert users. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([('x', pa.int64())]) >>> def iter_record_batches(): ... for i in range(2): ... yield pa.RecordBatch.from_arrays([pa.array([1, 2, 3])], schema=schema) >>> reader = pa.RecordBatchReader.from_batches(schema, iter_record_batches()) >>> print(reader.schema) x: int64 >>> for batch in reader: ... print(batch) pyarrow.RecordBatch x: int64 ---- x: [1,2,3] pyarrow.RecordBatch x: int64 ---- x: [1,2,3] Shared schema of the record batches in the stream. Returns ------- Schema pyarrow.lib._CRecordBatchWriterThe base RecordBatchWriter wrapper. Provides common implementations of convenience methods. Should not be instantiated directly by user code. Current IPC write statistics. pyarrow.lib.CompressedOutputStreamCompressedOutputStream(stream, unicode compression) An output stream wrapper which compresses data on the fly. Parameters ---------- stream : string, path, pyarrow.NativeFile, or file-like object Input stream object to wrap with the compression. compression : str The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd"). Examples -------- Create an output stream wich compresses the data: >>> import pyarrow as pa >>> data = b"Compressed stream" >>> raw = pa.BufferOutputStream() >>> with pa.CompressedOutputStream(raw, "gzip") as compressed: ... compressed.write(data) ... 17 pyarrow.lib.CompressedInputStreamCompressedInputStream(stream, unicode compression) An input stream wrapper which decompresses data on the fly. Parameters ---------- stream : string, path, pyarrow.NativeFile, or file-like object Input stream object to wrap with the compression. compression : str The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd"). Examples -------- Create an output stream wich compresses the data: >>> import pyarrow as pa >>> data = b"Compressed stream" >>> raw = pa.BufferOutputStream() >>> with pa.CompressedOutputStream(raw, "gzip") as compressed: ... compressed.write(data) ... 17 Create an input stream with decompression referencing the buffer with compressed data: >>> cdata = raw.getvalue() >>> with pa.input_stream(cdata, compression="gzip") as compressed: ... compressed.read() ... b'Compressed stream' which actually translates to the use of ``BufferReader``and ``CompressedInputStream``: >>> raw = pa.BufferReader(cdata) >>> with pa.CompressedInputStream(raw, "gzip") as compressed: ... compressed.read() ... b'Compressed stream' pyarrow.lib.BufferedOutputStreamBufferedOutputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None) An output stream that performs buffered reads from an unbuffered output stream, which can mitigate the overhead of many small writes in some cases. Parameters ---------- stream : NativeFile The writable output stream to wrap with the buffer buffer_size : int Size of the buffer that should be added. memory_pool : MemoryPool The memory pool used to allocate the buffer. pyarrow.lib.BufferedInputStreamBufferedInputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None) An input stream that performs buffered reads from an unbuffered input stream, which can mitigate the overhead of many small reads in some cases. Parameters ---------- stream : NativeFile The input stream to wrap with the buffer buffer_size : int Size of the temporary read buffer. memory_pool : MemoryPool The memory pool used to allocate the buffer. The base class for all Arrow streams. Streams are either readable, writable, or both. They optionally support seeking. While this class exposes methods to read or write data from Python, the primary intent of using a Arrow stream is to pass it to other Arrow facilities that will make use of it, such as Arrow IPC routines. Be aware that there are subtle differences with regular Python files, e.g. destroying a writable Arrow stream without closing it explicitly will not flush any pending data. The file mode. Currently instances of NativeFile may support: * rb: binary read * wb: binary write * rb+: binary read and write * ab: binary append A base class for buffers that can be resized. Buffer() The base class for all Arrow buffers. A buffer represents a contiguous memory area. Many buffers will own their memory, though not all of them do. The buffer size in bytes. The buffer's address, as an integer. The returned address may point to CPU or device memory. Use `is_cpu()` to disambiguate. Whether the buffer is mutable. Whether the buffer is CPU-accessible. The device where the buffer resides. Returns ------- Device The memory manager associated with the buffer. Returns ------- MemoryManager The device type where the buffer resides. Returns ------- DeviceAllocationType MemoryManager() An object that provides memory management primitives. A MemoryManager is always tied to a particular Device instance. It can also have additional parameters (such as a MemoryPool to allocate CPU memory). The device this MemoryManager is tied to. Whether this MemoryManager is tied to the main CPU device. This shorthand method is very useful when deciding whether a memory address is CPU-accessible. Device() Abstract interface for hardware devices This object represents a device with access to some memory spaces. When handling a Buffer or raw memory address, it allows deciding in which context the raw memory address should be interpreted (e.g. CPU-accessible memory, or embedded memory on some particular GPU). A shorthand for this device's type. A device ID to identify this device if there are multiple of this type. If there is no "device_id" equivalent (such as for the main CPU device on non-numa systems) returns -1. Whether this device is the main CPU device. This shorthand method is very useful when deciding whether a memory address is CPU-accessible. Return the DeviceAllocationType of this device. Batch of rows of columns of equal length Warnings -------- Do not call this class's constructor directly, use one of the ``RecordBatch.from_*`` functions instead. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Constructing a RecordBatch from arrays: >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Constructing a RecordBatch from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.RecordBatch.from_pandas(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_pandas(df).to_pandas() year month day n_legs animals 0 2020 3 1 2 Flamingo 1 2022 5 5 4 Horse 2 2021 7 9 5 Brittle stars 3 2022 9 13 100 Centipede Constructing a RecordBatch from pylist: >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, ... {'n_legs': 4, 'animals': 'Dog'}] >>> pa.RecordBatch.from_pylist(pylist).to_pandas() n_legs animals 0 2 Flamingo 1 4 Dog You can also construct a RecordBatch using :func:`pyarrow.record_batch`: >>> pa.record_batch([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> pa.record_batch(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Number of columns Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_columns 2 Number of rows Due to the definition of a RecordBatch, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_rows 6 Schema of the RecordBatch and its columns Returns ------- pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.schema n_legs: int64 animals: string Total number of bytes consumed by the elements of the record batch. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.nbytes 116 The device type where the arrays in the RecordBatch reside. Returns ------- DeviceAllocationType Whether the RecordBatch's arrays are CPU-accessible. A collection of top-level named, equal length Arrow arrays. Warnings -------- Do not call this class's constructor directly, use one of the ``from_*`` methods instead. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from arrays: >>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a RecordBatch: >>> batch = pa.record_batch([n_legs, animals], names=names) >>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.Table.from_pandas(df) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a dictionary of arrays: >>> pydict = {'n_legs': n_legs, 'animals': animals} >>> pa.Table.from_pydict(pydict) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_pydict(pydict).schema n_legs: int64 animals: string Construct a Table from a dictionary of arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a list of rows: >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, {'year': 2021, 'animals': 'Centipede'}] >>> pa.Table.from_pylist(pylist) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,null]] animals: [["Flamingo","Centipede"]] Construct a Table from a list of rows with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('year', pa.int64()), ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"year": "Year of entry"}) >>> pa.Table.from_pylist(pylist, schema=my_schema).schema year: int64 n_legs: int64 animals: string -- schema metadata -- year: 'Year of entry' Construct a Table with :func:`pyarrow.table`: >>> pa.table([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Schema of the table and its columns. Returns ------- Schema Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' ... Number of columns in this table. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_columns 2 Number of rows in this table. Due to the definition of a table, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_rows 4 Total number of bytes consumed by the elements of the table. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.nbytes 72 Whether all ChunkedArrays are CPU-accessible. _Tabular() Internal: An interface for common operations on tabular objects. Names of the Table or RecordBatch columns. Returns ------- list of str Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> table = pa.Table.from_arrays([[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]], ... names=['n_legs', 'animals']) >>> table.column_names ['n_legs', 'animals'] List of all columns in numerical order. Returns ------- columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.columns [ [ [ null, 4, 5, null ] ], [ [ "Flamingo", "Horse", null, "Centipede" ] ]] Dimensions of the table or record batch: (#rows, #columns). Returns ------- (int, int) Number of rows and number of columns. Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table.shape (4, 2) ChunkedArray() An array-like composed from a (possibly empty) collection of pyarrow.Arrays Warnings -------- Do not call this class's constructor directly. Examples -------- To construct a ChunkedArray object use :func:`pyarrow.chunked_array`: >>> import pyarrow as pa >>> pa.chunked_array([], type=pa.int8()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> isinstance(pa.chunked_array([[2, 2, 4], [4, 5, 100]]), pa.ChunkedArray) True Return data type of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Number of null entries Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.null_count 1 Total number of bytes consumed by the elements of the chunked array. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.nbytes 49 Number of underlying chunks. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.num_chunks 2 Convert to a list of single-chunked arrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.chunks [ [ 2, 2, null ], [ 4, 5, 100 ]] Whether all chunks in the ChunkedArray are CPU-accessible. pyarrow.lib.MonthDayNanoIntervalArray Concrete class for Arrow arrays of interval[MonthDayNano] type. Concrete class for Arrow extension arrays. Concrete class for dictionary-encoded Arrow arrays. Concrete class for Arrow arrays of variable-sized binary view data type. Concrete class for Arrow arrays of string (or utf8) view data type. Concrete class for Arrow arrays of variable-sized binary data type. The number of bytes from beginning to end of the data buffer addressed by the offsets of this BinaryArray. Concrete class for Arrow arrays of string (or utf8) data type. Concrete class for Arrow arrays of a Union data type. Get the value offsets array (dense arrays only). Does not account for any slice offset. pyarrow.lib.FixedSizeListArray Concrete class for Arrow arrays of a fixed size list data type. Return the underlying array of values which backs the FixedSizeListArray. Note even null elements are included. Compare with :meth:`flatten`, which returns only the non-null sub-list values. Returns ------- values : Array See Also -------- FixedSizeListArray.flatten : ... Examples -------- >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, None]], ... type=pa.list_(pa.int32(), 2) ... ) >>> array.values [ 1, 2, null, null, 3, null ] Concrete class for Arrow arrays of a map data type. Flattened array of keys across all maps in arrayFlattened array of items across all maps in arraypyarrow.lib.LargeListViewArray Concrete class for Arrow arrays of a large list view data type. Identical to ListViewArray, but with 64-bit offsets. Return the underlying array of values which backs the LargeListArray ignoring the array's offset. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the list view offsets as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return the list view sizes as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Concrete class for Arrow arrays of a list view data type. Return the underlying array of values which backs the ListViewArray ignoring the array's offset and sizes. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return the list sizes as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Concrete class for Arrow arrays of a large list data type. Identical to ListArray, but 64-bit offsets. Return the underlying array of values which backs the LargeListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from the sub-lists: >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, 4, None, 6]], ... type=pa.large_list(pa.int32()), ... ) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] Return the list offsets as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int64Array Concrete class for Arrow arrays of a list data type. Return the underlying array of values which backs the ListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- ListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, None, 6]]) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, 5]]) >>> array.offsets [ 0, 2, 2, 5 ] Concrete class for Arrow arrays of a struct data type. Concrete class for Arrow arrays of decimal256 data type. Concrete class for Arrow arrays of decimal128 data type. pyarrow.lib.FixedSizeBinaryArray Concrete class for Arrow arrays of a fixed-size binary data type. Concrete class for Arrow arrays of float64 data type. Concrete class for Arrow arrays of float32 data type. Concrete class for Arrow arrays of float16 data type. Concrete class for Arrow arrays of uint64 data type. Concrete class for Arrow arrays of int64 data type. Concrete class for Arrow arrays of uint32 data type. Concrete class for Arrow arrays of int32 data type. Concrete class for Arrow arrays of uint16 data type. Concrete class for Arrow arrays of int16 data type. Concrete class for Arrow arrays of uint8 data type. Concrete class for Arrow arrays of int8 data type. pyarrow.lib.FloatingPointArray A base class for Arrow floating-point arrays. A base class for Arrow integer arrays. A base class for Arrow numeric arrays. Concrete class for Arrow arrays of boolean data type. Concrete class for Arrow arrays of null data type. SparseCSFTensor() A sparse CSF tensor. CSF is a generalization of compressed sparse row (CSR) index. CSF index recursively compresses each dimension of a tensor into a set of prefix trees. Each path from a root to leaf forms one tensor non-zero index. CSF is implemented with two arrays of buffers and one arrays of integers. SparseCOOTensor() A sparse COO tensor. SparseCSCMatrix() A sparse CSC matrix. SparseCSRMatrix() A sparse CSR matrix. Tensor() A n-dimensional array a.k.a Tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) type: int32 shape: (2, 3) strides: (12, 4) Names of this tensor dimensions. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_names ['dim1', 'dim2'] Is this tensor mutable or immutable. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_mutable True Is this tensor contiguous in memory. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_contiguous True The dimension (n) of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.ndim 2 The size of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.size 6 The shape of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.shape (2, 3) Strides of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.strides (12, 4) Array() The base class for all Arrow arrays. Total number of bytes consumed by the elements of the array. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. A relative position into another array's data. The purpose is to enable zero-copy slicing. This value defaults to zero but must be applied on all operations with the physical storage buffers. The device type where the array resides. Returns ------- DeviceAllocationType Whether the array is CPU-accessible. pyarrow.lib._PandasConvertibleScalar() The base class for scalars. Data type of the Scalar object. Holds a valid (non-null) value. Schema() A named collection of types a.k.a schema. A schema defines the column names and types in a record batch or table data structure. They also contain metadata about the columns. For example, schemas converted from Pandas contain metadata about their original Pandas types so they can be converted back to the same types. Warnings -------- Do not call this class's constructor directly. Instead use :func:`pyarrow.schema` factory function which makes a new Arrow Schema object. Examples -------- Create a new Arrow Schema object: >>> import pyarrow as pa >>> pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ]) some_int: int32 some_string: string Create Arrow Schema with metadata: >>> pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Return deserialized-from-JSON pandas metadata field (if it exists) Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> schema = pa.Table.from_pandas(df).schema Select pandas metadata field from Arrow Schema: >>> schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, 'stop': 4, 'step': 1}], ... The schema's field names. Returns ------- list of str Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the names of the schema's fields: >>> schema.names ['n_legs', 'animals'] The schema's field types. Returns ------- list of DataType Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the types of the schema's fields: >>> schema.types [DataType(int64), DataType(string)] The schema's metadata. Returns ------- metadata: dict Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) Get the metadata of the schema's fields: >>> schema.metadata {b'n_legs': b'Number of legs per animal'} Field() A named field, with a data type, nullability, and optional metadata. Notes ----- Do not use this class's constructor directly; use pyarrow.field Examples -------- Create an instance of pyarrow.Field: >>> import pyarrow as pa >>> pa.field('key', pa.int32()) pyarrow.Field >>> pa.field('key', pa.int32(), nullable=False) pyarrow.Field >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field pyarrow.Field >>> field.metadata {b'key': b'Something important'} Use the field to create a struct type: >>> pa.struct([field]) StructType(struct) The field nullability. Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.nullable True >>> f2.nullable False The field name. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field.name 'key' The field metadata. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} KeyValueMetadata(__arg0__=None, **kwargs) KeyValueMetadata Parameters ---------- __arg0__ : dict A dict of the key-value metadata **kwargs : optional additional key-value metadata PyExtensionType(DataType storage_type) Concrete base class for Python-defined extension types based on pickle for (de)serialization. .. warning:: This class is deprecated and its deserialization is disabled by default. :class:`ExtensionType` is recommended instead. Parameters ---------- storage_type : DataType The storage type for which the extension is built. Concrete class for UUID extension type. Concrete class for opaque extension type. Opaque is a placeholder for a type from an external (often non-Arrow) system that could not be interpreted. Examples -------- Create an instance of opaque extension type: >>> import pyarrow as pa >>> pa.opaque(pa.int32(), "geometry", "postgis") OpaqueType(extension) The name of the type in the external system. The name of the external system. Concrete class for bool8 extension type. Bool8 is an alternate representation for boolean arrays using 8 bits instead of 1 bit per value. The underlying storage type is int8. Examples -------- Create an instance of bool8 extension type: >>> import pyarrow as pa >>> pa.bool8() Bool8Type(extension) pyarrow.lib.FixedShapeTensorType Concrete class for fixed shape tensor extension type. Examples -------- Create an instance of fixed shape tensor extension type: >>> import pyarrow as pa >>> pa.fixed_shape_tensor(pa.int32(), [2, 2]) FixedShapeTensorType(extension) Create an instance of fixed shape tensor extension type with permutation: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... permutation=[0, 2, 1]) >>> tensor_type.permutation [0, 2, 1] Data type of an individual tensor. Shape of the tensors. Explicit names of the dimensions. Indices of the dimensions ordering. ExtensionType(DataType storage_type, extension_name) Concrete base class for Python-defined extension types. Parameters ---------- storage_type : DataType The underlying storage type for the extension type. extension_name : str A unique name distinguishing this extension type. The name will be used when deserializing IPC data. Examples -------- Define a RationalType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class RationalType(pa.ExtensionType): ... def __init__(self, data_type: pa.DataType): ... if not pa.types.is_integer(data_type): ... raise TypeError(f"data_type must be an integer type not {data_type}") ... super().__init__( ... pa.struct( ... [ ... ("numer", data_type), ... ("denom", data_type), ... ], ... ), ... # N.B. This name does _not_ reference `data_type` so deserialization ... # will work for _any_ integer `data_type` after registration ... "my_package.rational", ... ) ... def __arrow_ext_serialize__(self) -> bytes: ... # No parameters are necessary ... return b"" ... @classmethod ... def __arrow_ext_deserialize__(cls, storage_type, serialized): ... # return an instance of this subclass ... return RationalType(storage_type[0].type) Register the extension type: >>> pa.register_extension_type(RationalType(pa.int64())) Create an instance of RationalType extension type: >>> rational_type = RationalType(pa.int32()) Inspect the extension type: >>> rational_type.extension_name 'my_package.rational' >>> rational_type.storage_type StructType(struct) Wrap an array as an extension array: >>> storage_array = pa.array( ... [ ... {"numer": 10, "denom": 17}, ... {"numer": 20, "denom": 13}, ... ], ... type=rational_type.storage_type ... ) >>> rational_array = rational_type.wrap_array(storage_array) >>> rational_array -- is_valid: all not null -- child 0 type: int32 [ 10, 20 ] -- child 1 type: int32 [ 17, 13 ] Or do the same with creating an ExtensionArray: >>> rational_array = pa.ExtensionArray.from_storage(rational_type, storage_array) >>> rational_array -- is_valid: all not null -- child 0 type: int32 [ 10, 20 ] -- child 1 type: int32 [ 17, 13 ] Unregister the extension type: >>> pa.unregister_extension_type("my_package.rational") Note that even though we registered the concrete type ``RationalType(pa.int64())``, PyArrow will be able to deserialize ``RationalType(integer_type)`` for any ``integer_type``, as the deserializer will reference the name ``my_package.rational`` and the ``@classmethod`` ``__arrow_ext_deserialize__``. Concrete base class for extension types. The extension type name. The underlying storage type. The byte width of the extension type. The bit width of the extension type. Concrete class for run-end encoded types. Concrete class for decimal256 data types. Examples -------- Create an instance of decimal256 type: >>> import pyarrow as pa >>> pa.decimal256(76, 38) Decimal256Type(decimal256(76, 38)) The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.precision 76 The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.scale 38 Concrete class for decimal128 data types. Examples -------- Create an instance of decimal128 type: >>> import pyarrow as pa >>> pa.decimal128(5, 2) Decimal128Type(decimal128(5, 2)) The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.precision 5 The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.scale 2 pyarrow.lib.FixedSizeBinaryType Concrete class for fixed-size binary data types. Examples -------- Create an instance of fixed-size binary type: >>> import pyarrow as pa >>> pa.binary(3) FixedSizeBinaryType(fixed_size_binary[3]) Concrete class for duration data types. Examples -------- Create an instance of duration type: >>> import pyarrow as pa >>> pa.duration('s') DurationType(duration[s]) The duration unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.duration('s') >>> t.unit 's' Concrete class for time64 data types. Supported time unit resolutions are 'us' [microsecond] and 'ns' [nanosecond]. Examples -------- Create an instance of time64 type: >>> import pyarrow as pa >>> pa.time64('us') Time64Type(time64[us]) The time unit ('us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.time64('us') >>> t.unit 'us' Concrete class for time32 data types. Supported time unit resolutions are 's' [second] and 'ms' [millisecond]. Examples -------- Create an instance of time32 type: >>> import pyarrow as pa >>> pa.time32('ms') Time32Type(time32[ms]) The time unit ('s' or 'ms'). Examples -------- >>> import pyarrow as pa >>> t = pa.time32('ms') >>> t.unit 'ms' Concrete class for timestamp data types. Examples -------- >>> import pyarrow as pa Create an instance of timestamp type: >>> pa.timestamp('us') TimestampType(timestamp[us]) Create an instance of timestamp type with timezone: >>> pa.timestamp('s', tz='UTC') TimestampType(timestamp[s, tz=UTC]) The timestamp unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('us') >>> t.unit 'us' The timestamp time zone, if any, or None. Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('s', tz='UTC') >>> t.tz 'UTC' Concrete class for dictionary data types. Examples -------- Create an instance of dictionary type: >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()) DictionaryType(dictionary) Whether the dictionary is ordered, i.e. whether the ordering of values in the dictionary is important. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()).ordered False The data type of dictionary indices (a signed integer type). Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).index_type DataType(int16) The dictionary value type. The dictionary values are found in an instance of DictionaryArray. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).value_type DataType(string) Tracking container for dictionary-encoded fields. Concrete class for struct data types. ``StructType`` supports direct indexing using ``[...]`` (implemented via ``__getitem__``) to access its fields. It will return the struct field with the given index or name. Examples -------- >>> import pyarrow as pa Accessing fields using direct indexing: >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct_type[0] pyarrow.Field >>> struct_type['y'] pyarrow.Field Accessing fields using ``field()``: >>> struct_type.field(1) pyarrow.Field >>> struct_type.field('x') pyarrow.Field # Creating a schema from the struct type's fields: >>> pa.schema(list(struct_type)) x: int32 y: string Lists the field names. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())]) >>> struct_type.names ['a', 'b', 'c'] Lists all fields within the StructType. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())]) >>> struct_type.fields [pyarrow.Field, pyarrow.Field, pyarrow.Field] Concrete class for fixed size list data types. Examples -------- Create an instance of FixedSizeListType: >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2) FixedSizeListType(fixed_size_list[2]) The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_field pyarrow.Field The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_type DataType(int32) The size of the fixed size lists. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).list_size 2 Concrete class for map data types. Examples -------- Create an instance of MapType: >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()) MapType(map) >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True) MapType(map) The field for keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_field pyarrow.Field The data type of keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_type DataType(string) The field for items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_field pyarrow.Field The data type of items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_type DataType(int32) Should the entries be sorted according to keys. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True).keys_sorted True Concrete class for large list view data types (like ListViewType, but with 64-bit offsets). Examples -------- Create an instance of LargeListViewType: >>> import pyarrow as pa >>> pa.large_list_view(pa.string()) LargeListViewType(large_list_view) The field for large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_field pyarrow.Field The data type of large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_type DataType(string) Concrete class for list view data types. Examples -------- Create an instance of ListViewType: >>> import pyarrow as pa >>> pa.list_view(pa.string()) ListViewType(list_view) The field for list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_field pyarrow.Field The data type of list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_type DataType(string) Concrete class for large list data types (like ListType, but with 64-bit offsets). Examples -------- Create an instance of LargeListType: >>> import pyarrow as pa >>> pa.large_list(pa.string()) LargeListType(large_list) The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.large_list(pa.string()).value_type DataType(string) Concrete class for list data types. Examples -------- Create an instance of ListType: >>> import pyarrow as pa >>> pa.list_(pa.string()) ListType(list) The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_field pyarrow.Field The data type of list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_type DataType(string) DataType() Base class of all Arrow data types. Each data type is an *instance* of this class. Examples -------- Instance of int64 type: >>> import pyarrow as pa >>> pa.int64() DataType(int64) Bit width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().bit_width 64 Byte width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().byte_width 8 The number of child fields. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().num_fields 0 >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.string()).num_fields 1 >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct.num_fields 2 Number of data buffers required to construct Array type excluding children. Examples -------- >>> import pyarrow as pa >>> pa.int64().num_buffers 2 >>> pa.string().num_buffers 3 MemoryPool() Base class for memory allocation. Besides tracking its number of allocated bytes, a memory pool also takes care of the required 64-byte alignment for Arrow data. The name of the backend used by this MemoryPool (e.g. "jemalloc"). Message() Container for an Arrow IPC message with metadata and optional body IpcReadOptions(bool ensure_native_endian=True, *, bool use_threads=True, list included_fields=None) Serialization options for reading IPC format. Parameters ---------- ensure_native_endian : bool, default True Whether to convert incoming data to platform-native endianness. use_threads : bool Whether to use the global CPU thread pool to parallelize any computational tasks like decompression included_fields : list If empty (the default), return all deserialized fields. If non-empty, the values are the indices of fields to read on the top-level schema IpcWriteOptions(metadata_version=MetadataVersion.V5, *, bool allow_64bit=False, use_legacy_format=False, compression=None, bool use_threads=True, bool emit_dictionary_deltas=False, bool unify_dictionaries=False) Serialization options for the IPC format. Parameters ---------- metadata_version : MetadataVersion, default MetadataVersion.V5 The metadata version to write. V5 is the current and latest, V4 is the pre-1.0 metadata version (with incompatible Union layout). allow_64bit : bool, default False If true, allow field lengths that don't fit in a signed 32-bit int. use_legacy_format : bool, default False Whether to use the pre-Arrow 0.15 IPC format. compression : str, Codec, or None compression codec to use for record batch buffers. If None then batch buffers will be uncompressed. Must be "lz4", "zstd" or None. To specify a compression_level use `pyarrow.Codec` use_threads : bool Whether to use the global CPU thread pool to parallelize any computational tasks like compression. emit_dictionary_deltas : bool Whether to emit dictionary deltas. Default is false for maximum stream compatibility. unify_dictionaries : bool If true then calls to write_table will attempt to unify dictionaries across all batches in the table. This can help avoid the need for replacement dictionaries (which the file format does not support) but requires computing the unified dictionary and then remapping the indices arrays. This parameter is ignored when writing to the IPC stream format as the IPC stream format can support replacement dictionaries. _unregister_py_extension_typesfg6gXgzggggh3hXh4sLudu|uu0h<<$4$|d|88Tl؊͊ŠHPRR8X@px|}}}xxxxxy.yTy}h(pء|4lvP8\, | q f [ P E : / $ ^!P ;j&X0  $;<<4=d=^c@bb0aaecpchc0c(cppp0ppЯP p0pp@ppp@(إ6\Τ4LLD6 8^HSY8ZhZXYdmTmlmcccd.dXddmnȌ``؞ȟ0xxЫȫ>> import pyarrow as pa >>> data = b"buffer data" >>> empty_obj = bytearray(11) >>> buf = pa.py_buffer(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read(6) ... b'buffer' or from a memoryview object: >>> buf = memoryview(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read() ... b'buffer data' Create a writable NativeFile from a string or file path: >>> with pa.output_stream('example_second.txt') as stream: ... stream.write(b'Write some data') ... 15 >>> with pa.input_stream('example_second.txt') as stream: ... stream.read() ... b'Write some data' input_stream(source, compression=u'detect', buffer_size=None) Create an Arrow input stream. Parameters ---------- source : str, Path, buffer, or file-like object The source to open for reading. compression : str optional, default 'detect' The compression algorithm to use for on-the-fly decompression. If "detect" and source is a file path, then compression will be chosen based on the file extension. If None, no compression will be applied. Otherwise, a well-known algorithm name must be supplied (e.g. "gzip"). buffer_size : int, default None If None or 0, no buffering will happen. Otherwise the size of the temporary read buffer. Examples -------- Create a readable BufferReader (NativeFile) from a Buffer or a memoryview object: >>> import pyarrow as pa >>> buf = memoryview(b"some data") >>> with pa.input_stream(buf) as stream: ... stream.read(4) ... b'some' Create a readable OSFile (NativeFile) from a string or file path: >>> import gzip >>> with gzip.open('example.gz', 'wb') as f: ... f.write(b'some data') ... 9 >>> with pa.input_stream('example.gz') as stream: ... stream.read() ... b'some data' Create a readable PythonFile (NativeFile) from a a Python file object: >>> with open('example.txt', mode='w') as f: ... f.write('some text') ... 9 >>> with pa.input_stream('example.txt') as stream: ... stream.read(6) ... b'some t' decompress(buf, decompressed_size=None, codec=u'lz4', asbytes=False, memory_pool=None) Decompress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or memoryview-compatible object Input object to decompress data from. decompressed_size : int, default None Size of the decompressed result codec : str, default 'lz4' Compression codec. Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'} asbytes : bool, default False Return result as Python bytes object, otherwise Buffer. memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any. Returns ------- uncompressed : pyarrow.Buffer or bytes (if asbytes=True) compress(buf, codec=u'lz4', asbytes=False, memory_pool=None) Compress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol codec : str, default 'lz4' Compression codec. Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'} asbytes : bool, default False Return result as Python bytes object, otherwise Buffer. memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any. Returns ------- compressed : pyarrow.Buffer or bytes (if asbytes=True) Codec.__setstate_cython__(self, __pyx_state)Codec.__reduce_cython__(self)Codec.decompress(self, buf, decompressed_size=None, asbytes=False, memory_pool=None) Decompress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or memoryview-compatible object decompressed_size : int, default None Size of the decompressed result asbytes : boolean, default False Return result as Python bytes object, otherwise Buffer memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any. Returns ------- uncompressed : pyarrow.Buffer or bytes (if asbytes=True) Codec.compress(self, buf, asbytes=False, memory_pool=None) Compress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol asbytes : bool, default False Return result as Python bytes object, otherwise Buffer memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any Returns ------- compressed : pyarrow.Buffer or bytes (if asbytes=True) Codec.maximum_compression_level(unicode compression) Returns the largest valid value for the compression level Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.minimum_compression_level(unicode compression) Returns the smallest valid value for the compression level Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.default_compression_level(unicode compression) Returns the compression level that Arrow will use for the codec if None is specified. Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.supports_compression_level(unicode compression) Returns true if the compression level parameter is supported for the given codec. Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.is_available(unicode compression) Returns whether the compression support has been built and enabled. Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Returns ------- bool Codec.detect(path) Detect and instantiate compression codec based on file extension. Parameters ---------- path : str, path-like File-path to detect compression from. Raises ------ TypeError If the passed value is not path-like. ValueError If the compression can't be detected from the path. Returns ------- Codec CacheOptions.__reduce__(self)CacheOptions._reconstruct(kwargs)CacheOptions.from_network_metrics(time_to_first_byte_millis, transfer_bandwidth_mib_per_sec, ideal_bandwidth_utilization_frac=0.9, max_ideal_request_size_mib=64) Create suiteable CacheOptions based on provided network metrics. Typically this will be used with object storage solutions like Amazon S3, Google Cloud Storage and Azure Blob Storage. Parameters ---------- time_to_first_byte_millis : int Seek-time or Time-To-First-Byte (TTFB) in milliseconds, also called call setup latency of a new read request. The value is a positive integer. transfer_bandwidth_mib_per_sec : int Data transfer Bandwidth (BW) in MiB/sec (per connection). The value is a positive integer. ideal_bandwidth_utilization_frac : int, default 0.9 Transfer bandwidth utilization fraction (per connection) to maximize the net data load. The value is a positive float less than 1. max_ideal_request_size_mib : int, default 64 The maximum single data request size (in MiB) to maximize the net data load. Returns ------- CacheOptions _detect_compression(path)as_buffer(o)foreign_buffer(address, size, base=None) Construct an Arrow buffer with the given *address* and *size*. The buffer will be optionally backed by the Python *base* object, if given. The *base* object will be kept alive as long as this buffer is alive, including across language boundaries (for example if the buffer is referenced by C++ code). Parameters ---------- address : int The starting address of the buffer. The address can refer to both device or host memory but it must be accessible from device after mapping it with `get_device_address` method. size : int The size of device buffer in bytes. base : {None, object} Object that owns the referenced memory. py_buffer(obj) Construct an Arrow buffer from a Python bytes-like or buffer-like object Parameters ---------- obj : object the object from which the buffer should be constructed. transcoding_input_stream(stream, src_encoding, dest_encoding) Add a transcoding transformation to the stream. Incoming data will be decoded according to ``src_encoding`` and then re-encoded according to ``dest_encoding``. Parameters ---------- stream : NativeFile The stream to which the transformation should be applied. src_encoding : str The codec to use when reading data. dest_encoding : str The codec to use for emitted data. Transcoder.__call__(self, buf)Transcoder.__init__(self, decoder, encoder)TransformInputStream.__setstate_cython__(self, __pyx_state)TransformInputStream.__reduce_cython__(self)BufferedOutputStream.__setstate_cython__(self, __pyx_state)BufferedOutputStream.__reduce_cython__(self)BufferedOutputStream.detach(self) Flush any buffered writes and release the raw OutputStream. Further operations on this stream are invalid. Returns ------- raw : NativeFile The underlying raw output stream. BufferedInputStream.__setstate_cython__(self, __pyx_state)BufferedInputStream.__reduce_cython__(self)BufferedInputStream.detach(self) Release the raw InputStream. Further operations on this stream are invalid. Returns ------- raw : NativeFile The underlying raw input stream CompressedOutputStream.__setstate_cython__(self, __pyx_state)CompressedOutputStream.__reduce_cython__(self)CompressedInputStream.__setstate_cython__(self, __pyx_state)CompressedInputStream.__reduce_cython__(self)BufferReader.__setstate_cython__(self, __pyx_state)BufferReader.__reduce_cython__(self)MockOutputStream.__setstate_cython__(self, __pyx_state)MockOutputStream.__reduce_cython__(self)MockOutputStream.size(self)BufferOutputStream.__setstate_cython__(self, __pyx_state)BufferOutputStream.__reduce_cython__(self)BufferOutputStream.getvalue(self) Finalize output stream and return result as pyarrow.Buffer. Returns ------- value : Buffer allocate_buffer(int64_t size, MemoryPool memory_pool=None, resizable=False) Allocate a mutable buffer. Parameters ---------- size : int Number of bytes to allocate (plus internal padding) memory_pool : MemoryPool, optional The pool to allocate memory from. If not given, the default memory pool is used. resizable : bool, default False If true, the returned buffer is resizable. Returns ------- buffer : Buffer or ResizableBuffer ResizableBuffer.resize(self, int64_t new_size, shrink_to_fit=False) Resize buffer to indicated size. Parameters ---------- new_size : int New size of buffer (padding may be added internally). shrink_to_fit : bool, default False If this is true, the buffer is shrunk when new_size is less than the current size. If this is false, the buffer is never shrunk. Buffer.to_pybytes(self) Return this buffer as a Python bytes object. Memory is copied. Buffer.__reduce_ex__(self, protocol)Buffer.equals(self, Buffer other) Determine if two buffers contain exactly the same data. Parameters ---------- other : Buffer Returns ------- are_equal : bool True if buffer contents and size are equal Buffer.slice(self, offset=0, length=None) Slice this buffer. Memory is not copied. You can also use the Python slice notation ``buffer[start:stop]``. Parameters ---------- offset : int, default 0 Offset from start of buffer to slice. length : int, default None Length of slice (default is until end of Buffer starting from offset). Returns ------- sliced : Buffer A logical view over this buffer. Buffer.hex(self) Compute hexadecimal representation of the buffer. Returns ------- : bytes Buffer._assert_cpu(self)FixedSizeBufferWriter.__setstate_cython__(self, __pyx_state)FixedSizeBufferWriter.__reduce_cython__(self)FixedSizeBufferWriter.set_memcopy_threshold(self, int64_t threshold) Parameters ---------- threshold : int64 FixedSizeBufferWriter.set_memcopy_blocksize(self, int64_t blocksize) Parameters ---------- blocksize : int64 FixedSizeBufferWriter.set_memcopy_threads(self, int num_threads) Parameters ---------- num_threads : int OSFile.__setstate_cython__(self, __pyx_state)OSFile.__reduce_cython__(self)OSFile.fileno(self)create_memory_map(path, size) Create a file of the given size and memory-map it. Parameters ---------- path : str The file path to create, on the local filesystem. size : int The file size to create. Returns ------- mmap : MemoryMappedFile Examples -------- Create a file with a memory map: >>> import pyarrow as pa >>> with pa.create_memory_map('example_mmap_create.dat', 27) as mmap: ... mmap.write(b'Create a memory-mapped file') ... mmap.read_at(10, 9) ... 27 b'memory-map' memory_map(path, mode=u'r') Open memory map at file path. Size of the memory map cannot change. Parameters ---------- path : str mode : {'r', 'r+', 'w'}, default 'r' Whether the file is opened for reading ('r'), writing ('w') or both ('r+'). Returns ------- mmap : MemoryMappedFile Examples -------- Reading from a memory map without any memory allocation or copying: >>> import pyarrow as pa >>> with pa.output_stream('example_mmap.txt') as stream: ... stream.write(b'Constructing a buffer referencing the mapped memory') ... 51 >>> with pa.memory_map('example_mmap.txt') as mmap: ... mmap.read_at(6,45) ... b'memory' MemoryMappedFile.__setstate_cython__(self, __pyx_state)MemoryMappedFile.__reduce_cython__(self)MemoryMappedFile.fileno(self)MemoryMappedFile.resize(self, new_size) Resize the map and underlying file. Parameters ---------- new_size : new size in bytes MemoryMappedFile._open(self, path, mode=u'r')MemoryMappedFile.create(path, size) Create a MemoryMappedFile Parameters ---------- path : str Where to create the file. size : int Size of the memory mapped file. PythonFile.__setstate_cython__(self, __pyx_state)PythonFile.__reduce_cython__(self)PythonFile.readlines(self, hint=None) Read lines of the file. Parameters ---------- hint : int Maximum number of bytes read until we stop PythonFile.readline(self, size=None) Read and return a line of bytes from the file. If size is specified, read at most size bytes. Parameters ---------- size : int Maximum number of bytes read PythonFile.truncate(self, pos=None) Parameters ---------- pos : int, optional NativeFile.__setstate_cython__(self, __pyx_state)NativeFile.__reduce_cython__(self)NativeFile._upload_nothreads(self, stream, buffer_size=None) Internal method to do an upload without separate threads, queues etc. Called by upload above if is_threading_enabled() == False NativeFile.upload(self, stream, buffer_size=None) Write from a source stream to this file. Parameters ---------- stream : file-like object Source stream to pipe to this file. buffer_size : int, optional The buffer size to use for data transfers. NativeFile._download_nothreads(self, stream_or_path, buffer_size=None) Internal method to do a download without separate threads, queues etc. Called by download above if is_threading_enabled() == False NativeFile.download(self, stream_or_path, buffer_size=None) Read this file completely to a local path or destination stream. This method first seeks to the beginning of the file. Parameters ---------- stream_or_path : str or file-like object If a string, a local file path to write to; otherwise, should be a writable stream. buffer_size : int, optional The buffer size to use for data transfers. NativeFile.writelines(self, lines) Write lines to the file. Parameters ---------- lines : iterable Iterable of bytes-like objects or exporters of buffer protocol NativeFile.truncate(self) NOT IMPLEMENTED NativeFile.read_buffer(self, nbytes=None) Read from buffer. Parameters ---------- nbytes : int, optional maximum number of bytes read NativeFile.readlines(self, hint=None) NOT IMPLEMENTED. Read lines of the file Parameters ---------- hint : int maximum number of bytes read until we stop NativeFile.readline(self, size=None) NOT IMPLEMENTED. Read and return a line of bytes from the file. If size is specified, read at most size bytes. Line terminator is always b"\n". Parameters ---------- size : int maximum number of bytes read NativeFile.readinto(self, b) Read into the supplied buffer Parameters ---------- b : buffer-like object A writable buffer object (such as a bytearray). Returns ------- written : int number of bytes written NativeFile.readall(self)NativeFile.read1(self, nbytes=None) Read and return up to n bytes. Unlike read(), if *nbytes* is None then a chunk is read, not the entire file. Parameters ---------- nbytes : int, default None The maximum number of bytes to read. Returns ------- data : bytes NativeFile.read_at(self, nbytes, offset) Read indicated number of bytes at offset from the file Parameters ---------- nbytes : int offset : int Returns ------- data : bytes NativeFile.get_stream(self, file_offset, nbytes) Return an input stream that reads a file segment independent of the state of the file. Allows reading portions of a random access file as an input stream without interfering with each other. Parameters ---------- file_offset : int nbytes : int Returns ------- stream : NativeFile NativeFile.read(self, nbytes=None) Read and return up to n bytes. If *nbytes* is None, then the entire remaining file contents are read. Parameters ---------- nbytes : int, default None Returns ------- data : bytes NativeFile.write(self, data) Write data to the file. Parameters ---------- data : bytes-like object or exporter of buffer protocol Returns ------- int nbytes: number of bytes written NativeFile.flush(self) Flush the stream, if applicable. An error is raised if stream is not writable. NativeFile.seek(self, int64_t position, int whence=0) Change current file stream position Parameters ---------- position : int Byte offset, interpreted relative to value of whence argument whence : int, default 0 Point of reference for seek offset Notes ----- Values of whence: * 0 -- start of stream (the default); offset should be zero or positive * 1 -- current stream position; offset may be negative * 2 -- end of stream; offset is usually negative Returns ------- int The new absolute stream position. NativeFile.tell(self) Return current stream position NativeFile.metadata(self) Return file metadata NativeFile.size(self) Return file size NativeFile._assert_seekable(self)NativeFile._assert_writable(self)NativeFile._assert_readable(self)NativeFile._assert_open(self)NativeFile.close(self)NativeFile.fileno(self) NOT IMPLEMENTED NativeFile.isatty(self)NativeFile.seekable(self)NativeFile.writable(self)NativeFile.readable(self)NativeFile.__exit__(self, exc_type, exc_value, tb)NativeFile.__enter__(self)set_io_thread_count(int count) Set the number of threads to use for I/O operations. Many operations, such as scanning a dataset, will implicitly make use of this pool. Parameters ---------- count : int The max number of threads that may be used for I/O. Must be positive. See Also -------- io_thread_count : Get the size of this pool. set_cpu_count : The analogous function for the CPU thread pool. io_thread_count() Return the number of threads to use for I/O operations. Many operations, such as scanning a dataset, will implicitly make use of this pool. The number of threads is set to a fixed value at startup. It can be modified at runtime by calling :func:`set_io_thread_count()`. See Also -------- set_io_thread_count : Modify the size of this pool. cpu_count : The analogous function for the CPU thread pool. have_libhdfs() Return true if HDFS (HadoopFileSystem) library is set up correctly. SparseCSFTensor.__setstate_cython__(self, __pyx_state)SparseCSFTensor.__reduce_cython__(self)SparseCSFTensor.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCSFTensor.equals(self, SparseCSFTensor other) Return true if sparse tensors contains exactly equal data Parameters ---------- other : SparseCSFTensor The other tensor to compare for equality. SparseCSFTensor.to_tensor(self) Convert arrow::SparseCSFTensor to arrow::Tensor SparseCSFTensor.to_numpy(self) Convert arrow::SparseCSFTensor to numpy.ndarrays with zero copy SparseCSFTensor.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCSFTensor Parameters ---------- obj : Tensor The dense tensor that should be converted. SparseCSFTensor.from_numpy(data, indptr, indices, shape, axis_order=None, dim_names=None) Create arrow::SparseCSFTensor from numpy.ndarrays Parameters ---------- data : numpy.ndarray Data used to populate the sparse tensor. indptr : numpy.ndarray The sparsity structure. Each two consecutive dimensions in a tensor correspond to a buffer in indices. A pair of consecutive values at `indptr[dim][i]` `indptr[dim][i + 1]` signify a range of nodes in `indices[dim + 1]` who are children of `indices[dim][i]` node. indices : numpy.ndarray Stores values of nodes. Each tensor dimension corresponds to a buffer in indptr. shape : tuple Shape of the matrix. axis_order : list, optional the sequence in which dimensions were traversed to produce the prefix tree. dim_names : list, optional Names of the dimensions. SparseCSFTensor.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCSFTensor Parameters ---------- obj : numpy.ndarray Data used to populate the rows. dim_names : list[str], optional Names of the dimensions. Returns ------- pyarrow.SparseCSFTensor SparseCSCMatrix.__setstate_cython__(self, __pyx_state)SparseCSCMatrix.__reduce_cython__(self)SparseCSCMatrix.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCSCMatrix.equals(self, SparseCSCMatrix other) Return true if sparse tensors contains exactly equal data Parameters ---------- other : SparseCSCMatrix The other tensor to compare for equality. SparseCSCMatrix.to_tensor(self) Convert arrow::SparseCSCMatrix to arrow::Tensor SparseCSCMatrix.to_scipy(self) Convert arrow::SparseCSCMatrix to scipy.sparse.csc_matrix SparseCSCMatrix.to_numpy(self) Convert arrow::SparseCSCMatrix to numpy.ndarrays with zero copy SparseCSCMatrix.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCSCMatrix Parameters ---------- obj : Tensor The dense tensor that should be converted. SparseCSCMatrix.from_scipy(obj, dim_names=None) Convert scipy.sparse.csc_matrix to arrow::SparseCSCMatrix Parameters ---------- obj : scipy.sparse.csc_matrix The scipy matrix that should be converted. dim_names : list, optional Names of the dimensions. SparseCSCMatrix.from_numpy(data, indptr, indices, shape, dim_names=None) Create arrow::SparseCSCMatrix from numpy.ndarrays Parameters ---------- data : numpy.ndarray Data used to populate the sparse matrix. indptr : numpy.ndarray Range of the rows, The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data. indices : numpy.ndarray Column indices of the corresponding non-zero values. shape : tuple Shape of the matrix. dim_names : list, optional Names of the dimensions. SparseCSCMatrix.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCSCMatrix Parameters ---------- obj : numpy.ndarray Data used to populate the rows. dim_names : list[str], optional Names of the dimensions. Returns ------- pyarrow.SparseCSCMatrix SparseCSRMatrix.__setstate_cython__(self, __pyx_state)SparseCSRMatrix.__reduce_cython__(self)SparseCSRMatrix.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCSRMatrix.equals(self, SparseCSRMatrix other) Return true if sparse tensors contains exactly equal data. Parameters ---------- other : SparseCSRMatrix The other tensor to compare for equality. SparseCSRMatrix.to_tensor(self) Convert arrow::SparseCSRMatrix to arrow::Tensor. SparseCSRMatrix.to_scipy(self) Convert arrow::SparseCSRMatrix to scipy.sparse.csr_matrix. SparseCSRMatrix.to_numpy(self) Convert arrow::SparseCSRMatrix to numpy.ndarrays with zero copy. SparseCSRMatrix.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCSRMatrix. Parameters ---------- obj : Tensor The dense tensor that should be converted. SparseCSRMatrix.from_scipy(obj, dim_names=None) Convert scipy.sparse.csr_matrix to arrow::SparseCSRMatrix. Parameters ---------- obj : scipy.sparse.csr_matrix The scipy matrix that should be converted. dim_names : list, optional Names of the dimensions. SparseCSRMatrix.from_numpy(data, indptr, indices, shape, dim_names=None) Create arrow::SparseCSRMatrix from numpy.ndarrays. Parameters ---------- data : numpy.ndarray Data used to populate the sparse matrix. indptr : numpy.ndarray Range of the rows, The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data. indices : numpy.ndarray Column indices of the corresponding non-zero values. shape : tuple Shape of the matrix. dim_names : list, optional Names of the dimensions. SparseCSRMatrix.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCSRMatrix Parameters ---------- obj : numpy.ndarray The dense numpy array that should be converted. dim_names : list, optional The names of the dimensions. Returns ------- pyarrow.SparseCSRMatrix SparseCOOTensor.__setstate_cython__(self, __pyx_state)SparseCOOTensor.__reduce_cython__(self)SparseCOOTensor.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCOOTensor.equals(self, SparseCOOTensor other) Return true if sparse tensors contains exactly equal data. Parameters ---------- other : SparseCOOTensor The other tensor to compare for equality. SparseCOOTensor.to_tensor(self) Convert arrow::SparseCOOTensor to arrow::Tensor. SparseCOOTensor.to_pydata_sparse(self) Convert arrow::SparseCOOTensor to pydata/sparse.COO. SparseCOOTensor.to_scipy(self) Convert arrow::SparseCOOTensor to scipy.sparse.coo_matrix. SparseCOOTensor.to_numpy(self) Convert arrow::SparseCOOTensor to numpy.ndarrays with zero copy. SparseCOOTensor.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCOOTensor. Parameters ---------- obj : Tensor The tensor that should be converted. SparseCOOTensor.from_pydata_sparse(obj, dim_names=None) Convert pydata/sparse.COO to arrow::SparseCOOTensor. Parameters ---------- obj : pydata.sparse.COO The sparse multidimensional array that should be converted. dim_names : list, optional Names of the dimensions. SparseCOOTensor.from_scipy(obj, dim_names=None) Convert scipy.sparse.coo_matrix to arrow::SparseCOOTensor Parameters ---------- obj : scipy.sparse.csr_matrix The scipy matrix that should be converted. dim_names : list, optional Names of the dimensions. SparseCOOTensor.from_numpy(data, coords, shape, dim_names=None) Create arrow::SparseCOOTensor from numpy.ndarrays Parameters ---------- data : numpy.ndarray Data used to populate the rows. coords : numpy.ndarray Coordinates of the data. shape : tuple Shape of the tensor. dim_names : list, optional Names of the dimensions. SparseCOOTensor.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCOOTensor Parameters ---------- obj : numpy.ndarray Data used to populate the rows. dim_names : list[str], optional Names of the dimensions. Returns ------- pyarrow.SparseCOOTensor Tensor.__setstate_cython__(self, __pyx_state)Tensor.__reduce_cython__(self)Tensor.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_name(0) 'dim1' >>> tensor.dim_name(1) 'dim2' Tensor.equals(self, Tensor other) Return true if the tensors contains exactly equal data. Parameters ---------- other : Tensor The other tensor to compare for equality. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> y = np.array([[2, 2, 4], [4, 5, 10]], np.int32) >>> tensor2 = pa.Tensor.from_numpy(y, dim_names=["a","b"]) >>> tensor.equals(tensor) True >>> tensor.equals(tensor2) False Tensor.to_numpy(self) Convert arrow::Tensor to numpy.ndarray with zero copy Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.to_numpy() array([[ 2, 2, 4], [ 4, 5, 100]], dtype=int32) Tensor.from_numpy(obj, dim_names=None) Create a Tensor from a numpy array. Parameters ---------- obj : numpy.ndarray The source numpy array dim_names : list, optional Names of each dimension of the Tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) type: int32 shape: (2, 3) strides: (12, 4) Tensor._make_shape_or_strides_buffer(self, values) Make a bytes object holding an array of `values` cast to `Py_ssize_t`. TableGroupBy.aggregate(self, aggregations) Perform an aggregation over the grouped columns of the table. Parameters ---------- aggregations : list[tuple(str, str)] or list[tuple(str, str, FunctionOptions)] List of tuples, where each tuple is one aggregation specification and consists of: aggregation column name followed by function name and optionally aggregation function option. Pass empty list to get a single row for each group. The column name can be a string, an empty list or a list of column names, for unary, nullary and n-ary aggregation functions respectively. For the list of function names and respective aggregation function options see :ref:`py-grouped-aggrs`. Returns ------- Table Results of the aggregation functions. Examples -------- >>> import pyarrow as pa >>> t = pa.table([ ... pa.array(["a", "a", "b", "b", "c"]), ... pa.array([1, 2, 3, 4, 5]), ... ], names=["keys", "values"]) Sum the column "values" over the grouped column "keys": >>> t.group_by("keys").aggregate([("values", "sum")]) pyarrow.Table keys: string values_sum: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] Count the rows over the grouped column "keys": >>> t.group_by("keys").aggregate([([], "count_all")]) pyarrow.Table keys: string count_all: int64 ---- keys: [["a","b","c"]] count_all: [[2,2,1]] Do multiple aggregations: >>> t.group_by("keys").aggregate([ ... ("values", "sum"), ... ("keys", "count") ... ]) pyarrow.Table keys: string values_sum: int64 keys_count: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] keys_count: [[2,2,1]] Count the number of non-null values for column "values" over the grouped column "keys": >>> import pyarrow.compute as pc >>> t.group_by(["keys"]).aggregate([ ... ("values", "count", pc.CountOptions(mode="only_valid")) ... ]) pyarrow.Table keys: string values_count: int64 ---- keys: [["a","b","c"]] values_count: [[2,2,1]] Get a single row for each group in column "keys": >>> t.group_by("keys").aggregate([]) pyarrow.Table keys: string ---- keys: [["a","b","c"]] TableGroupBy.__init__(self, table, keys, use_threads=True)_from_pylist(cls, mapping, schema, metadata) Construct a Table/RecordBatch from list of rows / dictionaries. Parameters ---------- cls : Class Table/RecordBatch mapping : list of dicts of rows A mapping of strings to row values. schema : Schema, default None If not passed, will be inferred from the first row of the mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table/RecordBatch _from_pydict(cls, mapping, schema, metadata) Construct a Table/RecordBatch from Arrow arrays or columns. Parameters ---------- cls : Class Table/RecordBatch mapping : dict or Mapping A mapping of strings to Arrays or Python lists. schema : Schema, default None If not passed, will be inferred from the Mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table/RecordBatch concat_tables(tables, MemoryPool memory_pool=None, unicode promote_options=u'none', **kwargs) Concatenate pyarrow.Table objects. If promote_options="none", a zero-copy concatenation will be performed. The schemas of all the Tables must be the same (except the metadata), otherwise an exception will be raised. The result Table will share the metadata with the first table. If promote_options="default", any null type arrays will be casted to the type of other arrays in the column of the same name. If a table is missing a particular field, null values of the appropriate type will be generated to take the place of the missing field. The new schema will share the metadata with the first table. Each field in the new schema will share the metadata with the first table which has the field defined. Note that type promotions may involve additional allocations on the given ``memory_pool``. If promote_options="permissive", the behavior of default plus types will be promoted to the common denominator that fits all the fields. Parameters ---------- tables : iterable of pyarrow.Table objects Pyarrow tables to concatenate into a single Table. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. promote_options : str, default none Accepts strings "none", "default" and "permissive". **kwargs : dict, optional Examples -------- >>> import pyarrow as pa >>> t1 = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> t2 = pa.table([ ... pa.array([2, 4]), ... pa.array(["Parrot", "Dog"]) ... ], names=['n_legs', 'animals']) >>> pa.concat_tables([t1,t2]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Parrot","Dog"]] table(data, names=None, schema=None, metadata=None, nthreads=None) Create a pyarrow.Table from a Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of arrays or chunked arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__``, ``__arrow_c_device_array__`` or ``__arrow_c_stream__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the Arrow Table. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. If passed, the output will have exactly this schema (raising an error when columns are not found in the data and ignoring additional data not specified in the schema, when data is a dict or DataFrame). metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). nthreads : int, default None For pandas.DataFrame inputs: if greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). Returns ------- Table See Also -------- Table.from_arrays, Table.from_pandas, Table.from_pydict Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from a python dictionary: >>> pa.table({"n_legs": n_legs, "animals": animals}) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays: >>> pa.table([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.table([n_legs, animals], names=names, metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.table(df) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from pandas DataFrame with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.table(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: '{"index_columns": [], "column_indexes": [{"name": null, ... Construct a Table from chunked arrays: >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] record_batch(data, names=None, schema=None, metadata=None) Create a pyarrow.RecordBatch from another Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of Arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_device_array__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the RecordBatch. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). Returns ------- RecordBatch See Also -------- RecordBatch.from_arrays, RecordBatch.from_pandas, table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from a python dictionary: >>> pa.record_batch({"n_legs": n_legs, "animals": animals}) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch({"n_legs": n_legs, "animals": animals}).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Creating a RecordBatch from a list of arrays with names: >>> pa.record_batch([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Creating a RecordBatch from a list of arrays with names and metadata: >>> my_metadata={"n_legs": "How many legs does an animal have?"} >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'How many legs does an animal have?' Creating a RecordBatch from a pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.record_batch(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> pa.record_batch(df).to_pandas() year month day n_legs animals 0 2020 3 1 2 Flamingo 1 2022 5 5 4 Horse 2 2021 7 9 5 Brittle stars 3 2022 9 13 100 Centipede Creating a RecordBatch from a pandas DataFrame with schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.record_batch(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... >>> pa.record_batch(df, my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede _reconstruct_table(arrays, schema) Internal: reconstruct pa.Table from pickled components. Table.__arrow_c_stream__(self, requested_schema=None) Export the table as an Arrow C stream PyCapsule. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the stream should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Currently, this is not supported and will raise a NotImplementedError if the schema doesn't match the current schema. Returns ------- PyCapsule Table.join_asof(self, right_table, on, by, tolerance, right_on=None, right_by=None) Perform an asof join between this table and another one. This is similar to a left-join except that we match on nearest key rather than equal keys. Both tables must be sorted by the key. This type of join is most useful for time series data that are not perfectly aligned. Optionally match on equivalent keys with "by" before searching with "on". Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. on : str The column from current table that should be used as the "on" key of the join operation left side. An inexact match is used on the "on" key, i.e. a row is considered a match if and only if left_on - tolerance <= right_on <= left_on. The input dataset must be sorted by the "on" key. Must be a single field of a common type. Currently, the "on" key must be an integer, date, or timestamp type. by : str or list[str] The columns from current table that should be used as the keys of the join operation left side. The join operation is then done only for the matches in these columns. tolerance : int The tolerance for inexact "on" key matching. A right row is considered a match with the left row ``right.on - left.on <= tolerance``. The ``tolerance`` may be: - negative, in which case a past-as-of-join occurs; - or positive, in which case a future-as-of-join occurs; - or zero, in which case an exact-as-of-join occurs. The tolerance is interpreted in the same units as the "on" key. right_on : str or list[str], default None The columns from the right_table that should be used as the on key on the join operation right side. When ``None`` use the same key name as the left table. right_by : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. Returns ------- Table Example -------- >>> import pyarrow as pa >>> t1 = pa.table({'id': [1, 3, 2, 3, 3], ... 'year': [2020, 2021, 2022, 2022, 2023]}) >>> t2 = pa.table({'id': [3, 4], ... 'year': [2020, 2021], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1.join_asof(t2, on='year', by='id', tolerance=-2) pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[1,3,2,3,3]] year: [[2020,2021,2022,2022,2023]] n_legs: [[null,5,null,5,null]] animal: [[null,"Brittle stars",null,"Brittle stars",null]] Table.join(self, right_table, keys, right_keys=None, join_type=u'left outer', left_suffix=None, right_suffix=None, coalesce_keys=True, use_threads=True) Perform a join between this table and another one. Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. keys : str or list[str] The columns from current table that should be used as keys of the join operation left side. right_keys : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. join_type : str, default "left outer" The kind of join that should be performed, one of ("left semi", "right semi", "left anti", "right anti", "inner", "left outer", "right outer", "full outer") left_suffix : str, default None Which suffix to add to left column names. This prevents confusion when the columns in left and right tables have colliding names. right_suffix : str, default None Which suffix to add to the right column names. This prevents confusion when the columns in left and right tables have colliding names. coalesce_keys : bool, default True If the duplicated keys should be omitted from one of the sides in the join result. use_threads : bool, default True Whether to use multithreading or not. Returns ------- Table Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df1 = pd.DataFrame({'id': [1, 2, 3], ... 'year': [2020, 2022, 2019]}) >>> df2 = pd.DataFrame({'id': [3, 4], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1 = pa.Table.from_pandas(df1) >>> t2 = pa.Table.from_pandas(df2) Left outer join: >>> t1.join(t2, 'id').combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2]] year: [[2019,2020,2022]] n_legs: [[5,null,null]] animal: [["Brittle stars",null,null]] Full outer join: >>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2,4]] year: [[2019,2020,2022,null]] n_legs: [[5,null,null,100]] animal: [["Brittle stars",null,null,"Centipede"]] Right outer join: >>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year') pyarrow.Table year: int64 id: int64 n_legs: int64 animal: string ---- year: [[2019,null]] id: [[3,4]] n_legs: [[5,100]] animal: [["Brittle stars","Centipede"]] Right anti join >>> t1.join(t2, 'id', join_type="right anti") pyarrow.Table id: int64 n_legs: int64 animal: string ---- id: [[4]] n_legs: [[100]] animal: [["Centipede"]] Table.group_by(self, keys, use_threads=True) Declare a grouping over the columns of the table. Resulting grouping can then be used to perform aggregations with a subsequent ``aggregate()`` method. Parameters ---------- keys : str or list[str] Name of the columns that should be used as the grouping key. use_threads : bool, default True Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed. Returns ------- TableGroupBy See Also -------- TableGroupBy.aggregate Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.group_by('year').aggregate([('n_legs', 'sum')]) pyarrow.Table year: int64 n_legs_sum: int64 ---- year: [[2020,2022,2021,2019]] n_legs_sum: [[2,6,104,5]] Table.drop(self, columns) Drop one or more columns and return a new table. Alias of Table.drop_columns, but kept for backwards compatibility. Parameters ---------- columns : str or list[str] Field name(s) referencing existing column(s). Returns ------- Table New table without the column(s). Table.rename_columns(self, names) Create new table with columns renamed to provided names. Parameters ---------- names : list[str] or dict[str, str] List of new column names or mapping of old column names to new column names. If a mapping of old to new column names is passed, then all columns which are found to match a provided old column name will be renamed to the new column name. If any column names are not found in the mapping, a KeyError will be raised. Raises ------ KeyError If any of the column names passed in the names mapping do not exist. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> new_names = ["n", "name"] >>> table.rename_columns(new_names) pyarrow.Table n: int64 name: string ---- n: [[2,4,5,100]] name: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> new_names = {"n_legs": "n", "animals": "name"} >>> table.rename_columns(new_names) pyarrow.Table n: int64 name: string ---- n: [[2,4,5,100]] name: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.set_column(self, int i, field_, column) Replace column in Table at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> table.set_column(1,'year', [year]) pyarrow.Table n_legs: int64 year: int64 ---- n_legs: [[2,4,5,100]] year: [[2021,2022,2019,2021]] Table.remove_column(self, int i) Create new Table with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New table without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.remove_column(1) pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Table.add_column(self, int i, field_, column) Add column to Table at position. A new table is returned with the column added, the original table object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> table.add_column(0,"year", [year]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2021,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Original table is left unchanged: >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.__sizeof__(self)Table.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the table. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.get_total_buffer_size() 76 Table._column(self, int i) Select a column by its numeric index. Parameters ---------- i : int The index of the column to retrieve. Returns ------- ChunkedArray Table._to_pandas(self, options, categories=None, ignore_metadata=False, types_mapper=None)Table.to_reader(self, max_chunksize=None) Convert the Table to a RecordBatchReader. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- RecordBatchReader Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatchReader: >>> table.to_reader() >>> reader = table.to_reader() >>> reader.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... >>> reader.read_all() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.to_batches(self, max_chunksize=None) Convert Table to a list of RecordBatch objects. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- list[RecordBatch] Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatch: >>> table.to_batches()[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Convert a Table to a list of RecordBatches: >>> table.to_batches(max_chunksize=2)[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse >>> table.to_batches(max_chunksize=2)[1].to_pandas() n_legs animals 0 5 Brittle stars 1 100 Centipede Table.from_batches(batches, Schema schema=None) Construct a Table from a sequence or iterator of Arrow RecordBatches. Parameters ---------- batches : sequence or iterator of RecordBatch Sequence of RecordBatch to be converted, all schemas must be equal. schema : Schema, default None If not passed, will be inferred from the first RecordBatch. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] >>> batch = pa.record_batch([n_legs, animals], names=names) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Construct a Table from a RecordBatch: >>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a sequence of RecordBatches: >>> pa.Table.from_batches([batch, batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]] Table.to_struct_array(self, max_chunksize=None) Convert to a chunked array of struct type. Parameters ---------- max_chunksize : int, default None Maximum number of rows for ChunkedArray chunks. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- ChunkedArray Table.from_struct_array(struct_array) Construct a Table from a StructArray. Each field in the StructArray will become a column in the resulting ``Table``. Parameters ---------- struct_array : StructArray or ChunkedArray Array to construct the table from. Returns ------- pyarrow.Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.Table.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 Table.from_arrays(arrays, names=None, schema=None, metadata=None) Construct a Table from Arrow arrays. Parameters ---------- arrays : list of pyarrow.Array or pyarrow.ChunkedArray Equal-length arrays that should form the table. names : list of str, optional Names for the table columns. If not passed, schema must be passed. schema : Schema, default None Schema for the created table. If not passed, names must be passed. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from arrays: >>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"animals": "Name of the animal species"}) >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Name of the animal species' Table.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None, bool safe=True) Convert pandas.DataFrame to an Arrow Table. The column types in the resulting Arrow Table are inferred from the dtypes of the pandas.Series in the DataFrame. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. In the case of `object`, we need to guess the datatype by looking at the Python objects in this Series. Be aware that Series of the `object` dtype don't carry enough information to always lead to a meaningful Arrow type. In the case that we cannot infer a type, e.g. because the DataFrame is of length 0 or the Series only contains None/nan objects, the type is set to null. This behavior can be avoided by constructing an explicit schema and passing it to this function. Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the Arrow Table. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``Table``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. safe : bool, default True Check for overflows or other unsafe conversions. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.Table.from_pandas(df) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.cast(self, Schema target_schema, safe=None, options=None) Cast table values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast table values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> table.cast(target_schema=my_schema) pyarrow.Table n_legs: duration[s] animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.equals(self, Table other, bool check_metadata=False) Check if contents of two tables are equal. Parameters ---------- other : pyarrow.Table Table to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names=["n_legs", "animals"] >>> table = pa.Table.from_arrays([n_legs, animals], names=names) >>> table_0 = pa.Table.from_arrays([]) >>> table_1 = pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata={"n_legs": "Number of legs per animal"}) >>> table.equals(table) True >>> table.equals(table_0) False >>> table.equals(table_1) True >>> table.equals(table_1, check_metadata=True) False Table.unify_dictionaries(self, MemoryPool memory_pool=None) Unify dictionaries across all chunks. This method returns an equivalent table, but where all chunks of each column share the same dictionary values. Dictionary indices are transposed accordingly. Columns without dictionaries are returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> table = pa.table([c_arr], names=["animals"]) >>> table pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog"] -- indices: [0,1,2], -- dictionary: ["Horse","Brittle stars","Centipede"] -- indices: [0,1,2]] Unify dictionaries across both chunks: >>> table.unify_dictionaries() pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [0,1,2], -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [3,4,5]] Table.combine_chunks(self, MemoryPool memory_pool=None) Make a new table by combining the chunks this table has. All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> names = ["n_legs", "animals"] >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] >>> table.combine_chunks() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4,4,5,100]] animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]] Table.flatten(self, MemoryPool memory_pool=None) Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> month = pa.array([4, 6]) >>> table = pa.Table.from_arrays([struct,month], ... names = ["a", "month"]) >>> table pyarrow.Table a: struct child 0, animals: string child 1, n_legs: int64 child 2, year: int64 month: int64 ---- a: [ -- is_valid: all not null -- child 0 type: string ["Parrot",null] -- child 1 type: int64 [2,4] -- child 2 type: int64 [null,2022]] month: [[4,6]] Flatten the columns with struct field: >>> table.flatten() pyarrow.Table a.animals: string a.n_legs: int64 a.year: int64 month: int64 ---- a.animals: [["Parrot",null]] a.n_legs: [[2,4]] a.year: [[null,2022]] month: [[4,6]] Table.replace_schema_metadata(self, metadata=None) Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None), which deletes any existing metadata. Parameters ---------- metadata : dict, default None Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Constructing a Table with pyarrow schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> table= pa.table(df, my_schema) >>> table.schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... Create a shallow copy of a Table with deleted schema metadata: >>> table.replace_schema_metadata().schema n_legs: int64 animals: string Create a shallow copy of a Table with new schema metadata: >>> metadata={"animals": "Which animal"} >>> table.replace_schema_metadata(metadata = metadata).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Which animal' Table.select(self, columns) Select columns of the Table. Returns a new Table with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.select([0,1]) pyarrow.Table year: int64 n_legs: int64 ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] >>> table.select(["year"]) pyarrow.Table year: int64 ---- year: [[2020,2022,2019,2021]] Table.slice(self, offset=0, length=None) Compute zero-copy slice of this Table. Parameters ---------- offset : int, default 0 Offset from start of table to slice. length : int, default None Length of slice (default is until end of table starting from offset). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.slice(length=3) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019]] n_legs: [[2,4,5]] animals: [["Flamingo","Horse","Brittle stars"]] >>> table.slice(offset=2) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019,2021]] n_legs: [[5,100]] animals: [["Brittle stars","Centipede"]] >>> table.slice(offset=2, length=1) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019]] n_legs: [[5]] animals: [["Brittle stars"]] Table.__reduce__(self)Table.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid Table._is_initialized(self)table_to_blocks(options, Table table, categories, extension_columns)_reconstruct_record_batch(columns, schema) Internal: reconstruct RecordBatch from pickled components. RecordBatch._import_from_c_device_capsule(schema_capsule, array_capsule) Import RecordBatch from a pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray, respectively. Parameters ---------- schema_capsule : PyCapsule A PyCapsule containing a C ArrowSchema representation of the schema. array_capsule : PyCapsule A PyCapsule containing a C ArrowDeviceArray representation of the array. Returns ------- pyarrow.RecordBatch RecordBatch.__arrow_c_device_array__(self, requested_schema=None, **kwargs) Get a pair of PyCapsules containing a C ArrowDeviceArray representation of the object. Parameters ---------- requested_schema : PyCapsule | None A PyCapsule containing a C ArrowSchema representation of a requested schema. PyArrow will attempt to cast the batch to this data type. If None, the batch will be returned as-is, with a type matching the one returned by :meth:`__arrow_c_schema__()`. kwargs Currently no additional keyword arguments are supported, but this method will accept any keyword with a value of ``None`` for compatibility with future keywords. Returns ------- Tuple[PyCapsule, PyCapsule] A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray, respectively. RecordBatch._import_from_c_device(in_ptr, schema) Import RecordBatch from a C ArrowDeviceArray struct, given its pointer and the imported schema. Parameters ---------- in_ptr: int The raw pointer to a C ArrowDeviceArray struct. type: Schema or int Either a Schema object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. RecordBatch._export_to_c_device(self, out_ptr, out_schema_ptr=0) Export to a C ArrowDeviceArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the record batch schema is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowDeviceArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowDeviceArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. RecordBatch._import_from_c_capsule(schema_capsule, array_capsule) Import RecordBatch from a pair of PyCapsules containing a C ArrowSchema and ArrowArray, respectively. Parameters ---------- schema_capsule : PyCapsule A PyCapsule containing a C ArrowSchema representation of the schema. array_capsule : PyCapsule A PyCapsule containing a C ArrowArray representation of the array. Returns ------- pyarrow.RecordBatch RecordBatch.__arrow_c_stream__(self, requested_schema=None) Export the batch as an Arrow C stream PyCapsule. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the stream should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Currently, this is not supported and will raise a NotImplementedError if the schema doesn't match the current schema. Returns ------- PyCapsule RecordBatch.__arrow_c_array__(self, requested_schema=None) Get a pair of PyCapsules containing a C ArrowArray representation of the object. Parameters ---------- requested_schema : PyCapsule | None A PyCapsule containing a C ArrowSchema representation of a requested schema. PyArrow will attempt to cast the batch to this schema. If None, the batch will be returned as-is, with a schema matching the one returned by :meth:`__arrow_c_schema__()`. Returns ------- Tuple[PyCapsule, PyCapsule] A pair of PyCapsules containing a C ArrowSchema and ArrowArray, respectively. RecordBatch._import_from_c(in_ptr, schema) Import RecordBatch from a C ArrowArray struct, given its pointer and the imported schema. Parameters ---------- in_ptr: int The raw pointer to a C ArrowArray struct. type: Schema or int Either a Schema object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. RecordBatch._export_to_c(self, out_ptr, out_schema_ptr=0) Export to a C ArrowArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the record batch schema is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. RecordBatch.copy_to(self, destination) Copy the entire RecordBatch to destination device. This copies each column of the record batch to create a new record batch where all underlying buffers for the columns have been copied to the destination MemoryManager. Parameters ---------- destination : pyarrow.MemoryManager or pyarrow.Device The destination device to copy the array to. Returns ------- RecordBatch RecordBatch.to_tensor(self, bool null_to_nan=False, bool row_major=True, MemoryPool memory_pool=None) Convert to a :class:`~pyarrow.Tensor`. RecordBatches that can be converted have fields of type signed or unsigned integer or float, including all bit-widths. ``null_to_nan`` is ``False`` by default and this method will raise an error in case any nulls are present. RecordBatches with nulls can be converted with ``null_to_nan`` set to ``True``. In this case null values are converted to ``NaN`` and integer type arrays are promoted to the appropriate float type. Parameters ---------- null_to_nan : bool, default False Whether to write null values in the result as ``NaN``. row_major : bool, default True Whether resulting Tensor is row-major or column-major memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Examples -------- >>> import pyarrow as pa >>> batch = pa.record_batch( ... [ ... pa.array([1, 2, 3, 4, None], type=pa.int32()), ... pa.array([10, 20, 30, 40, None], type=pa.float32()), ... ], names = ["a", "b"] ... ) >>> batch pyarrow.RecordBatch a: int32 b: float ---- a: [1,2,3,4,null] b: [10,20,30,40,null] Convert a RecordBatch to row-major Tensor with null values written as ``NaN``s >>> batch.to_tensor(null_to_nan=True) type: double shape: (5, 2) strides: (16, 8) >>> batch.to_tensor(null_to_nan=True).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) Convert a RecordBatch to column-major Tensor >>> batch.to_tensor(null_to_nan=True, row_major=False) type: double shape: (5, 2) strides: (8, 40) >>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) RecordBatch.to_struct_array(self) Convert to a struct array. RecordBatch.from_struct_array(StructArray struct_array) Construct a RecordBatch from a StructArray. Each field in the StructArray will become a column in the resulting ``RecordBatch``. Parameters ---------- struct_array : StructArray Array to construct the record batch from. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.RecordBatch.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 RecordBatch.from_arrays(list arrays, names=None, schema=None, metadata=None) Construct a RecordBatch from multiple pyarrow.Arrays Parameters ---------- arrays : list of pyarrow.Array One for each field in RecordBatch names : list of str, optional Names for the batch fields. If not passed, schema must be passed schema : Schema, default None Schema for the created batch. If not passed, names must be passed metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from pyarrow Arrays using names: >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Construct a RecordBatch from pyarrow Arrays using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' RecordBatch.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None) Convert pandas.DataFrame to an Arrow RecordBatch Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the RecordBatch. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``RecordBatch``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) Convert pandas DataFrame to RecordBatch: >>> import pyarrow as pa >>> pa.RecordBatch.from_pandas(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_pandas(df, schema=my_schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch specifying columns: >>> pa.RecordBatch.from_pandas(df, columns=["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] RecordBatch._to_pandas(self, options, **kwargs)RecordBatch.cast(self, Schema target_schema, safe=None, options=None) Cast record batch values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast batch values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> batch.cast(target_schema=my_schema) pyarrow.RecordBatch n_legs: duration[s] animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] RecordBatch.select(self, columns) Select columns of the RecordBatch. Returns a new RecordBatch with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) Select columns my indices: >>> batch.select([1]) pyarrow.RecordBatch animals: string ---- animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Select columns by names: >>> batch.select(["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,2,4,4,5,100] RecordBatch.equals(self, other, bool check_metadata=False) Check if contents of two record batches are equal. Parameters ---------- other : pyarrow.RecordBatch RecordBatch to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch_0 = pa.record_batch([]) >>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch.equals(batch) True >>> batch.equals(batch_0) False >>> batch.equals(batch_1) True >>> batch.equals(batch_1, check_metadata=True) False RecordBatch.slice(self, offset=0, length=None) Compute zero-copy slice of this RecordBatch Parameters ---------- offset : int, default 0 Offset from start of record batch to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> batch.slice(offset=3).to_pandas() n_legs animals 0 4 Horse 1 5 Brittle stars 2 100 Centipede >>> batch.slice(length=2).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot >>> batch.slice(offset=3, length=1).to_pandas() n_legs animals 0 4 Horse RecordBatch.serialize(self, memory_pool=None) Write RecordBatch to Buffer as encapsulated IPC message, which does not include a Schema. To reconstruct a RecordBatch from the encapsulated IPC message Buffer returned by this function, a Schema must be passed separately. See Examples. Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> buf = batch.serialize() >>> buf Reconstruct RecordBatch from IPC message Buffer and original Schema >>> pa.ipc.read_record_batch(buf, batch.schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] RecordBatch.rename_columns(self, names) Create new record batch with columns renamed to provided names. Parameters ---------- names : list[str] or dict[str, str] List of new column names or mapping of old column names to new column names. If a mapping of old to new column names is passed, then all columns which are found to match a provided old column name will be renamed to the new column name. If any column names are not found in the mapping, a KeyError will be raised. Raises ------ KeyError If any of the column names passed in the names mapping do not exist. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> new_names = ["n", "name"] >>> batch.rename_columns(new_names) pyarrow.RecordBatch n: int64 name: string ---- n: [2,4,5,100] name: ["Flamingo","Horse","Brittle stars","Centipede"] >>> new_names = {"n_legs": "n", "animals": "name"} >>> batch.rename_columns(new_names) pyarrow.RecordBatch n: int64 name: string ---- n: [2,4,5,100] name: ["Flamingo","Horse","Brittle stars","Centipede"] RecordBatch.set_column(self, int i, field_, column) Replace column in RecordBatch at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> batch.set_column(1,'year', year) pyarrow.RecordBatch n_legs: int64 year: int64 ---- n_legs: [2,4,5,100] year: [2021,2022,2019,2021] RecordBatch.remove_column(self, int i) Create new RecordBatch with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New record batch without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.remove_column(1) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] RecordBatch.add_column(self, int i, field_, column) Add column to RecordBatch at position i. A new record batch is returned with the column added, the original record batch object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> batch.add_column(0,"year", year) pyarrow.RecordBatch year: int64 n_legs: int64 animals: string ---- year: [2021,2022,2019,2021] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Original record batch is left unchanged: >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] RecordBatch.__sizeof__(self)RecordBatch.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the record batch An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.get_total_buffer_size() 120 RecordBatch._column(self, int i) Select single column from record batch by its numeric index. Parameters ---------- i : int The index of the column to retrieve. Returns ------- column : pyarrow.Array RecordBatch.replace_schema_metadata(self, metadata=None) Create shallow copy of record batch by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata Parameters ---------- metadata : dict, default None Returns ------- shallow_copy : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) Constructing a RecordBatch with schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64())], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema) >>> batch.schema n_legs: int64 -- schema metadata -- n_legs: 'Number of legs per animal' Shallow copy of a RecordBatch with deleted schema metadata: >>> batch.replace_schema_metadata().schema n_legs: int64 RecordBatch.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid RecordBatch.__reduce__(self)RecordBatch._is_initialized(self)_Tabular.__setstate_cython__(self, __pyx_state)_Tabular.__reduce_cython__(self)_Tabular.append_column(self, field_, column) Append column at end of columns. Parameters ---------- field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- Table or RecordBatch New table or record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Append column at the end: >>> year = [2021, 2022, 2019, 2021] >>> table.append_column('year', [year]) pyarrow.Table n_legs: int64 animals: string year: int64 ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] year: [[2021,2022,2019,2021]] _Tabular.add_column(self, int i, field_, column)_Tabular.drop_columns(self, columns) Drop one or more columns and return a new Table or RecordBatch. Parameters ---------- columns : str or list[str] Field name(s) referencing existing column(s). Raises ------ KeyError If any of the passed column names do not exist. Returns ------- Table or RecordBatch A tabular object without the column(s). Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Drop one column: >>> table.drop_columns("animals") pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Drop one or more columns: >>> table.drop_columns(["n_legs", "animals"]) pyarrow.Table ... ---- _Tabular.remove_column(self, int i)_Tabular.to_string(self, *, show_metadata=False, preview_cols=0) Return human-readable string representation of Table or RecordBatch. Parameters ---------- show_metadata : bool, default False Display Field-level and Schema-level KeyValueMetadata. preview_cols : int, default 0 Display values of the columns for the first N columns. Returns ------- str _Tabular.to_pylist(self) Convert the Table or RecordBatch to a list of rows / dictionaries. Returns ------- list Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> data = [[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]] >>> table = pa.table(data, names=["n_legs", "animals"]) >>> table.to_pylist() [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ... _Tabular.to_pydict(self) Convert the Table or RecordBatch to a dict or OrderedDict. Returns ------- dict Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"]) >>> table.to_pydict() {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']} _Tabular.filter(self, mask, null_selection_behavior=u'drop') Select rows from the table or record batch based on a boolean mask. The Table can be filtered based on a mask, which will be passed to :func:`pyarrow.compute.filter` to perform the filtering, or it can be filtered through a boolean :class:`.Expression` Parameters ---------- mask : Array or array-like or .Expression The boolean mask or the :class:`.Expression` to filter the table with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled, does nothing if an :class:`.Expression` is used. Returns ------- filtered : Table or RecordBatch A tabular object of the same schema, with only the rows selected by applied filtering Examples -------- Using a Table (works similarly for RecordBatch): >>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) Define an expression and select rows: >>> import pyarrow.compute as pc >>> expr = pc.field("year") <= 2020 >>> table.filter(expr) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2019]] n_legs: [[2,5]] animals: [["Flamingo","Brittle stars"]] Define a mask and select rows: >>> mask=[True, True, False, None] >>> table.filter(mask) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022]] n_legs: [[2,4]] animals: [["Flamingo","Horse"]] >>> table.filter(mask, null_selection_behavior='emit_null') pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,null]] n_legs: [[2,4,null]] animals: [["Flamingo","Horse",null]] _Tabular.take(self, indices) Select rows from a Table or RecordBatch. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the tabular object whose rows will be returned. Returns ------- Table or RecordBatch A tabular object with the same schema, containing the taken rows. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.take([1,3]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] _Tabular.sort_by(self, sorting, **kwargs) Sort the Table or RecordBatch by one or multiple columns. Parameters ---------- sorting : str or list[tuple(name, order)] Name of the column to use to sort (ascending), or a list of multiple sorting conditions where each entry is a tuple with column name and sorting order ("ascending" or "descending") **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- Table or RecordBatch A new tabular object sorted according to the sort keys. Examples -------- Table (works similarly for RecordBatch) >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.sort_by('animal') pyarrow.Table year: int64 n_legs: int64 animal: string ---- year: [[2019,2021,2021,2020,2022,2022]] n_legs: [[5,100,4,2,4,2]] animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]] _Tabular.itercolumns(self) Iterator over all columns in their numerical order. Yields ------ Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> for i in table.itercolumns(): ... print(i.null_count) ... 2 1 _Tabular.from_pylist(cls, mapping, schema=None, metadata=None) Construct a Table or RecordBatch from list of rows / dictionaries. Parameters ---------- mapping : list of dicts of rows A mapping of strings to row values. schema : Schema, default None If not passed, will be inferred from the first row of the mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, ... {'n_legs': 4, 'animals': 'Dog'}] Construct a Table from a list of rows: >>> pa.Table.from_pylist(pylist) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4]] animals: [["Flamingo","Dog"]] Construct a Table from a list of rows with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a list of rows with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pylist(pylist, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' _Tabular.from_pydict(cls, mapping, schema=None, metadata=None) Construct a Table or RecordBatch from Arrow arrays or columns. Parameters ---------- mapping : dict or Mapping A mapping of strings to Arrays or Python lists. schema : Schema, default None If not passed, will be inferred from the Mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> pydict = {'n_legs': n_legs, 'animals': animals} Construct a Table from a dictionary of arrays: >>> pa.Table.from_pydict(pydict) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_pydict(pydict).schema n_legs: int64 animals: string Construct a Table from a dictionary of arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a dictionary of arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pydict(pydict, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' _Tabular.field(self, i) Select a schema field by its column name or numeric index. Parameters ---------- i : int or string The index or name of the field to retrieve. Returns ------- Field Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.field(0) pyarrow.Field >>> table.field(1) pyarrow.Field _Tabular.drop_null(self) Remove rows that contain missing values from a Table or RecordBatch. See :func:`pyarrow.compute.drop_null` for full usage. Returns ------- Table or RecordBatch A tabular object with the same schema, with rows containing no missing values. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [None, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.drop_null() pyarrow.Table year: double n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] _Tabular.column(self, i) Select single column from Table or RecordBatch. Parameters ---------- i : int or string The index or name of the column to retrieve. Returns ------- column : Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Select a column by numeric index: >>> table.column(0) [ [ 2, 4, 5, 100 ] ] Select a column by its name: >>> table.column("animals") [ [ "Flamingo", "Horse", "Brittle stars", "Centipede" ] ] _Tabular._is_initialized(self)_Tabular._ensure_integer_index(self, i) Ensure integer index (convert string column name to integer if needed). _Tabular._column(self, int i) Slice or return column at given index or column name Parameters ---------- key : integer, str, or slice Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view Returns ------- Array (from RecordBatch) or ChunkedArray (from Table) for column input. RecordBatch or Table for slice input. _Tabular.__dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True) Return the dataframe interchange object implementing the interchange protocol. Parameters ---------- nan_as_null : bool, default False Whether to tell the DataFrame to overwrite null values in the data with ``NaN`` (or ``NaT``). allow_copy : bool, default True Whether to allow memory copying when exporting. If set to False it would cause non-zero-copy exports to fail. Returns ------- DataFrame interchange object The object which consuming library can use to ingress the dataframe. Notes ----- Details on the interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html `nan_as_null` currently has no effect; once support for nullable extension dtypes is added, this value should be propagated to columns. _Tabular.__array__(self, dtype=None, copy=None)chunked_array(arrays, type=None) Construct chunked array from list of array-like objects Parameters ---------- arrays : Array, list of Array, or array-like Must all be the same data type. Can be empty only if type also passed. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method) can be passed as well. type : DataType or string coercible to DataType Returns ------- ChunkedArray Examples -------- >>> import pyarrow as pa >>> pa.chunked_array([], type=pa.int8()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] ChunkedArray._assert_cpu(self)ChunkedArray._import_from_c_capsule(stream) Import ChunkedArray from a C ArrowArrayStream PyCapsule. Parameters ---------- stream: PyCapsule A capsule containing a C ArrowArrayStream PyCapsule. Returns ------- ChunkedArray ChunkedArray.__arrow_c_stream__(self, requested_schema=None) Export to a C ArrowArrayStream PyCapsule. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the stream should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Returns ------- PyCapsule A capsule containing a C ArrowArrayStream struct. ChunkedArray.to_pylist(self) Convert to a list of native Python objects. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.to_pylist() [2, 2, 4, 4, None, 100] ChunkedArray.iterchunks(self) Convert to an iterator of ChunkArrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> for i in n_legs.iterchunks(): ... print(i.null_count) ... 0 1 ChunkedArray.chunk(self, i) Select a chunk by its index. Parameters ---------- i : int Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.chunk(1) [ 4, 5, 100 ] ChunkedArray.unify_dictionaries(self, MemoryPool memory_pool=None) Unify dictionaries across all chunks. This method returns an equivalent chunked array, but where all chunks share the same dictionary values. Dictionary indices are transposed accordingly. If there are no dictionaries in the chunked array, it is returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : ChunkedArray Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> c_arr [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ] ] >>> c_arr.unify_dictionaries() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] ChunkedArray.sort(self, order=u'ascending', **kwargs) Sort the ChunkedArray Parameters ---------- order : str, default "ascending" Which order to sort values in. Accepted values are "ascending", "descending". **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- result : ChunkedArray ChunkedArray.drop_null(self) Remove missing values from a chunked array. See :func:`pyarrow.compute.drop_null` for full description. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.drop_null() [ [ 2, 2 ], [ 4, 5, 100 ] ] ChunkedArray.take(self, indices) Select values from the chunked array. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the array whose values will be returned. Returns ------- taken : Array or ChunkedArray An array with the same datatype, containing the taken values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.take([1,4,5]) [ [ 2, 5, 100 ] ] ChunkedArray.index(self, value, start=None, end=None, *, memory_pool=None) Find the first index of a value. See :func:`pyarrow.compute.index` for full usage. Parameters ---------- value : Scalar or object The value to look for in the array. start : int, optional The start index where to look for `value`. end : int, optional The end index where to look for `value`. memory_pool : MemoryPool, optional A memory pool for potential memory allocations. Returns ------- index : Int64Scalar The index of the value in the array (-1 if not found). Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.index(4) >>> n_legs.index(4, start=3) ChunkedArray.filter(self, mask, null_selection_behavior=u'drop') Select values from the chunked array. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the chunked array with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : Array or ChunkedArray An array of the same type, with only the elements selected by the boolean mask. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> mask = pa.array([True, False, None, True, False, True]) >>> n_legs.filter(mask) [ [ 2 ], [ 4, 100 ] ] >>> n_legs.filter(mask, null_selection_behavior="emit_null") [ [ 2, null ], [ 4, 100 ] ] ChunkedArray.slice(self, offset=0, length=None) Compute zero-copy slice of this ChunkedArray Parameters ---------- offset : int, default 0 Offset from start of array to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.slice(2,2) [ [ 4 ], [ 4 ] ] ChunkedArray.value_counts(self) Compute counts of unique elements in array. Returns ------- An array of structs Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.value_counts() -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] ChunkedArray.unique(self) Compute distinct elements in array Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.unique() [ 2, 4, 5, 100 ] ChunkedArray.combine_chunks(self, MemoryPool memory_pool=None) Flatten this ChunkedArray into a single non-chunked array. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.combine_chunks() [ 2, 2, 4, 4, 5, 100 ] ChunkedArray.flatten(self, MemoryPool memory_pool=None) Flatten this ChunkedArray. If it has a struct type, the column is flattened into one array per struct field. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : list of ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> c_arr = pa.chunked_array(n_legs.value_counts()) >>> c_arr [ -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] ] >>> c_arr.flatten() [ [ [ 2, 4, 5, 100 ] ], [ [ 2, 2, 1, 1 ] ]] >>> c_arr.type StructType(struct) >>> n_legs.type DataType(int64) ChunkedArray.dictionary_encode(self, null_encoding=u'mask') Compute dictionary-encoded representation of array. See :func:`pyarrow.compute.dictionary_encode` for full usage. Parameters ---------- null_encoding : str, default "mask" How to handle null entries. Returns ------- encoded : ChunkedArray A dictionary-encoded version of this array. Examples -------- >>> import pyarrow as pa >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> animals.dictionary_encode() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] ChunkedArray.cast(self, target_type=None, safe=None, options=None) Cast array values to another data type See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, None Type to cast array to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- cast : Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Change the data type of an array: >>> n_legs_seconds = n_legs.cast(pa.duration('s')) >>> n_legs_seconds.type DurationType(duration[s]) ChunkedArray.__array__(self, dtype=None, copy=None)ChunkedArray.to_numpy(self, zero_copy_only=False) Return a NumPy copy of this array (experimental). Parameters ---------- zero_copy_only : bool, default False Introduced for signature consistence with pyarrow.Array.to_numpy. This must be False here since NumPy arrays' buffer must be contiguous. Returns ------- array : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_numpy() array([ 2, 2, 4, 4, 5, 100]) ChunkedArray._to_pandas(self, options, types_mapper=None, **kwargs)ChunkedArray.equals(self, ChunkedArray other) Return whether the contents of two chunked arrays are equal. Parameters ---------- other : pyarrow.ChunkedArray Chunked array to compare against. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> n_legs.equals(n_legs) True >>> n_legs.equals(animals) False ChunkedArray.fill_null(self, fill_value) Replace each null element in values with fill_value. See :func:`pyarrow.compute.fill_null` for full usage. Parameters ---------- fill_value : any The replacement value for null entries. Returns ------- result : Array or ChunkedArray A new array with nulls replaced by the given value. Examples -------- >>> import pyarrow as pa >>> fill_value = pa.scalar(5, type=pa.int8()) >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.fill_null(fill_value) [ [ 2, 2, 4, 4, 5, 100 ] ] ChunkedArray.is_valid(self) Return boolean array indicating the non-null values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_valid() [ [ true, true, true ], [ true, false, true ] ] ChunkedArray.is_nan(self) Return boolean array indicating the NaN values. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]]) >>> arr.is_nan() [ [ false, true, false, false, null, false ] ] ChunkedArray.is_null(self, *, nan_is_null=False) Return boolean array indicating the null values. Parameters ---------- nan_is_null : bool (optional, default False) Whether floating-point NaN values should also be considered null. Returns ------- array : boolean Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_null() [ [ false, false, false, false, true, false ] ] Slice or return value at given index Parameters ---------- key : integer or slice Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view Returns ------- value : Scalar (index) or ChunkedArray (slice) ChunkedArray.__sizeof__(self)ChunkedArray.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the chunked array. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.get_total_buffer_size() 49 ChunkedArray.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid ChunkedArray.format(self, **kwargs) DEPRECATED, use pyarrow.ChunkedArray.to_string Parameters ---------- **kwargs : dict Returns ------- str ChunkedArray.to_string(self, *, int indent=0, int window=5, int container_window=2, bool skip_new_lines=False) Render a "pretty-printed" string representation of the ChunkedArray Parameters ---------- indent : int How much to indent right the content of the array, by default ``0``. window : int How many items to preview within each chunk at the begin and end of the chunk when the chunk is bigger than the window. The other elements will be ellipsed. container_window : int How many chunks to preview at the begin and end of the array when the array is bigger than the window. The other elements will be ellipsed. This setting also applies to list columns. skip_new_lines : bool If the array should be rendered as a single line of text or if each element should be on its own line. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_string(skip_new_lines=True) '[[2,2,4],[4,5,100]]' ChunkedArray.length(self) Return length of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.length() 6 ChunkedArray.__reduce__(self)StringViewBuilder.__setstate_cython__(self, __pyx_state)StringViewBuilder.__reduce_cython__(self)StringViewBuilder.finish(self) Return result of builder as an Array object; also resets the builder. Returns ------- array : pyarrow.Array StringViewBuilder.append_values(self, values) Append all the values from an iterable. Parameters ---------- values : iterable of string/bytes or np.nan/None values The values to append to the string array builder. StringViewBuilder.append(self, value) Append a single value to the builder. The value can either be a string/bytes object or a null value (np.nan or None). Parameters ---------- value : string/bytes or np.nan/None The value to append to the string array builder. StringBuilder.__setstate_cython__(self, __pyx_state)StringBuilder.__reduce_cython__(self)StringBuilder.finish(self) Return result of builder as an Array object; also resets the builder. Returns ------- array : pyarrow.Array StringBuilder.append_values(self, values) Append all the values from an iterable. Parameters ---------- values : iterable of string/bytes or np.nan/None values The values to append to the string array builder. StringBuilder.append(self, value) Append a single value to the builder. The value can either be a string/bytes object or a null value (np.nan or None). Parameters ---------- value : string/bytes or np.nan/None The value to append to the string array builder. _empty_array(DataType type) Create empty array of the given type. concat_arrays(arrays, MemoryPool memory_pool=None) Concatenate the given arrays. The contents of the input arrays are copied into the returned array. Raises ------ ArrowInvalid If not all of the arrays have the same type. Parameters ---------- arrays : iterable of pyarrow.Array Arrays to concatenate, must be identically typed. memory_pool : MemoryPool, default None For memory allocations. If None, the default pool is used. Examples -------- >>> import pyarrow as pa >>> arr1 = pa.array([2, 4, 5, 100]) >>> arr2 = pa.array([2, 4]) >>> pa.concat_arrays([arr1, arr2]) [ 2, 4, 5, 100, 2, 4 ] Bool8Array.from_numpy(obj) Convert numpy array to a bool8 extension array without making a copy. The input array must be 1-dimensional, with either bool_ or int8 dtype. Parameters ---------- obj : numpy.ndarray Returns ------- bool8_array : Bool8Array Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = np.array([True, False, True], dtype=np.bool_) >>> pa.Bool8Array.from_numpy(arr) [ 1, 0, 1 ] Bool8Array.from_storage(Int8Array storage) Construct Bool8Array from Int8Array storage. Parameters ---------- storage : Int8Array The underlying storage for the result array. Returns ------- bool8_array : Bool8Array Bool8Array.to_numpy(self, zero_copy_only=True, writable=False) Return a NumPy bool view or copy of this array. By default, tries to return a view of this array. This is only supported for arrays without any nulls. Parameters ---------- zero_copy_only : bool, default True If True, an exception will be raised if the conversion to a numpy array would require copying the underlying data (e.g. in presence of nulls). writable : bool, default False For numpy arrays created with zero copy (view on the Arrow data), the resulting array is not writable (Arrow data is immutable). By setting this to True, a copy of the array is made to ensure it is writable. Returns ------- array : numpy.ndarray FixedShapeTensorArray.from_numpy_ndarray(obj) Convert numpy tensors (ndarrays) to a fixed shape tensor extension array. The first dimension of ndarray will become the length of the fixed shape tensor array. If input array data is not contiguous a copy will be made. Parameters ---------- obj : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = np.array( ... [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]], ... dtype=np.float32) >>> pa.FixedShapeTensorArray.from_numpy_ndarray(arr) [ [ 1, 2, 3, 4, 5, 6 ], [ 1, 2, 3, 4, 5, 6 ] ] FixedShapeTensorArray.to_tensor(self) Convert fixed shape tensor extension array to a pyarrow.Tensor. The resulting Tensor will have (ndim + 1) dimensions. The size of the first dimension will be the length of the fixed shape tensor array and the rest of the dimensions will match the permuted shape of the fixed shape tensor. The conversion is zero-copy. Returns ------- pyarrow.Tensor Tensor representing tensors in the fixed shape tensor array concatenated along the first dimension. FixedShapeTensorArray.to_numpy_ndarray(self) Convert fixed shape tensor extension array to a multi-dimensional numpy.ndarray. The resulting ndarray will have (ndim + 1) dimensions. The size of the first dimension will be the length of the fixed shape tensor array and the rest of the dimensions will match the permuted shape of the fixed shape tensor. The conversion is zero-copy. Returns ------- numpy.ndarray Ndarray representing tensors in the fixed shape tensor array concatenated along the first dimension. ExtensionArray.from_storage(BaseExtensionType typ, Array storage) Construct ExtensionArray from type and storage array. Parameters ---------- typ : DataType The extension type for the result array. storage : Array The underlying storage for the result array. Returns ------- ext_array : ExtensionArray RunEndEncodedArray.find_physical_length(self) Find the physical length of this REE array. The physical length of an REE is the number of physical values (and run-ends) necessary to represent the logical range of values from offset to length. This function uses binary-search, so it has a O(log N) cost. RunEndEncodedArray.find_physical_offset(self) Find the physical offset of this REE array. This is the offset of the run that contains the value of the first logical element of this array considering its offset. This function uses binary-search, so it has a O(log N) cost. RunEndEncodedArray.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None) Construct a RunEndEncodedArray from all the parameters that make up an Array. RunEndEncodedArrays do not have buffers, only children arrays, but this implementation is needed to satisfy the Array interface. Parameters ---------- type : DataType The run_end_encoded(run_end_type, value_type) type. length : int The logical length of the run-end encoded array. Expected to match the last value of the run_ends array (children[0]) minus the offset. buffers : List[Buffer] Empty List or [None]. null_count : int, default -1 The number of null entries in the array. Run-end encoded arrays are specified to not have valid bits and null_count always equals 0. offset : int, default 0 The array's logical offset (in values, not in bytes) from the start of each buffer. children : List[Array] Nested type children containing the run_ends and values arrays. Returns ------- RunEndEncodedArray RunEndEncodedArray.from_arrays(run_ends, values, type=None) Construct RunEndEncodedArray from run_ends and values arrays. Parameters ---------- run_ends : Array (int16, int32, or int64 type) The run_ends array. values : Array (any type) The values array. type : pyarrow.DataType, optional The run_end_encoded(run_end_type, value_type) array type. Returns ------- RunEndEncodedArray RunEndEncodedArray._from_arrays(type, allow_none_for_type, logical_length, run_ends, values, logical_offset)StructArray.sort(self, order=u'ascending', by=None, **kwargs) Sort the StructArray Parameters ---------- order : str, default "ascending" Which order to sort values in. Accepted values are "ascending", "descending". by : str or None, default None If to sort the array by one of its fields or by the whole array. **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- result : StructArray StructArray.from_arrays(arrays, names=None, fields=None, mask=None, memory_pool=None, type=None) Construct StructArray from collection of arrays representing each field in the struct. Either field names, field instances or a struct type must be passed. Parameters ---------- arrays : sequence of Array names : List[str] (optional) Field names for each struct child. fields : List[Field] (optional) Field instances for each struct child. mask : pyarrow.Array[bool] (optional) Indicate which values are null (True) or not null (False). memory_pool : MemoryPool (optional) For memory allocations, if required, otherwise uses default pool. type : pyarrow.StructType (optional) Struct type for name and type of each child. Returns ------- result : StructArray StructArray.flatten(self, MemoryPool memory_pool=None) Return one individual array for each field in the struct. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- result : List[Array] StructArray._flattened_field(self, index, MemoryPool memory_pool=None) Retrieves the child array belonging to field, accounting for the parent array null bitmap. Parameters ---------- index : Union[int, str] Index / position or name of the field. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- result : Array StructArray.field(self, index) Retrieves the child array belonging to field. Parameters ---------- index : Union[int, str] Index / position or name of the field. Returns ------- result : Array DictionaryArray.from_arrays(indices, dictionary, mask=None, bool ordered=False, bool from_pandas=False, bool safe=True, MemoryPool memory_pool=None) Construct a DictionaryArray from indices and values. Parameters ---------- indices : pyarrow.Array, numpy.ndarray or pandas.Series, int type Non-negative integers referencing the dictionary values by zero based index. dictionary : pyarrow.Array, ndarray or pandas.Series The array of values referenced by the indices. mask : ndarray or pandas.Series, bool type True values indicate that indices are actually null. ordered : bool, default False Set to True if the category values are ordered. from_pandas : bool, default False If True, the indices should be treated as though they originated in a pandas.Categorical (null encoded as -1). safe : bool, default True If True, check that the dictionary indices are in range. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise uses default pool. Returns ------- dict_array : DictionaryArray DictionaryArray.from_buffers(DataType type, int64_t length, buffers, Array dictionary, int64_t null_count=-1, int64_t offset=0) Construct a DictionaryArray from buffers. Parameters ---------- type : pyarrow.DataType length : int The number of values in the array. buffers : List[Buffer] The buffers backing the indices array. dictionary : pyarrow.Array, ndarray or pandas.Series The array of values referenced by the indices. null_count : int, default -1 The number of null entries in the indices array. Negative value means that the null count is not known. offset : int, default 0 The array's logical offset (in values, not in bytes) from the start of each buffer. Returns ------- dict_array : DictionaryArray DictionaryArray.dictionary_decode(self) Decodes the DictionaryArray to an Array. DictionaryArray.dictionary_encode(self)LargeStringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0) Construct a LargeStringArray from value_offsets and data buffers. If there are nulls in the data, also a null_bitmap and the matching null_count must be passed. Parameters ---------- length : int value_offsets : Buffer data : Buffer null_bitmap : Buffer, optional null_count : int, default 0 offset : int, default 0 Returns ------- string_array : StringArray StringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0) Construct a StringArray from value_offsets and data buffers. If there are nulls in the data, also a null_bitmap and the matching null_count must be passed. Parameters ---------- length : int value_offsets : Buffer data : Buffer null_bitmap : Buffer, optional null_count : int, default 0 offset : int, default 0 Returns ------- string_array : StringArray UnionArray.from_sparse(Array types, list children, list field_names=None, list type_codes=None) Construct sparse UnionArray from arrays of int8 types and children arrays Parameters ---------- types : Array (int8 type) children : list field_names : list type_codes : list Returns ------- union_array : UnionArray UnionArray.from_dense(Array types, Array value_offsets, list children, list field_names=None, list type_codes=None) Construct dense UnionArray from arrays of int8 types, int32 offsets and children arrays Parameters ---------- types : Array (int8 type) value_offsets : Array (int32 type) children : list field_names : list type_codes : list Returns ------- union_array : UnionArray UnionArray.field(self, int pos) Return the given child field as an individual array. For sparse unions, the returned array has its offset, length, and null count adjusted. For dense unions, the returned array is unchanged. Parameters ---------- pos : int The physical index of the union child field (not its type code). Returns ------- field : Array The given child field. UnionArray.child(self, int pos) DEPRECATED, use field() instead. Parameters ---------- pos : int The physical index of the union child field (not its type code). Returns ------- field : pyarrow.Field The given child field. FixedSizeListArray.from_arrays(values, list_size=None, DataType type=None, mask=None) Construct FixedSizeListArray from array of values and a list length. Parameters ---------- values : Array (any type) list_size : int The fixed length of the lists. type : DataType, optional If not specified, a default ListType with the values' type and `list_size` length is used. mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- FixedSizeListArray Examples -------- Create from a values array and a list size: >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> arr = pa.FixedSizeListArray.from_arrays(values, 2) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] Or create from a values array, list size and matching type: >>> typ = pa.list_(pa.field("values", pa.int64()), 2) >>> arr = pa.FixedSizeListArray.from_arrays(values,type=typ) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] MapArray.from_arrays(offsets, keys, items, DataType type=None, MemoryPool pool=None, mask=None) Construct MapArray from arrays of int32 offsets and key, item arrays. Parameters ---------- offsets : array-like or sequence (int32 type) keys : array-like or sequence (any type) items : array-like or sequence (any type) type : DataType, optional If not specified, a default MapArray with the keys' and items' type is used. pool : MemoryPool mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- map_array : MapArray Examples -------- First, let's understand the structure of our dataset when viewed in a rectangular data model. The total of 5 respondents answered the question "How much did you like the movie x?". The value -1 in the integer array means that the value is missing. The boolean array represents the null bitmask corresponding to the missing values in the integer array. >>> import pyarrow as pa >>> movies_rectangular = np.ma.masked_array([ ... [10, -1, -1], ... [8, 4, 5], ... [-1, 10, 3], ... [-1, -1, -1], ... [-1, -1, -1] ... ], ... [ ... [False, True, True], ... [False, False, False], ... [True, False, False], ... [True, True, True], ... [True, True, True], ... ]) To represent the same data with the MapArray and from_arrays, the data is formed like this: >>> offsets = [ ... 0, # -- row 1 start ... 1, # -- row 2 start ... 4, # -- row 3 start ... 6, # -- row 4 start ... 6, # -- row 5 start ... 6, # -- row 5 end ... ] >>> movies = [ ... "Dark Knight", # ---------------------------------- row 1 ... "Dark Knight", "Meet the Parents", "Superman", # -- row 2 ... "Meet the Parents", "Superman", # ----------------- row 3 ... ] >>> likings = [ ... 10, # -------- row 1 ... 8, 4, 5, # --- row 2 ... 10, 3 # ------ row 3 ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 [] 4 [] dtype: object If the data in the empty rows needs to be marked as missing, it's possible to do so by modifying the offsets argument, so that we specify `None` as the starting positions of the rows we want marked as missing. The end row offset still has to refer to the existing value from keys (and values): >>> offsets = [ ... 0, # ----- row 1 start ... 1, # ----- row 2 start ... 4, # ----- row 3 start ... None, # -- row 4 start ... None, # -- row 5 start ... 6, # ----- row 5 end ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 None 4 None dtype: object LargeListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None) Construct LargeListViewArray from arrays of int64 offsets and values. Parameters ---------- offsets : Array (int64 type) sizes : Array (int64 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : LargeListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] ListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None) Construct ListViewArray from arrays of int32 offsets, sizes, and values. Parameters ---------- offsets : Array (int32 type) sizes : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : ListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] LargeListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None) Construct LargeListArray from arrays of int64 offsets and values. Parameters ---------- offsets : Array (int64 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_array : LargeListArray ListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None) Construct ListArray from arrays of int32 offsets and values. Parameters ---------- offsets : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_array : ListArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], [ 3, 4 ] ] >>> # nulls in the offsets array become null lists >>> offsets = pa.array([0, None, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], null, [ 3, 4 ] ] BaseListArray.value_lengths(self) Return integers array with values equal to the respective length of each list element. Null list values are null in the output. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_lengths() [ 3, 0, null, 1 ] BaseListArray.value_parent_indices(self) Return array of same length as list child values array where each output value is the index of the parent list array slot containing each child value. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_parent_indices() [ 0, 0, 0, 3 ] BaseListArray.flatten(self, recursive=False) Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray according to 'recursive'. Note that this method is different from ``self.values`` in that it takes care of the slicing offset as well as null elements backed by non-empty sub-lists. Parameters ---------- recursive : bool, default False, optional When True, flatten this logical list-array recursively until an array of non-list values is formed. When False, flatten only the top level. Returns ------- result : Array Examples -------- Basic logical list-array's flatten >>> import pyarrow as pa >>> values = [1, 2, 3, 4] >>> offsets = [2, 1, 0] >>> sizes = [2, 2, 2] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 3, 4 ], [ 2, 3 ], [ 1, 2 ] ] >>> array.flatten() [ 3, 4, 2, 3, 1, 2 ] When recursive=True, nested list arrays are flattened recursively until an array of non-list values is formed. >>> array = pa.array([ ... None, ... [ ... [1, None, 2], ... None, ... [3, 4] ... ], ... [], ... [ ... [], ... [5, 6], ... None ... ], ... [ ... [7, 8] ... ] ... ], type=pa.list_(pa.list_(pa.int64()))) >>> array.flatten(True) [ 1, null, 2, 3, 4, 5, 6, 7, 8 ] MonthDayNanoIntervalArray.to_pylist(self) Convert to a list of native Python objects. pyarrow.MonthDayNano is used as the native representation. Returns ------- lst : list Array.__dlpack_device__(self) Return the DLPack device tuple this arrays resides on. Returns ------- tuple : Tuple[int, int] Tuple with index specifying the type of the device (where CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the device which is 0 by default for CPU. Array.__dlpack__(self, stream=None) Export a primitive array as a DLPack capsule. Parameters ---------- stream : int, optional A Python integer representing a pointer to a stream. Currently not supported. Stream is provided by the consumer to the producer to instruct the producer to ensure that operations can safely be performed on the array. Returns ------- capsule : PyCapsule A DLPack capsule for the array, pointing to a DLManagedTensor. Array._import_from_c_device_capsule(schema_capsule, array_capsule)Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs) Get a pair of PyCapsules containing a C ArrowDeviceArray representation of the object. Parameters ---------- requested_schema : PyCapsule | None A PyCapsule containing a C ArrowSchema representation of a requested schema. PyArrow will attempt to cast the array to this data type. If None, the array will be returned as-is, with a type matching the one returned by :meth:`__arrow_c_schema__()`. kwargs Currently no additional keyword arguments are supported, but this method will accept any keyword with a value of ``None`` for compatibility with future keywords. Returns ------- Tuple[PyCapsule, PyCapsule] A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray, respectively. Array._import_from_c_device(in_ptr, type) Import Array from a C ArrowDeviceArray struct, given its pointer and the imported array type. Parameters ---------- in_ptr: int The raw pointer to a C ArrowDeviceArray struct. type: DataType or int Either a DataType object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. Array._export_to_c_device(self, out_ptr, out_schema_ptr=0) Export to a C ArrowDeviceArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the array type is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowDeviceArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowDeviceArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. Array._import_from_c_capsule(schema_capsule, array_capsule)Array.__arrow_c_array__(self, requested_schema=None) Get a pair of PyCapsules containing a C ArrowArray representation of the object. Parameters ---------- requested_schema : PyCapsule | None A PyCapsule containing a C ArrowSchema representation of a requested schema. PyArrow will attempt to cast the array to this data type. If None, the array will be returned as-is, with a type matching the one returned by :meth:`__arrow_c_schema__()`. Returns ------- Tuple[PyCapsule, PyCapsule] A pair of PyCapsules containing a C ArrowSchema and ArrowArray, respectively. Array._import_from_c(in_ptr, type) Import Array from a C ArrowArray struct, given its pointer and the imported array type. Parameters ---------- in_ptr: int The raw pointer to a C ArrowArray struct. type: DataType or int Either a DataType object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. Array._export_to_c(self, out_ptr, out_schema_ptr=0) Export to a C ArrowArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the array type is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. Array.copy_to(self, destination) Construct a copy of the array with all buffers on destination device. This method recursively copies the array's buffers and those of its children onto the destination MemoryManager device and returns the new Array. Parameters ---------- destination : pyarrow.MemoryManager or pyarrow.Device The destination device to copy the array to. Returns ------- Array Array.buffers(self) Return a list of Buffer objects pointing to this array's physical storage. To correctly interpret these buffers, you need to also apply the offset multiplied with the size of the stored data type. Array.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid Array.tolist(self) Alias of to_pylist for compatibility with NumPy. Array.to_pylist(self) Convert to a list of native Python objects. Returns ------- lst : list Array.to_numpy(self, zero_copy_only=True, writable=False) Return a NumPy view or copy of this array. By default, tries to return a view of this array. This is only supported for primitive arrays with the same memory layout as NumPy (i.e. integers, floating point, ..) and without any nulls. For the extension arrays, this method simply delegates to the underlying storage array. Parameters ---------- zero_copy_only : bool, default True If True, an exception will be raised if the conversion to a numpy array would require copying the underlying data (e.g. in presence of nulls, or for non-primitive types). writable : bool, default False For numpy arrays created with zero copy (view on the Arrow data), the resulting array is not writable (Arrow data is immutable). By setting this to True, a copy of the array is made to ensure it is writable. Returns ------- array : numpy.ndarray Array.__array__(self, dtype=None, copy=None)Array._to_pandas(self, options, types_mapper=None, **kwargs)Array.sort(self, order=u'ascending', **kwargs) Sort the Array Parameters ---------- order : str, default "ascending" Which order to sort values in. Accepted values are "ascending", "descending". **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- result : Array Array.index(self, value, start=None, end=None, *, memory_pool=None) Find the first index of a value. See :func:`pyarrow.compute.index` for full usage. Parameters ---------- value : Scalar or object The value to look for in the array. start : int, optional The start index where to look for `value`. end : int, optional The end index where to look for `value`. memory_pool : MemoryPool, optional A memory pool for potential memory allocations. Returns ------- index : Int64Scalar The index of the value in the array (-1 if not found). Array.filter(self, mask, *, null_selection_behavior=u'drop') Select values from an array. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the array with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : Array An array of the same type, with only the elements selected by the boolean mask. Array.drop_null(self) Remove missing values from an array. Array.take(self, indices) Select values from an array. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the array whose values will be returned. Returns ------- taken : Array An array with the same datatype, containing the taken values. Array.slice(self, offset=0, length=None) Compute zero-copy slice of this array. Parameters ---------- offset : int, default 0 Offset from start of array to slice. length : int, default None Length of slice (default is until end of Array starting from offset). Returns ------- sliced : Array An array with the same datatype, containing the sliced values. Slice or return value at given index Parameters ---------- key : integer or slice Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view Returns ------- value : Scalar (index) or Array (slice) Array.fill_null(self, fill_value) See :func:`pyarrow.compute.fill_null` for usage. Parameters ---------- fill_value : any The replacement value for null entries. Returns ------- result : Array A new array with nulls replaced by the given value. Array.is_valid(self) Return BooleanArray indicating the non-null values. Array.is_nan(self) Return BooleanArray indicating the NaN values. Returns ------- array : boolean Array Array.is_null(self, *, nan_is_null=False) Return BooleanArray indicating the null values. Parameters ---------- nan_is_null : bool (optional, default False) Whether floating-point NaN values should also be considered null. Returns ------- array : boolean Array Array.equals(self, Array other) Parameters ---------- other : pyarrow.Array Returns ------- bool Array.format(self, **kwargs) DEPRECATED, use pyarrow.Array.to_string Parameters ---------- **kwargs : dict Returns ------- str Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False) Render a "pretty-printed" string representation of the Array. Note: for data on a non-CPU device, the full array is copied to CPU memory. Parameters ---------- indent : int, default 2 How much to indent the internal items in the string to the right, by default ``2``. top_level_indent : int, default 0 How much to indent right the entire content of the array, by default ``0``. window : int How many primitive items to preview at the begin and end of the array when the array is bigger than the window. The other items will be ellipsed. container_window : int How many container items (such as a list in a list array) to preview at the begin and end of the array when the array is bigger than the window. skip_new_lines : bool If the array should be rendered as a single line of text or if each element should be on its own line. Array.__sizeof__(self)Array.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the array. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None) Construct an Array from a sequence of buffers. The concrete type returned depends on the datatype. Parameters ---------- type : DataType The value type of the array. length : int The number of values in the array. buffers : List[Buffer] The buffers backing this array. null_count : int, default -1 The number of null entries in the array. Negative value means that the null count is not known. offset : int, default 0 The array's logical offset (in values, not in bytes) from the start of each buffer. children : List[Array], default None Nested type children with length matching type.num_fields. Returns ------- array : Array Array.__reduce__(self)Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None) Convert pandas.Series to an Arrow Array. This method uses Pandas semantics about what values indicate nulls. See pyarrow.array for more general conversion from arrays or sequences to Arrow arrays. Parameters ---------- obj : ndarray, pandas.Series, array-like mask : array (boolean), optional Indicate which values are null (True) or not null (False). type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the data. safe : bool, default True Check for overflows or other unsafe conversions. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Notes ----- Localized timestamps will currently be returned as UTC (pandas's native representation). Timezone-naive data will be implicitly interpreted as UTC. Returns ------- array : pyarrow.Array or pyarrow.ChunkedArray ChunkedArray is returned if object data overflows binary buffer. Array.value_counts(self) Compute counts of unique elements in array. Returns ------- StructArray An array of structs Array.dictionary_encode(self, null_encoding=u'mask') Compute dictionary-encoded representation of array. See :func:`pyarrow.compute.dictionary_encode` for full usage. Parameters ---------- null_encoding : str, default "mask" How to handle null entries. Returns ------- encoded : DictionaryArray A dictionary-encoded version of this array. Array.unique(self) Compute distinct elements in array. Returns ------- unique : Array An array of the same data type, with deduplicated elements. Array.sum(self, **kwargs) Sum the values in a numerical array. See :func:`pyarrow.compute.sum` for full usage. Parameters ---------- **kwargs : dict, optional Options to pass to :func:`pyarrow.compute.sum`. Returns ------- sum : Scalar A scalar containing the sum value. Array.view(self, target_type) Return zero-copy "view" of array as another data type. The data types must have compatible columnar buffer layouts Parameters ---------- target_type : DataType Type to construct view as. Returns ------- view : Array Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None) Cast array values to another data type See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, default None Type to cast array to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions memory_pool : MemoryPool, optional memory pool to use for allocations during function execution. Returns ------- cast : Array Array.diff(self, Array other) Compare contents of this array against another one. Return a string containing the result of diffing this array (on the left side) against the other array (on the right side). Parameters ---------- other : Array The other array to compare this array with. Returns ------- diff : str A human-readable printout of the differences. Examples -------- >>> import pyarrow as pa >>> left = pa.array(["one", "two", "three"]) >>> right = pa.array(["two", None, "two-and-a-half", "three"]) >>> print(left.diff(right)) # doctest: +SKIP @@ -0, +0 @@ -"one" @@ -2, +1 @@ +null +"two-and-a-half" Array._debug_print(self)_PandasConvertible.__setstate_cython__(self, __pyx_state)_PandasConvertible.__reduce_cython__(self)_PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, unicode maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False) Convert to a pandas-compatible NumPy array or DataFrame, as appropriate Parameters ---------- memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. categories : list, default empty List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures. strings_to_categorical : bool, default False Encode string (UTF8) and binary types to pandas.Categorical. zero_copy_only : bool, default False Raise an ArrowException if this function call would require copying the underlying data. integer_object_nulls : bool, default False Cast integers with nulls to objects date_as_object : bool, default True Cast dates to objects. If False, convert to datetime64 dtype with the equivalent time unit (if supported). Note: in pandas version < 2.0, only datetime64[ns] conversion is supported. timestamp_as_object : bool, default False Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful in pandas version 1.x if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). Non-nanosecond timestamps are supported in pandas version 2.0. If False, all timestamps are converted to datetime64 dtype. use_threads : bool, default True Whether to parallelize the conversion using multiple threads. deduplicate_objects : bool, default True Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower. ignore_metadata : bool, default False If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present safe : bool, default True For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not. split_blocks : bool, default False If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use. self_destruct : bool, default False EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program. Note that you may not see always memory usage improvements. For example, if multiple columns share an underlying allocation, memory can't be freed until all columns are converted. maps_as_pydicts : str, optional, default `None` Valid values are `None`, 'lossy', or 'strict'. The default behavior (`None`), is to convert Arrow Map arrays to Python association lists (list-of-tuples) in the same order as the Arrow Map, as in [(key1, value1), (key2, value2), ...]. If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts. This can change the ordering of (key, value) pairs, and will deduplicate multiple keys, resulting in a possible loss of data. If 'lossy', this key deduplication results in a warning printed when detected. If 'strict', this instead results in an exception being raised when detected. types_mapper : function, default None A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or ``None`` if the default conversion should be used for that type. If you have a dictionary mapping, you can pass ``dict.get`` as function. coerce_temporal_nanoseconds : bool, default False Only applicable to pandas version >= 2.0. A legacy option to coerce date32, date64, duration, and timestamp time units to nanoseconds when converting to pandas. This is the default behavior in pandas version 1.x. Set this option to True if you'd like to use this coercion when using pandas version >= 2.0 for backwards compatibility (not recommended otherwise). Returns ------- pandas.Series or pandas.DataFrame depending on type of object Examples -------- >>> import pyarrow as pa >>> import pandas as pd Convert a Table to pandas DataFrame: >>> table = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> table.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(table.to_pandas(), pd.DataFrame) True Convert a RecordBatch to pandas DataFrame: >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(batch.to_pandas(), pd.DataFrame) True Convert a Chunked Array to pandas Series: >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_pandas() 0 2 1 2 2 4 3 4 4 5 5 100 dtype: int64 >>> isinstance(n_legs.to_pandas(), pd.Series) True _restore_array(data) Reconstruct an Array from pickled ArrayData. _normalize_slice(arrow_obj, slice key) Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view infer_type(values, mask=None, from_pandas=False) Attempt to infer Arrow data type that can hold the passed Python sequence type in an Array object Parameters ---------- values : array-like Sequence to infer type from. mask : ndarray (bool type), optional Optional exclusion mask where True marks null, False non-null. from_pandas : bool, default False Use pandas's NA/null sentinel values for type inference. Returns ------- type : DataType repeat(value, size, MemoryPool memory_pool=None) Create an Array instance whose slots are the given scalar. Parameters ---------- value : Scalar-like object Either a pyarrow.Scalar or any python object coercible to a Scalar. size : int Number of times to repeat the scalar in the output Array. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.repeat(10, 3) [ 10, 10, 10 ] >>> pa.repeat([1, 2], 2) [ [ 1, 2 ], [ 1, 2 ] ] >>> pa.repeat("string", 3) [ "string", "string", "string" ] >>> pa.repeat(pa.scalar({'a': 1, 'b': [1, 2]}), 2) -- is_valid: all not null -- child 0 type: int64 [ 1, 1 ] -- child 1 type: list [ [ 1, 2 ], [ 1, 2 ] ] nulls(size, type=None, MemoryPool memory_pool=None) Create a strongly-typed Array instance with all elements null. Parameters ---------- size : int Array length. type : pyarrow.DataType, default None Explicit type for the array. By default use NullType. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.nulls(10) 10 nulls >>> pa.nulls(3, pa.uint32()) [ null, null, null ] asarray(values, type=None) Convert to pyarrow.Array, inferring type if not provided. Parameters ---------- values : array-like This can be a sequence, numpy.ndarray, pyarrow.Array or pyarrow.ChunkedArray. If a ChunkedArray is passed, the output will be a ChunkedArray, otherwise the output will be a Array. type : string or DataType Explicitly construct the array with this type. Attempt to cast if indicated type is different. Returns ------- arr : Array or ChunkedArray array(obj, type=None, mask=None, size=None, from_pandas=None, bool safe=True, MemoryPool memory_pool=None) Create pyarrow.Array instance from a Python object. Parameters ---------- obj : sequence, iterable, ndarray, pandas.Series, Arrow-compatible array If both type and size are specified may be a single use iterable. If not strongly-typed, Arrow type will be inferred for resulting array. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_device_array__`` method) can be passed as well. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the data. mask : array[bool], optional Indicate which values are null (True) or not null (False). size : int64, optional Size of the elements. If the input is larger than size bail at this length. For iterators, if size is larger than the input iterator this will be treated as a "max size", but will involve an initial allocation of size followed by a resize to the actual size (so if you know the exact size specifying it correctly will give you better performance). from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. If passed, the mask tasks precedence, but if a value is unmasked (not-null), but still null according to pandas semantics, then it is null. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. safe : bool, default True Check for overflows or other unsafe conversions. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- array : pyarrow.Array or pyarrow.ChunkedArray A ChunkedArray instead of an Array is returned if: - the object data overflowed binary storage. - the object's ``__arrow_array__`` protocol method returned a chunked array. Notes ----- Timezone will be preserved in the returned array for timezone-aware data, else no timezone will be returned for naive timestamps. Internally, UTC values are stored for timezone-aware data with the timezone set in the data type. Pandas's DateOffsets and dateutil.relativedelta.relativedelta are by default converted as MonthDayNanoIntervalArray. relativedelta leapdays are ignored as are all absolute fields on both objects. datetime.timedelta can also be converted to MonthDayNanoIntervalArray but this requires passing MonthDayNanoIntervalType explicitly. Converting to dictionary array will promote to a wider integer type for indices if the number of distinct values cannot be represented, even if the index type was explicitly set. This means that if there are more than 127 values the returned dictionary array's index type will be at least pa.int16() even if pa.int8() was passed to the function. Note that an explicit index type will not be demoted even if it is wider than required. Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> pa.array(pd.Series([1, 2])) [ 1, 2 ] >>> pa.array(["a", "b", "a"], type=pa.dictionary(pa.int8(), pa.string())) ... -- dictionary: [ "a", "b" ] -- indices: [ 0, 1, 0 ] >>> import numpy as np >>> pa.array(pd.Series([1, 2]), mask=np.array([0, 1], dtype=bool)) [ 1, null ] >>> arr = pa.array(range(1024), type=pa.dictionary(pa.int8(), pa.int64())) >>> arr.type.index_type DataType(int16) _handle_arrow_array_protocol(obj, type, mask, size)_ndarray_to_arrow_type(values, DataType type)scalar(value, type=None, *, from_pandas=None, MemoryPool memory_pool=None) Create a pyarrow.Scalar instance from a Python object. Parameters ---------- value : Any Python object coercible to arrow's type system. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the value. from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- scalar : pyarrow.Scalar Examples -------- >>> import pyarrow as pa >>> pa.scalar(42) >>> pa.scalar("string") >>> pa.scalar([1, 2]) >>> pa.scalar([1, 2], type=pa.list_(pa.int16())) Bool8Scalar.as_py(self) Return this scalar as a Python object. FixedShapeTensorScalar.to_tensor(self) Convert fixed shape tensor extension scalar to a pyarrow.Tensor, using shape and strides derived from corresponding FixedShapeTensorType. The conversion is zero-copy. Returns ------- pyarrow.Tensor Tensor represented stored in FixedShapeTensorScalar. FixedShapeTensorScalar.to_numpy(self) Convert fixed shape tensor scalar to a numpy.ndarray. The resulting ndarray's shape matches the permuted shape of the fixed shape tensor scalar. The conversion is zero-copy. Returns ------- numpy.ndarray UuidScalar.as_py(self)ExtensionScalar.from_storage(BaseExtensionType typ, value) Construct ExtensionScalar from type and storage value. Parameters ---------- typ : DataType The extension type for the result scalar. value : object The storage value for the result scalar. Returns ------- ext_scalar : ExtensionScalar ExtensionScalar.as_py(self) Return this scalar as a Python object. UnionScalar.as_py(self) Return underlying value as a Python object. RunEndEncodedScalar.as_py(self) Return underlying value as a Python object. DictionaryScalar.as_py(self) Return this encoded value as a Python object. DictionaryScalar.__reduce__(self)DictionaryScalar._reconstruct(type, is_valid, index, dictionary)MapScalar.as_py(self) Return this value as a Python list. Iterate over this element's values. Return the value at the given index. StructScalar._as_py_tuple(self)StructScalar.as_py(self) Return this value as a Python dict. Return the child value for the given field. Parameters ---------- index : Union[int, str] Index / position or name of the field. Returns ------- result : Scalar StructScalar.items(self)ListScalar.as_py(self) Return this value as a Python list. Iterate over this element's values. Return the value at the given index. Return the number of values. StringScalar.as_py(self) Return this value as a Python string. BinaryScalar.as_py(self) Return this value as a Python bytes. BinaryScalar.as_buffer(self) Return a view over this value as a Buffer object. MonthDayNanoIntervalScalar.as_py(self) Return this value as a pyarrow.MonthDayNano. DurationScalar.as_py(self) Return this value as a Pandas Timedelta instance (if units are nanoseconds and pandas is available), otherwise as a Python datetime.timedelta instance. Return the representation of TimestampScalar using `strftime` to avoid original repr datetime values being out of range. TimestampScalar.as_py(self) Return this value as a Pandas Timestamp instance (if units are nanoseconds and pandas is available), otherwise as a Python datetime.datetime instance. Time64Scalar.as_py(self) Return this value as a Python datetime.timedelta instance. Time32Scalar.as_py(self) Return this value as a Python datetime.timedelta instance. _datetime_from_int(int64_t value, TimeUnit unit, tzinfo=None)Date64Scalar.as_py(self) Return this value as a Python datetime.datetime instance. Date32Scalar.as_py(self) Return this value as a Python datetime.datetime instance. Decimal256Scalar.as_py(self) Return this value as a Python Decimal. Decimal128Scalar.as_py(self) Return this value as a Python Decimal. DoubleScalar.as_py(self) Return this value as a Python float. FloatScalar.as_py(self) Return this value as a Python float. HalfFloatScalar.as_py(self) Return this value as a Python float. Int64Scalar.as_py(self) Return this value as a Python int. UInt64Scalar.as_py(self) Return this value as a Python int. Int32Scalar.as_py(self) Return this value as a Python int. UInt32Scalar.as_py(self) Return this value as a Python int. Int16Scalar.as_py(self) Return this value as a Python int. UInt16Scalar.as_py(self) Return this value as a Python int. Int8Scalar.as_py(self) Return this value as a Python int. UInt8Scalar.as_py(self) Return this value as a Python int. BooleanScalar.as_py(self) Return this value as a Python bool. NullScalar.as_py(self) Return this value as a Python None. Scalar.as_py(self)Scalar.__reduce__(self)Scalar.equals(self, Scalar other) Parameters ---------- other : pyarrow.Scalar Returns ------- bool Scalar.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None) Cast scalar value to another data type. See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, default None Type to cast scalar to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions memory_pool : MemoryPool, optional memory pool to use for allocations during function execution. Returns ------- scalar : A Scalar of the given target data type. _unregister_py_extension_types()_register_py_extension_type()_ExtensionRegistryNanny.__setstate_cython__(self, __pyx_state)_ExtensionRegistryNanny.__reduce_cython__(self)_ExtensionRegistryNanny.release_registry(self)is_float_value(obj) Check if the object is a float. Parameters ---------- obj : object The object to check is_integer_value(obj) Check if the object is an integer. Parameters ---------- obj : object The object to check is_boolean_value(obj) Check if the object is a boolean. Parameters ---------- obj : object The object to check from_numpy_dtype(dtype) Convert NumPy dtype to pyarrow.DataType. Parameters ---------- dtype : the numpy dtype to convert Examples -------- Create a pyarrow DataType from NumPy dtype: >>> import pyarrow as pa >>> import numpy as np >>> pa.from_numpy_dtype(np.dtype('float16')) DataType(halffloat) >>> pa.from_numpy_dtype('U') DataType(string) >>> pa.from_numpy_dtype(bool) DataType(bool) >>> pa.from_numpy_dtype(np.str_) DataType(string) schema(fields, metadata=None) Construct pyarrow.Schema from collection of fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). metadata : dict, default None Keys and values must be coercible to bytes. Examples -------- Create a Schema from iterable of tuples: >>> import pyarrow as pa >>> pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()), ... pa.field('some_required_string', pa.string(), nullable=False) ... ]) some_int: int32 some_string: string some_required_string: string not null Create a Schema from iterable of Fields: >>> pa.schema([ ... pa.field('some_int', pa.int32()), ... pa.field('some_string', pa.string()) ... ]) some_int: int32 some_string: string Returns ------- schema : pyarrow.Schema ensure_type(ty, bool allow_none=False) -> DataTypetype_for_alias(name) Return DataType given a string alias if one exists. Parameters ---------- name : str The alias of the DataType that should be retrieved. Returns ------- type : DataType opaque(DataType storage_type, unicode type_name, unicode vendor_name) Create instance of opaque extension type. Parameters ---------- storage_type : DataType The underlying data type. type_name : str The name of the type in the external system. vendor_name : str The name of the external system. Examples -------- Create an instance of an opaque extension type: >>> import pyarrow as pa >>> type = pa.opaque(pa.binary(), "other", "jdbc") >>> type OpaqueType(extension) Inspect the data type: >>> type.storage_type DataType(binary) >>> type.type_name 'other' >>> type.vendor_name 'jdbc' Create a table with an opaque array: >>> arr = [None, b"foobar"] >>> storage = pa.array(arr, pa.binary()) >>> other = pa.ExtensionArray.from_storage(type, storage) >>> pa.table([other], names=["unknown_col"]) pyarrow.Table unknown_col: extension ---- unknown_col: [[null,666F6F626172]] Returns ------- type : OpaqueType bool8() Create instance of bool8 extension type. Examples -------- Create an instance of bool8 extension type: >>> import pyarrow as pa >>> type = pa.bool8() >>> type Bool8Type(extension) Inspect the data type: >>> type.storage_type DataType(int8) Create a table with a bool8 array: >>> arr = [-1, 0, 1, 2, None] >>> storage = pa.array(arr, pa.int8()) >>> other = pa.ExtensionArray.from_storage(type, storage) >>> pa.table([other], names=["unknown_col"]) pyarrow.Table unknown_col: extension ---- unknown_col: [[-1,0,1,2,null]] Returns ------- type : Bool8Type fixed_shape_tensor(DataType value_type, shape, dim_names=None, permutation=None) Create instance of fixed shape tensor extension type with shape and optional names of tensor dimensions and indices of the desired logical ordering of dimensions. Parameters ---------- value_type : DataType Data type of individual tensor elements. shape : tuple or list of integers The physical shape of the contained tensors. dim_names : tuple or list of strings, default None Explicit names to tensor dimensions. permutation : tuple or list integers, default None Indices of the desired ordering of the original dimensions. The indices contain a permutation of the values ``[0, 1, .., N-1]`` where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor. For more information on this parameter see :ref:`fixed_shape_tensor_extension`. Examples -------- Create an instance of fixed shape tensor extension type: >>> import pyarrow as pa >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2]) >>> tensor_type FixedShapeTensorType(extension) Inspect the data type: >>> tensor_type.value_type DataType(int32) >>> tensor_type.shape [2, 2] Create a table with fixed shape tensor extension array: >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]] >>> storage = pa.array(arr, pa.list_(pa.int32(), 4)) >>> tensor = pa.ExtensionArray.from_storage(tensor_type, storage) >>> pa.table([tensor], names=["tensor_array"]) pyarrow.Table tensor_array: extension ---- tensor_array: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]] Create an instance of fixed shape tensor extension type with names of tensor dimensions: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... dim_names=['C', 'H', 'W']) >>> tensor_type.dim_names ['C', 'H', 'W'] Create an instance of fixed shape tensor extension type with permutation: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... permutation=[0, 2, 1]) >>> tensor_type.permutation [0, 2, 1] Returns ------- type : FixedShapeTensorType uuid() Create UuidType instance. Returns ------- type : UuidType run_end_encoded(run_end_type, value_type) Create RunEndEncodedType from run-end and value types. Parameters ---------- run_end_type : pyarrow.DataType The integer type of the run_ends array. Must be 'int16', 'int32', or 'int64'. value_type : pyarrow.DataType The type of the values array. Returns ------- type : RunEndEncodedType union(child_fields, mode, type_codes=None) Create UnionType from child fields. A union is a nested type where each logical value is taken from a single child. A buffer of 8-bit type ids indicates which child a given logical value is to be taken from. Unions come in two flavors: sparse and dense (see also `pyarrow.sparse_union` and `pyarrow.dense_union`). Parameters ---------- child_fields : sequence of Field values Each field must have a UTF8-encoded name, and these field names are part of the type metadata. mode : str Must be 'sparse' or 'dense' type_codes : list of integers, default None Returns ------- type : UnionType dense_union(child_fields, type_codes=None) Create DenseUnionType from child fields. A dense union is a nested type where each logical value is taken from a single child, at a specific offset. A buffer of 8-bit type ids indicates which child a given logical value is to be taken from, and a buffer of 32-bit offsets indicates at which physical position in the given child array the logical value is to be taken from. Unlike a sparse union, a dense union allows encoding only the child array values which are actually referred to by the union array. This is counterbalanced by the additional footprint of the offsets buffer, and the additional indirection cost when looking up values. Parameters ---------- child_fields : sequence of Field values Each field must have a UTF8-encoded name, and these field names are part of the type metadata. type_codes : list of integers, default None Returns ------- type : DenseUnionType sparse_union(child_fields, type_codes=None) Create SparseUnionType from child fields. A sparse union is a nested type where each logical value is taken from a single child. A buffer of 8-bit type ids indicates which child a given logical value is to be taken from. In a sparse union, each child array should have the same length as the union array, regardless of the actual number of union values that refer to it. Parameters ---------- child_fields : sequence of Field values Each field must have a UTF8-encoded name, and these field names are part of the type metadata. type_codes : list of integers, default None Returns ------- type : SparseUnionType struct(fields) Create StructType instance from fields. A struct is a nested type parameterized by an ordered sequence of types (which can all be distinct), called its fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Each field must have a UTF8-encoded name, and these field names are part of the type metadata. Examples -------- Create an instance of StructType from an iterable of tuples: >>> import pyarrow as pa >>> fields = [ ... ('f1', pa.int32()), ... ('f2', pa.string()), ... ] >>> struct_type = pa.struct(fields) >>> struct_type StructType(struct) Retrieve a field from a StructType: >>> struct_type[0] pyarrow.Field >>> struct_type['f1'] pyarrow.Field Create an instance of StructType from an iterable of Fields: >>> fields = [ ... pa.field('f1', pa.int32()), ... pa.field('f2', pa.string(), nullable=False), ... ] >>> pa.struct(fields) StructType(struct) Returns ------- type : DataType dictionary(index_type, value_type, bool ordered=False) -> DictionaryType Dictionary (categorical, or simply encoded) type. Parameters ---------- index_type : DataType value_type : DataType ordered : bool Returns ------- type : DictionaryType Examples -------- Create an instance of dictionary type: >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()) DictionaryType(dictionary) Use dictionary type to create an array: >>> pa.array(["a", "b", None, "d"], pa.dictionary(pa.int64(), pa.utf8())) ... -- dictionary: [ "a", "b", "d" ] -- indices: [ 0, 1, null, 2 ] map_(key_type, item_type, keys_sorted=False) -> MapType Create MapType instance from key and item data types or fields. Parameters ---------- key_type : DataType or Field item_type : DataType or Field keys_sorted : bool Returns ------- map_type : DataType Examples -------- Create an instance of MapType: >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()) MapType(map) >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True) MapType(map) Use MapType to create an array: >>> data = [[{'key': 'a', 'value': 1}, {'key': 'b', 'value': 2}], [{'key': 'c', 'value': 3}]] >>> pa.array(data, type=pa.map_(pa.string(), pa.int32(), keys_sorted=True)) [ keys: [ "a", "b" ] values: [ 1, 2 ], keys: [ "c" ] values: [ 3 ] ] large_list_view(value_type) -> LargeListViewType Create LargeListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of LargeListViewType: >>> import pyarrow as pa >>> pa.large_list_view(pa.int8()) LargeListViewType(large_list_view) list_view(value_type) -> ListViewType Create ListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of ListViewType: >>> import pyarrow as pa >>> pa.list_view(pa.string()) ListViewType(list_view) large_list(value_type) -> LargeListType Create LargeListType instance from child data type or field. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2**31 elements, you should prefer list_(). Parameters ---------- value_type : DataType or Field Returns ------- list_type : DataType Examples -------- Create an instance of LargeListType: >>> import pyarrow as pa >>> pa.large_list(pa.int8()) LargeListType(large_list) Use the LargeListType to create an array: >>> pa.array([[-1, 3]] * 5, type=pa.large_list(pa.int8())) [ [ -1, 3 ], [ -1, 3 ], ... list_(value_type, int list_size=-1) Create ListType instance from child data type or field. Parameters ---------- value_type : DataType or Field list_size : int, optional, default -1 If length == -1 then return a variable length list type. If length is greater than or equal to 0 then return a fixed size list type. Returns ------- list_type : DataType Examples -------- Create an instance of ListType: >>> import pyarrow as pa >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.int32(), 2) FixedSizeListType(fixed_size_list[2]) Use the ListType to create a scalar: >>> pa.scalar(['foo', None], type=pa.list_(pa.string(), 2)) or an array: >>> pa.array([[1, 2], [3, 4]], pa.list_(pa.int32(), 2)) [ [ 1, 2 ], [ 3, 4 ] ] string_view() Create UTF8 variable-length string view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string_view() DataType(string_view) binary_view() Create a variable-length binary view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.binary_view() DataType(binary_view) large_utf8() Alias for large_string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_utf8() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_utf8()) [ "foo", "bar", ... "foo", "bar" ] large_string() Create large UTF8 variable-length string type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_string() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_string()) [ "foo", "bar", ... "foo", "bar" ] large_binary() Create large variable-length binary type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer binary(). Examples -------- Create an instance of large variable-length binary type: >>> import pyarrow as pa >>> pa.large_binary() DataType(large_binary) and use the type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.large_binary()) [ 666F6F, 626172, 62617A ] binary(int length=-1) Create variable-length or fixed size binary type. Parameters ---------- length : int, optional, default -1 If length == -1 then return a variable length binary type. If length is greater than or equal to 0 then return a fixed size binary type of width `length`. Examples -------- Create an instance of a variable-length binary type: >>> import pyarrow as pa >>> pa.binary() DataType(binary) and use the variable-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary()) [ 666F6F, 626172, 62617A ] Create an instance of a fixed-size binary type: >>> pa.binary(3) FixedSizeBinaryType(fixed_size_binary[3]) and use the fixed-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary(3)) [ 666F6F, 626172, 62617A ] utf8() Alias for string(). Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.utf8() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.utf8()) [ "foo", "bar", "baz" ] string() Create UTF8 variable-length string type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.string()) [ "foo", "bar", "baz" ] decimal256(int precision, int scale=0) -> DataType Create decimal type with precision and scale and 256-bit width. Arrow decimals are fixed-point decimal numbers encoded as a scaled integer. The precision is the number of significant digits that the decimal type can represent; the scale is the number of digits after the decimal point (note the scale can be negative). For most use cases, the maximum precision offered by ``decimal128`` is sufficient, and it will result in a more compact and more efficient encoding. ``decimal256`` is useful if you need a precision higher than 38 significant digits. Parameters ---------- precision : int Must be between 1 and 76 scale : int Returns ------- decimal_type : Decimal256Type decimal128(int precision, int scale=0) -> DataType Create decimal type with precision and scale and 128-bit width. Arrow decimals are fixed-point decimal numbers encoded as a scaled integer. The precision is the number of significant digits that the decimal type can represent; the scale is the number of digits after the decimal point (note the scale can be negative). As an example, ``decimal128(7, 3)`` can exactly represent the numbers 1234.567 and -1234.567 (encoded internally as the 128-bit integers 1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567. ``decimal128(5, -3)`` can exactly represent the number 12345000 (encoded internally as the 128-bit integer 12345), but neither 123450000 nor 1234500. If you need a precision higher than 38 significant digits, consider using ``decimal256``. Parameters ---------- precision : int Must be between 1 and 38 scale : int Returns ------- decimal_type : Decimal128Type Examples -------- Create an instance of decimal type: >>> import pyarrow as pa >>> pa.decimal128(5, 2) Decimal128Type(decimal128(5, 2)) Create an array with decimal type: >>> import decimal >>> a = decimal.Decimal('123.45') >>> pa.array([a], pa.decimal128(5, 2)) [ 123.45 ] float64() Create double-precision floating point type. Examples -------- Create an instance of float64 type: >>> import pyarrow as pa >>> pa.float64() DataType(double) >>> print(pa.float64()) double Create an array with float64 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float64()) [ 0, 1, 2 ] float32() Create single-precision floating point type. Examples -------- Create an instance of float32 type: >>> import pyarrow as pa >>> pa.float32() DataType(float) >>> print(pa.float32()) float Create an array with float32 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float32()) [ 0, 1, 2 ] float16() Create half-precision floating point type. Examples -------- Create an instance of float16 type: >>> import pyarrow as pa >>> pa.float16() DataType(halffloat) >>> print(pa.float16()) halffloat Create an array with float16 type: >>> arr = np.array([1.5, np.nan], dtype=np.float16) >>> a = pa.array(arr, type=pa.float16()) >>> a [ 15872, 32256 ] Note that unlike other float types, if you convert this array to a python list, the types of its elements will be ``np.float16`` >>> [type(val) for val in a.to_pylist()] [, ] date64() Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01). Examples -------- Create an instance of 64-bit date type: >>> import pyarrow as pa >>> pa.date64() DataType(date64[ms]) Create a scalar with 64-bit date type: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.date64()) date32() Create instance of 32-bit date (days since UNIX epoch 1970-01-01). Examples -------- Create an instance of 32-bit date type: >>> import pyarrow as pa >>> pa.date32() DataType(date32[day]) Create a scalar with 32-bit date type: >>> from datetime import date >>> pa.scalar(date(2012, 1, 1), type=pa.date32()) month_day_nano_interval() Create instance of an interval type representing months, days and nanoseconds between two dates. Examples -------- Create an instance of an month_day_nano_interval type: >>> import pyarrow as pa >>> pa.month_day_nano_interval() DataType(month_day_nano_interval) Create a scalar with month_day_nano_interval type: >>> pa.scalar((1, 15, -30), type=pa.month_day_nano_interval()) duration(unit) Create instance of a duration type with unit resolution. Parameters ---------- unit : str One of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.DurationType Examples -------- Create an instance of duration type: >>> import pyarrow as pa >>> pa.duration('us') DurationType(duration[us]) >>> pa.duration('s') DurationType(duration[s]) Create an array with duration type: >>> pa.array([0, 1, 2], type=pa.duration('s')) [ 0, 1, 2 ] time64(unit) Create instance of 64-bit time (time of day) type with unit resolution. Parameters ---------- unit : str One of 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.Time64Type Examples -------- >>> import pyarrow as pa >>> pa.time64('us') Time64Type(time64[us]) >>> pa.time64('ns') Time64Type(time64[ns]) time32(unit) Create instance of 32-bit time (time of day) type with unit resolution. Parameters ---------- unit : str one of 's' [second], or 'ms' [millisecond] Returns ------- type : pyarrow.Time32Type Examples -------- >>> import pyarrow as pa >>> pa.time32('s') Time32Type(time32[s]) >>> pa.time32('ms') Time32Type(time32[ms]) timestamp(unit, tz=None) Create instance of timestamp type with resolution and optional time zone. Parameters ---------- unit : str one of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond] tz : str, default None Time zone name. None indicates time zone naive Examples -------- Create an instance of timestamp type: >>> import pyarrow as pa >>> pa.timestamp('us') TimestampType(timestamp[us]) >>> pa.timestamp('s', tz='America/New_York') TimestampType(timestamp[s, tz=America/New_York]) >>> pa.timestamp('s', tz='+07:30') TimestampType(timestamp[s, tz=+07:30]) Use timestamp type when creating a scalar object: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('s', tz='UTC')) >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('us')) Returns ------- timestamp_type : TimestampType string_to_tzinfo(name) Convert a time zone name into a time zone object. Supported input strings are: * As used in the Olson time zone database (the "tz database" or "tzdata"), such as "America/New_York" * An absolute time zone offset of the form +XX:XX or -XX:XX, such as +07:30 Parameters ---------- name: str Time zone name. Returns ------- tz : datetime.tzinfo Time zone object tzinfo_to_string(tz) Converts a time zone object into a string indicating the name of a time zone, one of: * As used in the Olson time zone database (the "tz database" or "tzdata"), such as "America/New_York" * An absolute time zone offset of the form +XX:XX or -XX:XX, such as +07:30 Parameters ---------- tz : datetime.tzinfo Time zone object Returns ------- name : str Time zone name int64() Create instance of signed int64 type. Examples -------- Create an instance of int64 type: >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> print(pa.int64()) int64 Create an array with int64 type: >>> pa.array([0, 1, 2], type=pa.int64()) [ 0, 1, 2 ] uint64() Create instance of unsigned uint64 type. Examples -------- Create an instance of unsigned int64 type: >>> import pyarrow as pa >>> pa.uint64() DataType(uint64) >>> print(pa.uint64()) uint64 Create an array with unsigned uint64 type: >>> pa.array([0, 1, 2], type=pa.uint64()) [ 0, 1, 2 ] int32() Create instance of signed int32 type. Examples -------- Create an instance of int32 type: >>> import pyarrow as pa >>> pa.int32() DataType(int32) >>> print(pa.int32()) int32 Create an array with int32 type: >>> pa.array([0, 1, 2], type=pa.int32()) [ 0, 1, 2 ] uint32() Create instance of unsigned uint32 type. Examples -------- Create an instance of unsigned int32 type: >>> import pyarrow as pa >>> pa.uint32() DataType(uint32) >>> print(pa.uint32()) uint32 Create an array with unsigned int32 type: >>> pa.array([0, 1, 2], type=pa.uint32()) [ 0, 1, 2 ] int16() Create instance of signed int16 type. Examples -------- Create an instance of int16 type: >>> import pyarrow as pa >>> pa.int16() DataType(int16) >>> print(pa.int16()) int16 Create an array with int16 type: >>> pa.array([0, 1, 2], type=pa.int16()) [ 0, 1, 2 ] uint16() Create instance of unsigned uint16 type. Examples -------- Create an instance of unsigned int16 type: >>> import pyarrow as pa >>> pa.uint16() DataType(uint16) >>> print(pa.uint16()) uint16 Create an array with unsigned int16 type: >>> pa.array([0, 1, 2], type=pa.uint16()) [ 0, 1, 2 ] int8() Create instance of signed int8 type. Examples -------- Create an instance of int8 type: >>> import pyarrow as pa >>> pa.int8() DataType(int8) >>> print(pa.int8()) int8 Create an array with int8 type: >>> pa.array([0, 1, 2], type=pa.int8()) [ 0, 1, 2 ] uint8() Create instance of unsigned int8 type. Examples -------- Create an instance of unsigned int8 type: >>> import pyarrow as pa >>> pa.uint8() DataType(uint8) >>> print(pa.uint8()) uint8 Create an array with unsigned int8 type: >>> pa.array([0, 1, 2], type=pa.uint8()) [ 0, 1, 2 ] bool_() Create instance of boolean type. Examples -------- Create an instance of a boolean type: >>> import pyarrow as pa >>> pa.bool_() DataType(bool) >>> print(pa.bool_()) bool Create a ``Field`` type with a boolean type and a name: >>> pa.field('bool_field', pa.bool_()) pyarrow.Field null() Create instance of null type. Examples -------- Create an instance of a null type: >>> import pyarrow as pa >>> pa.null() DataType(null) >>> print(pa.null()) null Create a ``Field`` type with a null type and a name: >>> pa.field('null_field', pa.null()) pyarrow.Field field(name, type=None, nullable=None, metadata=None) Create a pyarrow.Field instance. Parameters ---------- name : str or bytes Name of the field. Alternatively, you can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). type : pyarrow.DataType Arrow datatype of the field. nullable : bool, default True Whether the field's values are nullable. metadata : dict, default None Optional field metadata, the keys and values must be coercible to bytes. Returns ------- field : pyarrow.Field Examples -------- Create an instance of pyarrow.Field: >>> import pyarrow as pa >>> pa.field('key', pa.int32()) pyarrow.Field >>> pa.field('key', pa.int32(), nullable=False) pyarrow.Field >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field pyarrow.Field >>> field.metadata {b'key': b'Something important'} Use the field to create a struct type: >>> pa.struct([field]) StructType(struct) unify_schemas(schemas, *, promote_options=u'default') Unify schemas by merging fields by name. The resulting schema will contain the union of fields from all schemas. Fields with the same name will be merged. Note that two fields with different types will fail merging by default. - The unified field will inherit the metadata from the schema where that field is first defined. - The first N fields in the schema will be ordered the same as the N fields in the first schema. The resulting schema will inherit its metadata from the first input schema. Parameters ---------- schemas : list of Schema Schemas to merge into a single one. promote_options : str, default default Accepts strings "default" and "permissive". Default: null and only null can be unified with another type. Permissive: types are promoted to the greater common denominator. Returns ------- Schema Raises ------ ArrowInvalid : If any input schema contains fields with duplicate names. If Fields of the same name are not mergeable. Schema._import_from_c_capsule(schema) Import a Schema from a ArrowSchema PyCapsule Parameters ---------- schema : PyCapsule A valid PyCapsule with name 'arrow_schema' containing an ArrowSchema pointer. Schema.__arrow_c_schema__(self) Export to a ArrowSchema PyCapsule Unlike _export_to_c, this will not leak memory if the capsule is not used. Schema._import_from_c(in_ptr) Import Schema from a C ArrowSchema struct, given its pointer. This is a low-level function intended for expert users. Schema._export_to_c(self, out_ptr) Export to a C ArrowSchema struct, given its pointer. Be careful: if you don't pass the ArrowSchema struct to a consumer, its memory will leak. This is a low-level function intended for expert users. Schema.to_string(self, truncate_metadata=True, show_field_metadata=True, show_schema_metadata=True) Return human-readable representation of Schema Parameters ---------- truncate_metadata : boolean, default True Limit metadata key/value display to a single line of ~80 characters or less show_field_metadata : boolean, default True Display Field-level KeyValueMetadata show_schema_metadata : boolean, default True Display Schema-level KeyValueMetadata Returns ------- str : the formatted output Schema.remove_metadata(self) Create new schema without metadata, if any Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Create a new schema with removing the metadata from the original: >>> schema.remove_metadata() n_legs: int64 animals: string Schema.serialize(self, memory_pool=None) Write Schema to Buffer as encapsulated IPC message Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Write schema to Buffer: >>> schema.serialize() Schema.with_metadata(self, metadata) Add metadata as dict of string keys and values to Schema Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Add metadata to existing schema field: >>> schema.with_metadata({"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Schema.add_metadata(self, metadata) DEPRECATED Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Schema.set(self, int i, Field field) Replace a field at position i in the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Replace the second field of the schema with a new field 'extra': >>> schema.set(1, pa.field('replaced', pa.bool_())) n_legs: int64 replaced: bool Schema.remove(self, int i) Remove the field at index i from the schema. Parameters ---------- i : int Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Remove the second field of the schema: >>> schema.remove(1) n_legs: int64 Schema.insert(self, int i, Field field) Add a field at position i to the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Insert a new field on the second position: >>> schema.insert(1, pa.field('extra', pa.bool_())) n_legs: int64 extra: bool animals: string Schema.append(self, Field field) Append a field at the end of the schema. In contrast to Python's ``list.append()`` it does return a new object, leaving the original Schema unmodified. Parameters ---------- field : Field Returns ------- schema: Schema New object with appended field. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Append a field 'extra' at the end of the schema: >>> schema_new = schema.append(pa.field('extra', pa.bool_())) >>> schema_new n_legs: int64 animals: string extra: bool Original schema is unmodified: >>> schema n_legs: int64 animals: string Schema.get_all_field_indices(self, name) Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())]) Get the indexes of the fields named 'animals': >>> schema.get_all_field_indices("animals") [1, 2] Schema.get_field_index(self, name) Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the index of the field named 'animals': >>> schema.get_field_index("animals") 1 Index in case of several fields with the given name: >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema.get_field_index("animals") -1 Schema.field_by_name(self, name) DEPRECATED Parameters ---------- name : str Returns ------- field: pyarrow.Field Schema._field(self, int i) Select a field by its numeric index. Parameters ---------- i : int Returns ------- pyarrow.Field Schema.field(self, i) Select a field by its column name or numeric index. Parameters ---------- i : int or string Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Select the second field: >>> schema.field(1) pyarrow.Field Select the field of the column named 'n_legs': >>> schema.field('n_legs') pyarrow.Field Schema.from_pandas(cls, df, preserve_index=None) Returns implied schema from dataframe Parameters ---------- df : pandas.DataFrame preserve_index : bool, default True Whether to store the index as an additional column (or columns, for MultiIndex) in the resulting `Table`. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. Returns ------- pyarrow.Schema Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({ ... 'int': [1, 2], ... 'str': ['a', 'b'] ... }) Create an Arrow Schema from the schema of a pandas dataframe: >>> pa.Schema.from_pandas(df) int: int64 str: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, ... Schema.equals(self, Schema other, bool check_metadata=False) Test if this schema is equal to the other Parameters ---------- other : pyarrow.Schema check_metadata : bool, default False Key/value metadata must be equal too Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> schema1 = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema2 = pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ]) Test two equal schemas: >>> schema1.equals(schema1) True Test two unequal schemas: >>> schema1.equals(schema2) False Schema.empty_table(self) Provide an empty table according to the schema. Returns ------- table: pyarrow.Table Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Create an empty table with schema's fields: >>> schema.empty_table() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[]] animals: [[]] Schema.__sizeof__(self)Schema.__reduce__(self)Field._import_from_c_capsule(schema) Import a Field from a ArrowSchema PyCapsule Parameters ---------- schema : PyCapsule A valid PyCapsule with name 'arrow_schema' containing an ArrowSchema pointer. Field.__arrow_c_schema__(self) Export to a ArrowSchema PyCapsule Unlike _export_to_c, this will not leak memory if the capsule is not used. Field._import_from_c(in_ptr) Import Field from a C ArrowSchema struct, given its pointer. This is a low-level function intended for expert users. Field._export_to_c(self, out_ptr) Export to a C ArrowSchema struct, given its pointer. Be careful: if you don't pass the ArrowSchema struct to a consumer, its memory will leak. This is a low-level function intended for expert users. Field.flatten(self) Flatten this field. If a struct field, individual child fields will be returned with their names prefixed by the parent's name. Returns ------- fields : List[pyarrow.Field] Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('bar', pa.float64(), nullable=False) >>> f2 = pa.field('foo', pa.int32()).with_metadata({"key": "Something important"}) >>> ff = pa.field('ff', pa.struct([f1, f2]), nullable=False) Flatten a struct field: >>> ff pyarrow.Field not null> >>> ff.flatten() [pyarrow.Field, pyarrow.Field] Field.with_nullable(self, nullable) A copy of this field with the replaced nullability Parameters ---------- nullable : bool Returns ------- field: pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field >>> field.nullable True Create new field by replacing the nullability of an existing one: >>> field_new = field.with_nullable(False) >>> field_new pyarrow.Field >>> field_new.nullable False Field.with_name(self, name) A copy of this field with the replaced name Parameters ---------- name : str Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing the name of an existing one: >>> field_new = field.with_name('lock') >>> field_new pyarrow.Field Field.with_type(self, DataType new_type) A copy of this field with the replaced type Parameters ---------- new_type : pyarrow.DataType Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing type of an existing one: >>> field_new = field.with_type(pa.int64()) >>> field_new pyarrow.Field Field.remove_metadata(self) Create new field without metadata, if any Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} Create new field by removing the metadata from the existing one: >>> field_new = field.remove_metadata() >>> field_new.metadata Field.with_metadata(self, metadata) Add metadata as dict of string keys and values to Field Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) Create new field by adding metadata to existing one: >>> field_new = field.with_metadata({"key": "Something important"}) >>> field_new pyarrow.Field >>> field_new.metadata {b'key': b'Something important'} Field.__reduce__(self)Field.equals(self, Field other, bool check_metadata=False) Test if this field is equal to the other Parameters ---------- other : pyarrow.Field check_metadata : bool, default False Whether Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.equals(f2) False >>> f1.equals(f1) True ensure_metadata(meta, bool allow_none=False) -> KeyValueMetadataKeyValueMetadata.to_dict(self) Convert KeyValueMetadata to dict. If a key occurs twice, the value for the first one is returned KeyValueMetadata.get_all(self, key) Parameters ---------- key : str Returns ------- list[byte] KeyValueMetadata.items(self)KeyValueMetadata.values(self)KeyValueMetadata.keys(self)KeyValueMetadata.value(self, i) Parameters ---------- i : int Returns ------- byte KeyValueMetadata.key(self, i) Parameters ---------- i : int Returns ------- byte KeyValueMetadata.__reduce__(self)KeyValueMetadata.equals(self, KeyValueMetadata other) Parameters ---------- other : pyarrow.KeyValueMetadata Returns ------- bool unregister_extension_type(type_name) Unregister a Python extension type. Parameters ---------- type_name : str The name of the ExtensionType subclass to unregister. Examples -------- Define a RationalType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class RationalType(pa.ExtensionType): ... def __init__(self, data_type: pa.DataType): ... if not pa.types.is_integer(data_type): ... raise TypeError(f"data_type must be an integer type not {data_type}") ... super().__init__( ... pa.struct( ... [ ... ("numer", data_type), ... ("denom", data_type), ... ], ... ), ... # N.B. This name does _not_ reference `data_type` so deserialization ... # will work for _any_ integer `data_type` after registration ... "my_package.rational", ... ) ... def __arrow_ext_serialize__(self) -> bytes: ... # No parameters are necessary ... return b"" ... @classmethod ... def __arrow_ext_deserialize__(cls, storage_type, serialized): ... # return an instance of this subclass ... return RationalType(storage_type[0].type) Register the extension type: >>> pa.register_extension_type(RationalType(pa.int64())) Unregister the extension type: >>> pa.unregister_extension_type("my_package.rational") register_extension_type(ext_type) Register a Python extension type. Registration is based on the extension name (so different registered types need unique extension names). Registration needs an extension type instance, but then works for any instance of the same subclass regardless of parametrization of the type. Parameters ---------- ext_type : BaseExtensionType instance The ExtensionType subclass to register. Examples -------- Define a RationalType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class RationalType(pa.ExtensionType): ... def __init__(self, data_type: pa.DataType): ... if not pa.types.is_integer(data_type): ... raise TypeError(f"data_type must be an integer type not {data_type}") ... super().__init__( ... pa.struct( ... [ ... ("numer", data_type), ... ("denom", data_type), ... ], ... ), ... # N.B. This name does _not_ reference `data_type` so deserialization ... # will work for _any_ integer `data_type` after registration ... "my_package.rational", ... ) ... def __arrow_ext_serialize__(self) -> bytes: ... # No parameters are necessary ... return b"" ... @classmethod ... def __arrow_ext_deserialize__(cls, storage_type, serialized): ... # return an instance of this subclass ... return RationalType(storage_type[0].type) Register the extension type: >>> pa.register_extension_type(RationalType(pa.int64())) Unregister the extension type: >>> pa.unregister_extension_type("my_package.rational") UnknownExtensionType.__arrow_ext_serialize__(self)PyExtensionType.set_auto_load(cls, value) Enable or disable auto-loading of serialized PyExtensionType instances. Parameters ---------- value : bool Whether to enable auto-loading. PyExtensionType.__arrow_ext_deserialize__(cls, storage_type, serialized)PyExtensionType.__arrow_ext_serialize__(self)PyExtensionType.__reduce__(self)OpaqueType.__arrow_ext_scalar_class__(self)OpaqueType.__reduce__(self)OpaqueType.__arrow_ext_class__(self)Bool8Type.__arrow_ext_scalar_class__(self)Bool8Type.__reduce__(self)Bool8Type.__arrow_ext_class__(self)FixedShapeTensorType.__arrow_ext_scalar_class__(self)FixedShapeTensorType.__reduce__(self)FixedShapeTensorType.__arrow_ext_class__(self)UuidType.__arrow_ext_scalar_class__(self)UuidType.__reduce__(self)UuidType.__arrow_ext_class__(self)ExtensionType.__arrow_ext_scalar_class__(self) Return an extension scalar class for building scalars with this extension type. This method should return subclass of the ExtensionScalar class. By default, if not specialized in the extension implementation, an extension type scalar will be a built-in ExtensionScalar instance. ExtensionType.__arrow_ext_class__(self) Return an extension array class to be used for building or deserializing arrays with this extension type. This method should return a subclass of the ExtensionArray class. By default, if not specialized in the extension implementation, an extension type array will be a built-in ExtensionArray instance. ExtensionType.__reduce__(self)ExtensionType.__arrow_ext_deserialize__(cls, storage_type, serialized) Return an extension type instance from the storage type and serialized metadata. This method should return an instance of the ExtensionType subclass that matches the passed storage type and serialized metadata (the return value of ``__arrow_ext_serialize__``). ExtensionType.__arrow_ext_serialize__(self) Serialized representation of metadata to reconstruct the type object. This method should return a bytes object, and those serialized bytes are stored in the custom metadata of the Field holding an extension type in an IPC message. The bytes are passed to ``__arrow_ext_deserialize`` and should hold sufficient information to reconstruct the data type instance. Initialize an extension type instance. This should be called at the end of the subclass' ``__init__`` method. BaseExtensionType.wrap_array(self, storage) Wrap the given storage array as an extension array. Parameters ---------- storage : Array or ChunkedArray Returns ------- array : Array or ChunkedArray Extension array wrapping the storage array BaseExtensionType.__arrow_ext_scalar_class__(self) The associated scalar class BaseExtensionType.__arrow_ext_class__(self) The associated array extension class RunEndEncodedType.__reduce__(self)Decimal256Type.__reduce__(self)Decimal128Type.__reduce__(self)FixedSizeBinaryType.__reduce__(self)TimestampType.__reduce__(self)UnionType.__reduce__(self) Return a child field by its index. Alias of ``field``. UnionType.field(self, i) -> Field Return a child field by its numeric index. Parameters ---------- i : int Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union[0] pyarrow.Field Iterate over union members, in order. Like num_fields(). StructType.__reduce__(self) Return the struct field with the given index or name. Alias of ``field``. Iterate over struct fields, in order. Like num_fields(). StructType.get_all_field_indices(self, name) Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct_type.get_all_field_indices('x') [0] StructType.field(self, i) -> Field Select a field by its column name or numeric index. Parameters ---------- i : int or str Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Select the second field: >>> struct_type.field(1) pyarrow.Field Select the field named 'x': >>> struct_type.field('x') pyarrow.Field StructType.get_field_index(self, name) Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Index of the field with a name 'y': >>> struct_type.get_field_index('y') 1 Index of the field that does not exist: >>> struct_type.get_field_index('z') -1 FixedSizeListType.__reduce__(self)MapType.__reduce__(self)LargeListViewType.__reduce__(self)ListViewType.__reduce__(self)LargeListType.__reduce__(self)ListType.__reduce__(self)DictionaryType.__reduce__(self)DictionaryMemo.__setstate_cython__(self, __pyx_state)DictionaryMemo.__reduce_cython__(self)DataType._import_from_c_capsule(schema) Import a DataType from a ArrowSchema PyCapsule Parameters ---------- schema : PyCapsule A valid PyCapsule with name 'arrow_schema' containing an ArrowSchema pointer. DataType.__arrow_c_schema__(self) Export to a ArrowSchema PyCapsule Unlike _export_to_c, this will not leak memory if the capsule is not used. DataType._import_from_c(in_ptr) Import DataType from a C ArrowSchema struct, given its pointer. This is a low-level function intended for expert users. DataType._export_to_c(self, out_ptr) Export to a C ArrowSchema struct, given its pointer. Be careful: if you don't pass the ArrowSchema struct to a consumer, its memory will leak. This is a low-level function intended for expert users. DataType.to_pandas_dtype(self) Return the equivalent NumPy / Pandas dtype. Examples -------- >>> import pyarrow as pa >>> pa.int64().to_pandas_dtype() DataType.equals(self, other, *, check_metadata=False) Return true if type is equivalent to passed value. Parameters ---------- other : DataType or string convertible to DataType check_metadata : bool Whether nested Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> pa.int64().equals(pa.string()) False >>> pa.int64().equals(pa.int64()) True DataType.__reduce__(self)DataType.field(self, i) -> Field Parameters ---------- i : int Returns ------- pyarrow.Field _to_pandas_dtype(arrow_type, options=None)_get_pandas_tz_type(arrow_type, coerce_to_ns=False)_get_pandas_type(arrow_type, coerce_to_ns=False)_is_primitive(Type type)_get_pandas_type_map()default_cpu_memory_manager() Return the default CPU MemoryManager instance. The returned singleton instance uses the default MemoryPool. MemoryManager.__setstate_cython__(self, __pyx_state)MemoryManager.__reduce_cython__(self)Device.__setstate_cython__(self, __pyx_state)Device.__reduce_cython__(self)supported_memory_backends() Return a list of available memory pool backends jemalloc_set_decay_ms(decay_ms) Set arenas.dirty_decay_ms and arenas.muzzy_decay_ms to indicated number of milliseconds. A value of 0 (the default) results in dirty / muzzy memory pages being released right away to the OS, while a higher value will result in a time-based decay. See the jemalloc docs for more information It's best to set this at the start of your application. Parameters ---------- decay_ms : int Number of milliseconds to set for jemalloc decay conf parameters. Note that this change will only affect future memory arenas total_allocated_bytes() Return the currently allocated bytes from the default memory pool. Other memory pools may not be accounted for. log_memory_allocations(enable=True) Enable or disable memory allocator logging for debugging purposes Parameters ---------- enable : bool, default True Pass False to disable logging set_memory_pool(MemoryPool pool) Set the default memory pool. Parameters ---------- pool : MemoryPool The memory pool that should be used by default. mimalloc_memory_pool() Return a memory pool based on the mimalloc heap. NotImplementedError is raised if mimalloc support is not enabled. jemalloc_memory_pool() Return a memory pool based on the jemalloc heap. NotImplementedError is raised if jemalloc support is not enabled. system_memory_pool() Return a memory pool based on the C malloc heap. logging_memory_pool(MemoryPool parent) Create and return a MemoryPool instance that redirects to the *parent*, but also dumps allocation logs on stderr. Parameters ---------- parent : MemoryPool The real memory pool that should be used for allocations. proxy_memory_pool(MemoryPool parent) Create and return a MemoryPool instance that redirects to the *parent*, but with separate allocation statistics. Parameters ---------- parent : MemoryPool The real memory pool that should be used for allocations. default_memory_pool() Return the process-global memory pool. Examples -------- >>> default_memory_pool() ProxyMemoryPool.__setstate_cython__(self, __pyx_state)ProxyMemoryPool.__reduce_cython__(self)LoggingMemoryPool.__setstate_cython__(self, __pyx_state)LoggingMemoryPool.__reduce_cython__(self)MemoryPool.__setstate_cython__(self, __pyx_state)MemoryPool.__reduce_cython__(self)MemoryPool.max_memory(self) Return the peak memory allocation in this memory pool. This can be an approximate number in multi-threaded applications. None is returned if the pool implementation doesn't know how to compute this number. MemoryPool.bytes_allocated(self) Return the number of bytes that are currently allocated from this memory pool. MemoryPool.release_unused(self) Attempt to return to the OS any memory being held onto by the pool. This function should not be called except potentially for benchmarking or debugging as it could be expensive and detrimental to performance. This is best effort and may not have any effect on some memory pools or in some situations (e.g. fragmentation). _PandasAPIShim.__setstate_cython__(self, __pyx_state)_PandasAPIShim.__reduce_cython__(self)_PandasAPIShim.get_rangeindex_attribute(self, level, name)_PandasAPIShim.get_values(self, obj) Get the underlying array values of a pandas Series or Index in the format (np.ndarray or pandas ExtensionArray) as we need them. Assumes obj is a pandas Series or Index. _PandasAPIShim.is_index(self, obj)_PandasAPIShim.is_series(self, obj)_PandasAPIShim.is_data_frame(self, obj)_PandasAPIShim.is_sparse(self, obj)_PandasAPIShim.is_extension_array_dtype(self, obj)_PandasAPIShim.is_datetimetz(self, obj)_PandasAPIShim.is_categorical(self, obj)_PandasAPIShim.is_array_like(self, obj)_PandasAPIShim.is_ge_v3(self)_PandasAPIShim.is_ge_v21(self)_PandasAPIShim.is_v1(self)_PandasAPIShim.pandas_dtype(self, dtype)_PandasAPIShim.infer_dtype(self, obj)_PandasAPIShim.data_frame(self, *args, **kwargs)_PandasAPIShim.series(self, *args, **kwargs)set_timezone_db_path(path) Configure the path to text timezone database on Windows. Parameters ---------- path : str Path to text timezone database. runtime_info() Get runtime information. Returns ------- info : pyarrow.RuntimeInfo SignalStopHandler.__setstate_cython__(self, __pyx_state)SignalStopHandler.__reduce_cython__(self)SignalStopHandler.__exit__(self, exc_type, exc_value, exc_tb)SignalStopHandler.__enter__(self)SignalStopHandler._init_signals(self)enable_signal_handlers(bool enable) Enable or disable interruption of long-running operations. By default, certain long running operations will detect user interruptions, such as by pressing Ctrl-C. This detection relies on setting a signal handler for the duration of the long-running operation, and may therefore interfere with other frameworks or libraries (such as an event loop). Parameters ---------- enable : bool Whether to enable user interruption by setting a temporary signal handler. StopToken.__setstate_cython__(self, __pyx_state)StopToken.__reduce_cython__(self)ArrowCancelled.__init__(self, message, signum=None)ArrowKeyError.__str__(self)frombytes(o, *, safe=False) Decode the given bytestring to unicode. Parameters ---------- o : bytes-like Input object. safe : bool, default False If true, raise on encoding errors. tobytes(o) Encode a unicode or bytes string to bytes. Parameters ---------- o : str or bytes Input string. encode_file_path(path)_gdb_test_session()_ensure_cuda_loaded()_pac()_pc()is_threading_enabled() -> bool Returns True if threading is enabled in libarrow. If it isn't enabled, then python shouldn't create any threads either, because we're probably on a system where threading doesn't work (e.g. Emscripten). set_cpu_count(int count) Set the number of threads to use in parallel operations. Parameters ---------- count : int The number of concurrent threads that should be used. See Also -------- cpu_count : Get the size of this pool. set_io_thread_count : The analogous function for the I/O thread pool. cpu_count() Return the number of threads to use in parallel operations. The number of threads is determined at startup by inspecting the ``OMP_NUM_THREADS`` and ``OMP_THREAD_LIMIT`` environment variables. If neither is present, it will default to the number of hardware threads on the system. It can be modified at runtime by calling :func:`set_cpu_count()`. See Also -------- set_cpu_count : Modify the size of this pool. io_thread_count : The analogous function for the I/O thread pool. pyarrow requires pandas 1.0.0 or above, pandas {} is installed. Therefore, pandas-specific integration is not used.precision should be between 1 and 76Unable to avoid a copy while creating a numpy array as requested (converting a pyarrow.Unable to avoid a copy while creating a numpy array as requested (converting a pyarrow.ChunkedArray always results in a copy). If using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when neededThe 'names' and 'metadata' arguments are not valid when using Arrow PyCapsule InterfaceStructType.get_all_field_indices (line 991)RunEndEncodedType's expected number of buffers ({0}) did not match the passed number ({1}).RunEndEncodedArray.find_physical_lengthRecordBatch.replace_schema_metadata (line 2593)RecordBatch.get_total_buffer_size (line 2762)RecordBatch._import_from_c_device_capsuleRecordBatchReader.read_next_batch_with_custom_metadataRecordBatchReader._import_from_c_capsuleMust pass either names or fields, not bothIncompatible checksums (0x%x vs (0xa1b2f29, 0xabbf506, 0x682aee0) = (_array_like_types, _categorical_type, _compat_module, _data_frame, _datetimetz_type, _extension_array, _extension_dtype, _have_pandas, _index, _is_extension_array_dtype, _is_ge_v21, _is_ge_v3, _is_v1, _lock, _loose_version, _pd, _pd024, _series, _tried_importing_pandas, _types_api, _version, has_sparse))Implemented only for data on CPU device or data with equal addressesFixedSizeBufferWriter.set_memcopy_thresholdFixedSizeBufferWriter.set_memcopy_blocksizeFixedShapeTensorType.__arrow_ext_scalar_class__FixedShapeTensorArray.from_numpy_ndarray (line 4437)Expected a list of 1-dimensional arrays for SparseCSFTensor.indicesExpected 1-dimensional array for SparseCSCMatrix indicesExpected 1-dimensional array for SparseCSCMatrix indptrExpected 1-dimensional array for SparseCSRMatrix indicesDo not call {}'s constructor directly, use one of the RecordBatchReader.from_* functions instead.Do not call {}'s constructor directly, use `pyarrow.ipc.MessageReader.open_stream` function instead.Do not call {}'s constructor directly, use `pyarrow.ipc.read_message` function instead.Do not call {}'s constructor directly, use one of the `pyarrow.Array.from_*` functions instead.Do not call {}'s constructor directly, use pa.scalar() instead.Do not call {}'s constructor directly, use public functions like pyarrow.int64, pyarrow.list_, etc. instead.Do not call {}'s constructor directly, use pyarrow.proxy_memory_pool instead.Do not call {}'s constructor directly, use pyarrow.logging_memory_pool instead.ChunkedArray.get_total_buffer_size (line 261)BaseListArray.value_parent_indices (line 2433) Unify dictionaries across all chunks. This method returns an equivalent table, but where all chunks of each column share the same dictionary values. Dictionary indices are transposed accordingly. Columns without dictionaries are returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> table = pa.table([c_arr], names=["animals"]) >>> table pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog"] -- indices: [0,1,2], -- dictionary: ["Horse","Brittle stars","Centipede"] -- indices: [0,1,2]] Unify dictionaries across both chunks: >>> table.unify_dictionaries() pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [0,1,2], -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [3,4,5]] The sum of bytes in each buffer referenced by the table. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.get_total_buffer_size() 76 The sum of bytes in each buffer referenced by the record batch An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.get_total_buffer_size() 120 The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_field pyarrow.Field The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.scale 38 The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.precision 76 The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_type DataType(int32) Target schema's field names are not matching the table's field names: Target schema's field names are not matching the table's field names: {!r}, {!r} Select a field by its column name or numeric index. Parameters ---------- i : int or string Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Select the second field: >>> schema.field(1) pyarrow.Field Select the field of the column named 'n_legs': >>> schema.field('n_legs') pyarrow.Field Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())]) Get the indexes of the fields named 'animals': >>> schema.get_all_field_indices("animals") [1, 2] Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the index of the field named 'animals': >>> schema.get_field_index("animals") 1 Index in case of several fields with the given name: >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema.get_field_index("animals") -1 Return boolean array indicating the non-null values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_valid() [ [ true, true, true ], [ true, false, true ] ] Return boolean array indicating the NaN values. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]]) >>> arr.is_nan() [ [ false, true, false, false, null, false ] ] _RecordBatchFileReader.get_batch_with_custom_metadataExpected list of {ndim} np.arrays for SparseCSFTensor.indicesExpected an object implementing the Arrow PyCapsule Protocol for schema (i.e. having a `__arrow_c_schema__` method), got Create instance of unsigned uint64 type. Examples -------- Create an instance of unsigned int64 type: >>> import pyarrow as pa >>> pa.uint64() DataType(uint64) >>> print(pa.uint64()) uint64 Create an array with unsigned uint64 type: >>> pa.array([0, 1, 2], type=pa.uint64()) [ 0, 1, 2 ] Create instance of unsigned uint32 type. Examples -------- Create an instance of unsigned int32 type: >>> import pyarrow as pa >>> pa.uint32() DataType(uint32) >>> print(pa.uint32()) uint32 Create an array with unsigned int32 type: >>> pa.array([0, 1, 2], type=pa.uint32()) [ 0, 1, 2 ] Create UTF8 variable-length string view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string_view() DataType(string_view) Convert the Table or RecordBatch to a list of rows / dictionaries. Returns ------- list Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> data = [[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]] >>> table = pa.table(data, names=["n_legs", "animals"]) >>> table.to_pylist() [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ... Construct a Table or RecordBatch from list of rows / dictionaries. Parameters ---------- mapping : list of dicts of rows A mapping of strings to row values. schema : Schema, default None If not passed, will be inferred from the first row of the mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, ... {'n_legs': 4, 'animals': 'Dog'}] Construct a Table from a list of rows: >>> pa.Table.from_pylist(pylist) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4]] animals: [["Flamingo","Dog"]] Construct a Table from a list of rows with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a list of rows with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pylist(pylist, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Compute zero-copy slice of this Table. Parameters ---------- offset : int, default 0 Offset from start of table to slice. length : int, default None Length of slice (default is until end of table starting from offset). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.slice(length=3) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019]] n_legs: [[2,4,5]] animals: [["Flamingo","Horse","Brittle stars"]] >>> table.slice(offset=2) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019,2021]] n_legs: [[5,100]] animals: [["Brittle stars","Centipede"]] >>> table.slice(offset=2, length=1) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019]] n_legs: [[5]] animals: [["Brittle stars"]] Compute zero-copy slice of this RecordBatch Parameters ---------- offset : int, default 0 Offset from start of record batch to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> batch.slice(offset=3).to_pandas() n_legs animals 0 4 Horse 1 5 Brittle stars 2 100 Centipede >>> batch.slice(length=2).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot >>> batch.slice(offset=3, length=1).to_pandas() n_legs animals 0 4 Horse ChunkedArray.unify_dictionaries (line 1145)Can only instantiate subclasses of PyExtensionType A copy of this field with the replaced nullability Parameters ---------- nullable : bool Returns ------- field: pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field >>> field.nullable True Create new field by replacing the nullability of an existing one: >>> field_new = field.with_nullable(False) >>> field_new pyarrow.Field >>> field_new.nullable False A copy of this field with the replaced name Parameters ---------- name : str Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing the name of an existing one: >>> field_new = field.with_name('lock') >>> field_new pyarrow.Field zero_copy_only must be False for pyarrow.ChunkedArray.to_numpyunregister_extension_type (line 2156)self.wrapped cannot be converted to a Python object for picklingself.stop_token cannot be converted to a Python object for picklingself.sp_tensor,self.tp cannot be converted to a Python object for picklingself.sp_sparse_tensor,self.stp cannot be converted to a Python object for picklingself.pool,self.proxy_pool cannot be converted to a Python object for picklingself.pool cannot be converted to a Python object for picklingself.memory_manager cannot be converted to a Python object for picklingself.logging_pool,self.pool cannot be converted to a Python object for picklingself.c_options cannot be converted to a Python object for picklingregister_extension_type (line 2092)read_next_batch_with_custom_metadatapyarrow requires pandas 1.0.0 or above, pandas {} is installedpyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism.promote has been superseded by promote_options='default'.precision should be between 1 and 38pa.output_stream() called with instance of '{}'pa.input_stream() called with instance of '{}'only slices with step 1 supportedno default __reduce__ due to non-trivial __cinit__names must be a list or dict not month_day_nano_interval (line 4290)list_size should be a positive integeriter_batches_with_custom_metadataideal_bandwidth_utilization_fracfield or tuple expected, got Nonecould not infer open mode for file-like object cannot specify 'type' when creating a Field from an ArrowSchemabinary file expected, got text fileWritable buffer requested but Arrow buffer was not mutableUuidType.__arrow_ext_scalar_class__UnknownExtensionType.__arrow_ext_serialize__UnionType.type_codes.__get__ (line 1118)UnionType.mode.__get__ (line 1098)Unable to write to object of type: {0}Unable to wrap Datum in a Python objectUnable to read message from object with type: {0}Unable to avoid a copy while creating a numpy array as requested. If using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when neededType's expected number of children ({0}) did not match the passed number ({1}).Type's expected number of buffers ({0}) did not match the passed number ({1}).TransformInputStream.__reduce_cython__ Total number of bytes consumed by the elements of the table. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.nbytes 72 Total number of bytes consumed by the elements of the record batch. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.nbytes 116 TimestampType.tz.__get__ (line 1259)Time zones are not available from the C-API.Time64Type.unit.__get__ (line 1335)Time32Type.unit.__get__ (line 1300)This object's internal pointer is NULL, do not use any methods or attributes on this objectThe run_end_type should be 'int16', 'int32', or 'int64'The passed mapping doesn't contain the following field(s) of the schema: {}The 'ordered' flag of the passed categorical values does not match the 'ordered' of the specified type. The object's __arrow_array__ method does not return a pyarrow Array or ChunkedArray.The 'names' and 'metadata' arguments are not valid when passing a pandas DataFrameThe 'field_by_name' method is deprecated, use 'field' insteadThe dtype of the 'categories' of the passed categorical values ({0}) does not match the specified type ({1}). For now ignoring the specified type, but in the future this mismatch will raise a TypeErrorThe dictionary index type should be integer.The 'add_metadata' method is deprecated, use 'with_metadata' insteadThe Scalar value passed as index must have identical type to the dictionary type's index_typeThe Array passed as dictionary must have identical type to the dictionary type's value_typeTensor.is_mutable.__get__ (line 186)Tensor.is_contiguous.__get__ (line 202)Tensor.dim_names.__get__ (line 170)Tensor._make_shape_or_strides_bufferTable.unify_dictionaries (line 4516)Table.replace_schema_metadata (line 4339)Table.num_rows.__get__ (line 5202)Table.num_columns.__get__ (line 5181)Table.get_total_buffer_size (line 5261)Table.from_struct_array (line 4871)TableGroupBy.aggregate (line 6396)Struct field name corresponds to more than one fieldStructType.names.__get__ (line 1037)StructType.get_all_field_indicesStructType.fields.__get__ (line 1051)StringViewBuilder only accepts string objectsStringViewBuilder.__setstate_cython__StringViewBuilder.__reduce_cython__StringBuilder only accepts string objectsSparseCSRMatrix.from_dense_numpySparseCSRMatrix.__setstate_cython__SparseCSRMatrix.__get__..genexprSparseCSFTensor.from_dense_numpySparseCSFTensor.__setstate_cython__SparseCSFTensor.__get__..genexprSparseCSCMatrix.from_dense_numpySparseCSCMatrix.__setstate_cython__SparseCSCMatrix.__get__..genexprSparseCOOTensor.to_pydata_sparseSparseCOOTensor.from_pydata_sparseSparseCOOTensor.from_dense_numpySparseCOOTensor.__setstate_cython__SparseCOOTensor.__get__..genexprSignalStopHandler.__setstate_cython__SignalStopHandler.__reduce_cython__ Select values from the chunked array. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the array whose values will be returned. Returns ------- taken : Array or ChunkedArray An array with the same datatype, containing the taken values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.take([1,4,5]) [ [ 2, 5, 100 ] ] Schema.remove_metadata (line 3456)Schema passed to names= option, please pass schema= explicitly. Will raise exception in futureSchema.pandas_metadata.__get__ (line 2852)Schema must be an instance of pyarrow.SchemaSchema.metadata.__get__ (line 2929)Schema.get_field_index (line 3160)Schema.get_all_field_indices (line 3200)Schema field name corresponds to more than one fieldSchema and number of arrays unequalRunEndEncodedType's expected number of children ({0}) did not match the passed number ({1}).RunEndEncodedType's expected null_count (0) did not match passed number ({0})RunEndEncodedType expects None as validity bitmap, buffers[0] is not NoneRunEndEncodedArray.find_physical_offset Return the underlying array of values which backs the FixedSizeListArray. Note even null elements are included. Compare with :meth:`flatten`, which returns only the non-null sub-list values. Returns ------- values : Array See Also -------- FixedSizeListArray.flatten : ... Examples -------- >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, None]], ... type=pa.list_(pa.int32(), 2) ... ) >>> array.values [ 1, 2, null, null, 3, null ] Return the underlying array of values which backs the LargeListArray ignoring the array's offset. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the underlying array of values which backs the ListViewArray ignoring the array's offset and sizes. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the underlying array of values which backs the LargeListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from the sub-lists: >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, 4, None, 6]], ... type=pa.large_list(pa.int32()), ... ) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] RecordBatch with its custom metadata Parameters ---------- batch : RecordBatch custom_metadata : KeyValueMetadata RecordBatch.set_column (line 2908)RecordBatch.replace_schema_metadataRecordBatch.remove_column (line 2874)RecordBatch.num_rows.__get__ (line 2660)RecordBatch.get_total_buffer_sizeRecordBatch.from_struct_array (line 3519)RecordBatch.from_pandas (line 3325)RecordBatch.from_arrays (line 3420)RecordBatch.add_column (line 2793)RecordBatch._import_from_c_deviceRecordBatch._import_from_c_capsuleRecordBatch.__arrow_c_device_array__RecordBatchReader.read_next_batchRecordBatchReader.iter_batches_with_custom_metadataRecordBatchReader._import_from_cRecordBatchReader.__setstate_cython__RecordBatchReader.__reduce_cython__RecordBatchReader.__arrow_c_stream__Received unsupported keyword argument(s): PyExtensionType.__arrow_ext_serialize__PyExtensionType.__arrow_ext_deserialize__ProxyMemoryPool.__setstate_cython__Property `compression` must be None, str, or pyarrow.CodecPassing a pointer value as a float is unsafe and only supported for compatibility with older versions of the R Arrow libraryOpaqueType.__arrow_ext_scalar_class__Only extension types can be registeredNanosecond resolution temporal type {} is not safely convertible to microseconds to convert to datetime.datetime. Install pandas to return as Timestamp with nanosecond support or access the .value attribute.Nanosecond duration {} is not safely convertible to microseconds to convert to datetime.timedelta. Install pandas to return as Timedelta with nanosecond support or access the .value attribute.Must pass schema, or at least one RecordBatchMust pass names or schema when constructing Table or RecordBatch.Must pass either names or fieldsMust pass a DictionaryType instanceMonthDayNanoIntervalScalar.as_pyMonthDayNanoIntervalArray.to_pylistMockOutputStream.__setstate_cython__MockOutputStream.__reduce_cython__MemoryMappedFile.__setstate_cython__MemoryMappedFile.__reduce_cython__Map key field should be non-nullableMapType.keys_sorted.__get__ (line 780)MapType.key_type.__get__ (line 741)MapType.key_field.__get__ (line 728)MapType.item_type.__get__ (line 767)LoggingMemoryPool.__setstate_cython__LoggingMemoryPool.__reduce_cython__ListView requires DataType or FieldListViewArray.values.__get__ (line 2908)ListViewArray.sizes.__get__ (line 2992)ListViewArray.offsets.__get__ (line 2962)ListViewArray.from_arrays (line 2807)ListType.value_type.__get__ (line 558)ListType.value_field.__get__ (line 545)ListArray.values.__get__ (line 2560)ListArray.offsets.__get__ (line 2631)Length of names ({}) does not match length of arrays ({})LargeListViewType.value_type.__get__ (line 692)LargeListViewArray.offsets.__get__ (line 3189)LargeListViewArray.from_arrays (line 3029)LargeListArray.values.__get__ (line 2713)Iterable should contain Array objects, got {0} insteadIpcWriteOptions.__setstate_cython__IpcReadOptions.__setstate_cython__Invalid value for 'maps_as_pydicts': valid values are 'lossy', 'strict' or `None` (default). Index must either be string or integerIncompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ())Implemented only for data on CPU deviceIPC read statistics Parameters ---------- num_messages : int Number of messages. num_record_batches : int Number of record batches. num_dictionary_batches : int Number of dictionary batches. num_dictionary_deltas : int Delta of dictionaries. num_replaced_dictionaries : int Number of replaced dictionaries. IO thread count must be strictly positiveFixedSizeListType.value_type.__get__ (line 827)FixedSizeListType.list_size.__get__ (line 840)FixedSizeListArray.from_arrays (line 3394)FixedSizeBufferWriter.set_memcopy_threadsFixedSizeBufferWriter.__setstate_cython__FixedSizeBufferWriter.__reduce_cython__FixedShapeTensorType.__arrow_ext_class__FixedShapeTensorScalar.to_tensorFixedShapeTensorArray.to_numpy_ndarrayFixedShapeTensorArray.from_numpy_ndarrayFirst stride needs to be largest to ensure that individual tensor data is contiguous in memory.Field.nullable.__get__ (line 2465)Field.metadata.__get__ (line 2496)ExtensionType.__arrow_ext_serialize__ExtensionType.__arrow_ext_scalar_class__ExtensionType.__arrow_ext_deserialize__Expected scipy.sparse.csr_matrix, got {}Expected scipy.sparse.csc_matrix, got {}Expected scipy.sparse.coo_matrix, got {}Expected pandas DataFrame, python dictionary or list of arraysExpected pandas DataFrame or list of arraysExpected integer or string indexExpected file path, but {0} is a directoryExpected a list of 1-dimensional arrays for SparseCSFTensor.indptrExpected 2-dimensional array for SparseCOOTensor indicesExpected 1-dimensional array for SparseCSRMatrix indptrDurationType.unit.__get__ (line 1367)Duplicate key {}, use pass all items as list of tuples if you intend to have duplicate keysDo not call {}'s constructor directly, use pyarrow.*_memory_pool instead.Do not call Tensor's constructor directly, use one of the `pyarrow.Tensor.from_*` functions instead.Do not call SparseCSRMatrix's constructor directly, use one of the `pyarrow.SparseCSRMatrix.from_*` functions instead.Do not call SparseCSFTensor's constructor directly, use one of the `pyarrow.SparseCSFTensor.from_*` functions instead.Do not call SparseCSCMatrix's constructor directly, use one of the `pyarrow.SparseCSCMatrix.from_*` functions instead.Do not call SparseCOOTensor's constructor directly, use one of the `pyarrow.SparseCOOTensor.from_*` functions instead.Do not call Schema's constructor directly, use `pyarrow.schema` instead.Do not call MemoryManager's constructor directly, use pyarrow.default_cpu_memory_manager() instead.Do not call Device's constructor directly, use the device attribute of the MemoryManager instead.Do not call ChunkedArray's constructor directly, use `chunked_array` function instead.Do not call Buffer's constructor directly, use `pyarrow.py_buffer` function instead.DictionaryType.ordered.__get__ (line 482)DictionaryMemo.__setstate_cython__DictionaryArray.dictionary_encodeDictionaryArray.dictionary_decodeDevice on which the data resides differs between buffers: Decimal256Type.scale.__get__ (line 1487)Decimal128Type.scale.__get__ (line 1438)DataType.to_pandas_dtype (line 377)DataType.num_fields.__get__ (line 291)DataType.num_buffers.__get__ (line 313)DataType.byte_width.__get__ (line 269) Convert pandas.DataFrame to an Arrow Table. The column types in the resulting Arrow Table are inferred from the dtypes of the pandas.Series in the DataFrame. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. In the case of `object`, we need to guess the datatype by looking at the Python objects in this Series. Be aware that Series of the `object` dtype don't carry enough information to always lead to a meaningful Arrow type. In the case that we cannot infer a type, e.g. because the DataFrame is of length 0 or the Series only contains None/nan objects, the type is set to null. This behavior can be avoided by constructing an explicit schema and passing it to this function. Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the Arrow Table. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``Table``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. safe : bool, default True Check for overflows or other unsafe conversions. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.Table.from_pandas(df) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Compression type must be lz4, zstd or NoneCompressedOutputStream.__setstate_cython__CompressedInputStream.__setstate_cython__CompressedInputStream.__reduce_cython__Codec.supports_compression_levelChunkedArray.value_counts (line 816)ChunkedArray.type.__get__ (line 84)ChunkedArray.to_pylist (line 1352)ChunkedArray.nbytes.__get__ (line 229)ChunkedArray.iterchunks (line 1334)ChunkedArray.get_total_buffer_sizeChunkedArray.format is deprecated, use ChunkedArray.to_stringChunkedArray.drop_null (line 1081)ChunkedArray.dictionary_encode (line 595)ChunkedArray.combine_chunks (line 730)ChunkedArray.chunks.__get__ (line 1296)ChunkedArray._import_from_c_capsuleCasting to a requested schema is only supported for CPU dataCasting field {!r} with null values to non-nullableCannot specify both list_size and typeCannot return a writable array if asking for zero-copyCannot return a numpy.ndarray if NumPy is not presentCannot pass both schema and namesCannot pass both schema and metadataCannot pass a numpy masked array and specify a mask at the same timeCannot create multiple NullScalar instancesCannot convert 1D array or scalar to fixed shape tensor arrayCan't convert PyCapsule with name 'Can only get value offsets for dense arraysCacheOptions.from_network_metricsCPU count must be strictly positiveBufferedOutputStream.__reduce_cython__BufferedInputStream.__setstate_cython__BufferedInputStream.__reduce_cython__BufferOutputStream.__setstate_cython__BufferOutputStream.__reduce_cython__Bool8Type.__arrow_ext_scalar_class__BaseListArray.value_parent_indicesBaseListArray.value_lengths (line 2455)BaseExtensionType.__arrow_ext_scalar_class__BaseExtensionType.__arrow_ext_class__Arrays were not all the same length: {0} vs {1}Array.format is deprecated, use Array.to_stringArray._import_from_c_device_capsuleArgument 'destination' has incorrect type (expected a pyarrow Device or MemoryManager, got Add metadata as dict of string keys and values to Schema Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Add metadata to existing schema field: >>> schema.with_metadata({"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' A null type field may not be non-nullabletype_codes should have the same length as fieldsself.writer cannot be converted to a Python object for picklingself.reader cannot be converted to a Python object for picklingself.device cannot be converted to a Python object for pickling_python_extension_types_registry type: {0.type} shape: {0.shape} strides: {0.strides}pyarrow.Message type: {0} metadata length: {1} body length: {2}pickle-based deserialization of pyarrow.PyExtensionType subclasses is disabled by default; if you only ingest trusted data files, you may re-enable this using `pyarrow.PyExtensionType.set_auto_load(True)`. In the future, Python-defined extension subclasses should derive from pyarrow.ExtensionType (not pyarrow.PyExtensionType) and implement their own serialization mechanism. not supported for buffer protocol'max_chunksize' should be strictly positivemask not implemented with Arrow array inputs yet incompatible with bool8 storage_download_nothreads..cleanup_break_traceback_cycle_from_frame always results in a copy). If using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when needed Write Schema to Buffer as encapsulated IPC message Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Write schema to Buffer: >>> schema.serialize() Whether the dictionary is ordered, i.e. whether the ordering of values in the dictionary is important. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()).ordered False Unregister a Python extension type. Parameters ---------- type_name : str The name of the ExtensionType subclass to unregister. Examples -------- Define a RationalType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class RationalType(pa.ExtensionType): ... def __init__(self, data_type: pa.DataType): ... if not pa.types.is_integer(data_type): ... raise TypeError(f"data_type must be an integer type not {data_type}") ... super().__init__( ... pa.struct( ... [ ... ("numer", data_type), ... ("denom", data_type), ... ], ... ), ... # N.B. This name does _not_ reference `data_type` so deserialization ... # will work for _any_ integer `data_type` after registration ... "my_package.rational", ... ) ... def __arrow_ext_serialize__(self) -> bytes: ... # No parameters are necessary ... return b"" ... @classmethod ... def __arrow_ext_deserialize__(cls, storage_type, serialized): ... # return an instance of this subclass ... return RationalType(storage_type[0].type) Register the extension type: >>> pa.register_extension_type(RationalType(pa.int64())) Unregister the extension type: >>> pa.unregister_extension_type("my_package.rational") Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray according to 'recursive'. Note that this method is different from ``self.values`` in that it takes care of the slicing offset as well as null elements backed by non-empty sub-lists. Parameters ---------- recursive : bool, default False, optional When True, flatten this logical list-array recursively until an array of non-list values is formed. When False, flatten only the top level. Returns ------- result : Array Examples -------- Basic logical list-array's flatten >>> import pyarrow as pa >>> values = [1, 2, 3, 4] >>> offsets = [2, 1, 0] >>> sizes = [2, 2, 2] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 3, 4 ], [ 2, 3 ], [ 1, 2 ] ] >>> array.flatten() [ 3, 4, 2, 3, 1, 2 ] When recursive=True, nested list arrays are flattened recursively until an array of non-list values is formed. >>> array = pa.array([ ... None, ... [ ... [1, None, 2], ... None, ... [3, 4] ... ], ... [], ... [ ... [], ... [5, 6], ... None ... ], ... [ ... [7, 8] ... ] ... ], type=pa.list_(pa.list_(pa.int64()))) >>> array.flatten(True) [ 1, null, 2, 3, 4, 5, 6, 7, 8 ] Unify dictionaries across all chunks. This method returns an equivalent chunked array, but where all chunks share the same dictionary values. Dictionary indices are transposed accordingly. If there are no dictionaries in the chunked array, it is returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : ChunkedArray Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> c_arr [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ] ] >>> c_arr.unify_dictionaries() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] Trying to import data on a CUDA device, but PyArrow is not built with CUDA support. (importing 'pyarrow.cuda' resulted in "TransformInputStream.__setstate_cython__TimestampType.unit.__get__ (line 1245) The timestamp unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('us') >>> t.unit 'us' The time unit ('us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.time64('us') >>> t.unit 'us' The sum of bytes in each buffer referenced by the chunked array. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.get_total_buffer_size() 49 The size of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.size 6 The size of the fixed size lists. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).list_size 2 The shape of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.shape (2, 3) The schema's metadata. Returns ------- metadata: dict Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) Get the metadata of the schema's fields: >>> schema.metadata {b'n_legs': b'Number of legs per animal'} The schema's field types. Returns ------- list of DataType Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the types of the schema's fields: >>> schema.types [DataType(int64), DataType(string)] The schema's field names. Returns ------- list of str Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the names of the schema's fields: >>> schema.names ['n_legs', 'animals'] The number of child fields. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().num_fields 0 >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.string()).num_fields 1 >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct.num_fields 2 The 'names' argument is not valid when passing a dictionary The field metadata. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_field pyarrow.Field The field for large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_field pyarrow.Field The field for keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_field pyarrow.Field The duration unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.duration('s') >>> t.unit 's' The dictionary value type. The dictionary values are found in an instance of DictionaryArray. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).value_type DataType(string) The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.scale 2 The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.precision 5 The data type of list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_type DataType(string) The data type of list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_type DataType(string) The data type of large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_type DataType(string) The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.large_list(pa.string()).value_type DataType(string) The data type of keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_type DataType(string) The data type of items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_type DataType(int32) The data type of dictionary indices (a signed integer type). Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).index_type DataType(int16) The column must be allocated on the same device as the RecordBatch. Got column on device Test if this schema is equal to the other Parameters ---------- other : pyarrow.Schema check_metadata : bool, default False Key/value metadata must be equal too Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> schema1 = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema2 = pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ]) Test two equal schemas: >>> schema1.equals(schema1) True Test two unequal schemas: >>> schema1.equals(schema2) False Tensor.strides.__get__ (line 267)Target schema's field names are not matching the record batch's field names: {!r}, {!r}_Tabular.shape.__get__ (line 2071)_Tabular.columns.__get__ (line 1803)_Tabular.column_names.__get__ (line 1781)_Tabular.append_column (line 2413)StructType.get_field_index (line 923)StringViewBuilder.append_valuesStringBuilder.__setstate_cython__ Strides of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.strides (12, 4) SparseCSRMatrix.__reduce_cython__SparseCSFTensor.__reduce_cython__SparseCSCMatrix.__reduce_cython__SparseCOOTensor.__reduce_cython__ Sort the Table or RecordBatch by one or multiple columns. Parameters ---------- sorting : str or list[tuple(name, order)] Name of the column to use to sort (ascending), or a list of multiple sorting conditions where each entry is a tuple with column name and sorting order ("ascending" or "descending") **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- Table or RecordBatch A new tabular object sorted according to the sort keys. Examples -------- Table (works similarly for RecordBatch) >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.sort_by('animal') pyarrow.Table year: int64 n_legs: int64 animal: string ---- year: [[2019,2021,2021,2020,2022,2022]] n_legs: [[5,100,4,2,4,2]] animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]] SignalStopHandler._init_signals Should the entries be sorted according to keys. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True).keys_sorted True Should specify one of list_size and type Select single column from Table or RecordBatch. Parameters ---------- i : int or string The index or name of the column to retrieve. Returns ------- column : Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Select a column by numeric index: >>> table.column(0) [ [ 2, 4, 5, 100 ] ] Select a column by its name: >>> table.column("animals") [ [ "Flamingo", "Horse", "Brittle stars", "Centipede" ] ] Select rows from the table or record batch based on a boolean mask. The Table can be filtered based on a mask, which will be passed to :func:`pyarrow.compute.filter` to perform the filtering, or it can be filtered through a boolean :class:`.Expression` Parameters ---------- mask : Array or array-like or .Expression The boolean mask or the :class:`.Expression` to filter the table with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled, does nothing if an :class:`.Expression` is used. Returns ------- filtered : Table or RecordBatch A tabular object of the same schema, with only the rows selected by applied filtering Examples -------- Using a Table (works similarly for RecordBatch): >>> import pyarrow as pa >>> table = pa.table({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) Define an expression and select rows: >>> import pyarrow.compute as pc >>> expr = pc.field("year") <= 2020 >>> table.filter(expr) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2019]] n_legs: [[2,5]] animals: [["Flamingo","Brittle stars"]] Define a mask and select rows: >>> mask=[True, True, False, None] >>> table.filter(mask) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022]] n_legs: [[2,4]] animals: [["Flamingo","Horse"]] >>> table.filter(mask, null_selection_behavior='emit_null') pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,null]] n_legs: [[2,4,null]] animals: [["Flamingo","Horse",null]] Select rows from a Table or RecordBatch. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the tabular object whose rows will be returned. Returns ------- Table or RecordBatch A tabular object with the same schema, containing the taken rows. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.take([1,3]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] Select columns of the Table. Returns a new Table with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.select([0,1]) pyarrow.Table year: int64 n_legs: int64 ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] >>> table.select(["year"]) pyarrow.Table year: int64 ---- year: [[2020,2022,2019,2021]] Select columns of the RecordBatch. Returns a new RecordBatch with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) Select columns my indices: >>> batch.select([1]) pyarrow.RecordBatch animals: string ---- animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Select columns by names: >>> batch.select(["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,2,4,4,5,100] Select a schema field by its column name or numeric index. Parameters ---------- i : int or string The index or name of the field to retrieve. Returns ------- Field Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.field(0) pyarrow.Field >>> table.field(1) pyarrow.Field Select a field by its column name or numeric index. Parameters ---------- i : int or str Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Select the second field: >>> struct_type.field(1) pyarrow.Field Select the field named 'x': >>> struct_type.field('x') pyarrow.Field Select a chunk by its index. Parameters ---------- i : int Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.chunk(1) [ 4, 5, 100 ] Schema of the table and its columns. Returns ------- Schema Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' ... Schema of the RecordBatch and its columns Returns ------- pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.schema n_legs: int64 animals: string RunEndEncodedArray.from_buffersRunEndEncodedArray._from_arrays Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_name(0) 'dim1' >>> tensor.dim_name(1) 'dim2' Returns implied schema from dataframe Parameters ---------- df : pandas.DataFrame preserve_index : bool, default True Whether to store the index as an additional column (or columns, for MultiIndex) in the resulting `Table`. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. Returns ------- pyarrow.Schema Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({ ... 'int': [1, 2], ... 'str': ['a', 'b'] ... }) Create an Arrow Schema from the schema of a pandas dataframe: >>> pa.Schema.from_pandas(df) int: int64 str: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, ... Return true if type is equivalent to passed value. Parameters ---------- other : DataType or string convertible to DataType check_metadata : bool Whether nested Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> pa.int64().equals(pa.string()) False >>> pa.int64().equals(pa.int64()) True Return true if the tensors contains exactly equal data. Parameters ---------- other : Tensor The other tensor to compare for equality. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> y = np.array([[2, 2, 4], [4, 5, 10]], np.int32) >>> tensor2 = pa.Tensor.from_numpy(y, dim_names=["a","b"]) >>> tensor.equals(tensor) True >>> tensor.equals(tensor2) False Return the process-global memory pool. Examples -------- >>> default_memory_pool() Return the list view sizes as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Return the list view offsets as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return the list sizes as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, 5]]) >>> array.offsets [ 0, 2, 2, 5 ] Return the equivalent NumPy / Pandas dtype. Examples -------- >>> import pyarrow as pa >>> pa.int64().to_pandas_dtype() Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct_type.get_all_field_indices('x') [0] Return length of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.length() 6 Return integers array with values equal to the respective length of each list element. Null list values are null in the output. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_lengths() [ 3, 0, null, 1 ] Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Index of the field with a name 'y': >>> struct_type.get_field_index('y') 1 Index of the field that does not exist: >>> struct_type.get_field_index('z') -1 Return deserialized-from-JSON pandas metadata field (if it exists) Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> schema = pa.Table.from_pandas(df).schema Select pandas metadata field from Arrow Schema: >>> schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, 'stop': 4, 'step': 1}], ... Return data type of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Return boolean array indicating the null values. Parameters ---------- nan_is_null : bool (optional, default False) Whether floating-point NaN values should also be considered null. Returns ------- array : boolean Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_null() [ [ false, false, false, false, true, false ] ] Return a child field by its numeric index. Parameters ---------- i : int Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union[0] pyarrow.Field Return a NumPy copy of this array (experimental). Parameters ---------- zero_copy_only : bool, default False Introduced for signature consistence with pyarrow.Array.to_numpy. This must be False here since NumPy arrays' buffer must be contiguous. Returns ------- array : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_numpy() array([ 2, 2, 4, 4, 5, 100]) Replace each null element in values with fill_value. See :func:`pyarrow.compute.fill_null` for full usage. Parameters ---------- fill_value : any The replacement value for null entries. Returns ------- result : Array or ChunkedArray A new array with nulls replaced by the given value. Examples -------- >>> import pyarrow as pa >>> fill_value = pa.scalar(5, type=pa.int8()) >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.fill_null(fill_value) [ [ 2, 2, 4, 4, 5, 100 ] ] Replace column in Table at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> table.set_column(1,'year', [year]) pyarrow.Table n_legs: int64 year: int64 ---- n_legs: [[2,4,5,100]] year: [[2021,2022,2019,2021]] Replace column in RecordBatch at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> batch.set_column(1,'year', year) pyarrow.RecordBatch n_legs: int64 year: int64 ---- n_legs: [2,4,5,100] year: [2021,2022,2019,2021] Replace a field at position i in the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Replace the second field of the schema with a new field 'extra': >>> schema.set(1, pa.field('replaced', pa.bool_())) n_legs: int64 replaced: bool Remove the field at index i from the schema. Parameters ---------- i : int Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Remove the second field of the schema: >>> schema.remove(1) n_legs: int64 Remove rows that contain missing values from a Table or RecordBatch. See :func:`pyarrow.compute.drop_null` for full usage. Returns ------- Table or RecordBatch A tabular object with the same schema, with rows containing no missing values. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [None, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.drop_null() pyarrow.Table year: double n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] Remove missing values from a chunked array. See :func:`pyarrow.compute.drop_null` for full description. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.drop_null() [ [ 2, 2 ], [ 4, 5, 100 ] ] Register a Python extension type. Registration is based on the extension name (so different registered types need unique extension names). Registration needs an extension type instance, but then works for any instance of the same subclass regardless of parametrization of the type. Parameters ---------- ext_type : BaseExtensionType instance The ExtensionType subclass to register. Examples -------- Define a RationalType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class RationalType(pa.ExtensionType): ... def __init__(self, data_type: pa.DataType): ... if not pa.types.is_integer(data_type): ... raise TypeError(f"data_type must be an integer type not {data_type}") ... super().__init__( ... pa.struct( ... [ ... ("numer", data_type), ... ("denom", data_type), ... ], ... ), ... # N.B. This name does _not_ reference `data_type` so deserialization ... # will work for _any_ integer `data_type` after registration ... "my_package.rational", ... ) ... def __arrow_ext_serialize__(self) -> bytes: ... # No parameters are necessary ... return b"" ... @classmethod ... def __arrow_ext_deserialize__(cls, storage_type, serialized): ... # return an instance of this subclass ... return RationalType(storage_type[0].type) Register the extension type: >>> pa.register_extension_type(RationalType(pa.int64())) Unregister the extension type: >>> pa.unregister_extension_type("my_package.rational") RecordBatch.to_tensor (line 3570)RecordBatch.serialize (line 3049)RecordBatch.schema.__get__ (line 2684)RecordBatch.rename_columns (line 2974)RecordBatch.nbytes.__get__ (line 2727)RecordBatch._export_to_c_device_RecordBatchStreamWriter.__setstate_cython___RecordBatchStreamWriter.__reduce_cython___RecordBatchStreamReader.__setstate_cython___RecordBatchStreamReader.__reduce_cython___RecordBatchFileWriter.__setstate_cython___RecordBatchFileReader.get_batch_RecordBatchFileReader.__setstate_cython__ProxyMemoryPool.__reduce_cython__ Provide an empty table according to the schema. Returns ------- table: pyarrow.Table Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Create an empty table with schema's fields: >>> schema.empty_table() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[]] animals: [[]] Perform an asof join between this table and another one. This is similar to a left-join except that we match on nearest key rather than equal keys. Both tables must be sorted by the key. This type of join is most useful for time series data that are not perfectly aligned. Optionally match on equivalent keys with "by" before searching with "on". Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. on : str The column from current table that should be used as the "on" key of the join operation left side. An inexact match is used on the "on" key, i.e. a row is considered a match if and only if left_on - tolerance <= right_on <= left_on. The input dataset must be sorted by the "on" key. Must be a single field of a common type. Currently, the "on" key must be an integer, date, or timestamp type. by : str or list[str] The columns from current table that should be used as the keys of the join operation left side. The join operation is then done only for the matches in these columns. tolerance : int The tolerance for inexact "on" key matching. A right row is considered a match with the left row ``right.on - left.on <= tolerance``. The ``tolerance`` may be: - negative, in which case a past-as-of-join occurs; - or positive, in which case a future-as-of-join occurs; - or zero, in which case an exact-as-of-join occurs. The tolerance is interpreted in the same units as the "on" key. right_on : str or list[str], default None The columns from the right_table that should be used as the on key on the join operation right side. When ``None`` use the same key name as the left table. right_by : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. Returns ------- Table Example -------- >>> import pyarrow as pa >>> t1 = pa.table({'id': [1, 3, 2, 3, 3], ... 'year': [2020, 2021, 2022, 2022, 2023]}) >>> t2 = pa.table({'id': [3, 4], ... 'year': [2020, 2021], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1.join_asof(t2, on='year', by='id', tolerance=-2) pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[1,3,2,3,3]] year: [[2020,2021,2022,2022,2023]] n_legs: [[null,5,null,5,null]] animal: [[null,"Brittle stars",null,"Brittle stars",null]] Perform an aggregation over the grouped columns of the table. Parameters ---------- aggregations : list[tuple(str, str)] or list[tuple(str, str, FunctionOptions)] List of tuples, where each tuple is one aggregation specification and consists of: aggregation column name followed by function name and optionally aggregation function option. Pass empty list to get a single row for each group. The column name can be a string, an empty list or a list of column names, for unary, nullary and n-ary aggregation functions respectively. For the list of function names and respective aggregation function options see :ref:`py-grouped-aggrs`. Returns ------- Table Results of the aggregation functions. Examples -------- >>> import pyarrow as pa >>> t = pa.table([ ... pa.array(["a", "a", "b", "b", "c"]), ... pa.array([1, 2, 3, 4, 5]), ... ], names=["keys", "values"]) Sum the column "values" over the grouped column "keys": >>> t.group_by("keys").aggregate([("values", "sum")]) pyarrow.Table keys: string values_sum: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] Count the rows over the grouped column "keys": >>> t.group_by("keys").aggregate([([], "count_all")]) pyarrow.Table keys: string count_all: int64 ---- keys: [["a","b","c"]] count_all: [[2,2,1]] Do multiple aggregations: >>> t.group_by("keys").aggregate([ ... ("values", "sum"), ... ("keys", "count") ... ]) pyarrow.Table keys: string values_sum: int64 keys_count: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] keys_count: [[2,2,1]] Count the number of non-null values for column "values" over the grouped column "keys": >>> import pyarrow.compute as pc >>> t.group_by(["keys"]).aggregate([ ... ("values", "count", pc.CountOptions(mode="only_valid")) ... ]) pyarrow.Table keys: string values_count: int64 ---- keys: [["a","b","c"]] values_count: [[2,2,1]] Get a single row for each group in column "keys": >>> t.group_by("keys").aggregate([]) pyarrow.Table keys: string ---- keys: [["a","b","c"]] Perform a join between this table and another one. Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. keys : str or list[str] The columns from current table that should be used as keys of the join operation left side. right_keys : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. join_type : str, default "left outer" The kind of join that should be performed, one of ("left semi", "right semi", "left anti", "right anti", "inner", "left outer", "right outer", "full outer") left_suffix : str, default None Which suffix to add to left column names. This prevents confusion when the columns in left and right tables have colliding names. right_suffix : str, default None Which suffix to add to the right column names. This prevents confusion when the columns in left and right tables have colliding names. coalesce_keys : bool, default True If the duplicated keys should be omitted from one of the sides in the join result. use_threads : bool, default True Whether to use multithreading or not. Returns ------- Table Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df1 = pd.DataFrame({'id': [1, 2, 3], ... 'year': [2020, 2022, 2019]}) >>> df2 = pd.DataFrame({'id': [3, 4], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1 = pa.Table.from_pandas(df1) >>> t2 = pa.Table.from_pandas(df2) Left outer join: >>> t1.join(t2, 'id').combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2]] year: [[2019,2020,2022]] n_legs: [[5,null,null]] animal: [["Brittle stars",null,null]] Full outer join: >>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2,4]] year: [[2019,2020,2022,null]] n_legs: [[5,null,null,100]] animal: [["Brittle stars",null,null,"Centipede"]] Right outer join: >>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year') pyarrow.Table year: int64 id: int64 n_legs: int64 animal: string ---- year: [[2019,null]] id: [[3,4]] n_legs: [[5,100]] animal: [["Brittle stars","Centipede"]] Right anti join >>> t1.join(t2, 'id', join_type="right anti") pyarrow.Table id: int64 n_legs: int64 animal: string ---- id: [[4]] n_legs: [[100]] animal: [["Centipede"]] _PandasConvertible.to_pandas (line 707)_PandasConvertible.__setstate_cython___PandasConvertible.__reduce_cython___PandasAPIShim.is_extension_array_dtype_PandasAPIShim.get_rangeindex_attribute_PandasAPIShim.__setstate_cython__ Open memory map at file path. Size of the memory map cannot change. Parameters ---------- path : str mode : {'r', 'r+', 'w'}, default 'r' Whether the file is opened for reading ('r'), writing ('w') or both ('r+'). Returns ------- mmap : MemoryMappedFile Examples -------- Reading from a memory map without any memory allocation or copying: >>> import pyarrow as pa >>> with pa.output_stream('example_mmap.txt') as stream: ... stream.write(b'Constructing a buffer referencing the mapped memory') ... 51 >>> with pa.memory_map('example_mmap.txt') as mmap: ... mmap.read_at(6,45) ... b'memory' Number of underlying chunks. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.num_chunks 2 Number of rows in this table. Due to the definition of a table, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_rows 4 Number of rows Due to the definition of a RecordBatch, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_rows 6 Number of data buffers required to construct Array type excluding children. Examples -------- >>> import pyarrow as pa >>> pa.int64().num_buffers 2 >>> pa.string().num_buffers 3 Number of columns in this table. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_columns 2 Number of columns Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_columns 2 Names of this tensor dimensions. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_names ['dim1', 'dim2'] Names of the Table or RecordBatch columns. Returns ------- list of str Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> table = pa.Table.from_arrays([[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]], ... names=['n_legs', 'animals']) >>> table.column_names ['n_legs', 'animals'] Must pass either fields or type, not bothMessageReader.read_next_messageMessageReader.__setstate_cython__MemoryManager.__setstate_cython__Mask must be a pyarrow.Array of type booleanMask must be a numpy array when converting numpy arraysMask is a different length from sequence being convertedMapType.item_field.__get__ (line 754) Make a new table by combining the chunks this table has. All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> names = ["n_legs", "animals"] >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] >>> table.combine_chunks() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4,4,5,100]] animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]] Lists all fields within the StructType. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())]) >>> struct_type.fields [pyarrow.Field, pyarrow.Field, pyarrow.Field] List requires DataType or Field List of all columns in numerical order. Returns ------- columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.columns [ [ [ null, 4, 5, null ] ], [ [ "Flamingo", "Horse", null, "Centipede" ] ]] ListArray.from_arrays (line 2482)LargeListView requires DataType or FieldLargeListViewArray.values.__get__ (line 3130) Iterator over all columns in their numerical order. Yields ------ Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> for i in table.itercolumns(): ... print(i.null_count) ... 2 1 Is this tensor mutable or immutable. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_mutable True Is this tensor contiguous in memory. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_contiguous True IpcWriteOptions.__reduce_cython__Invalid value for compression: {!r}Incompatible storage type {0} for extension type {1}IPC write statistics Parameters ---------- num_messages : int Number of messages. num_record_batches : int Number of record batches. num_dictionary_batches : int Number of dictionary batches. num_dictionary_deltas : int Delta of dictionaries. num_replaced_dictionaries : int Number of replaced dictionaries. Flatten this ChunkedArray. If it has a struct type, the column is flattened into one array per struct field. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : list of ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> c_arr = pa.chunked_array(n_legs.value_counts()) >>> c_arr [ -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] ] >>> c_arr.flatten() [ [ [ 2, 4, 5, 100 ] ], [ [ 2, 2, 1, 1 ] ]] >>> c_arr.type StructType(struct) >>> n_legs.type DataType(int64) FixedSizeListArray.values.__get__ (line 3479)FixedShapeTensorScalar.to_numpyFixedShapeTensorArray.to_tensor Find the first index of a value. See :func:`pyarrow.compute.index` for full usage. Parameters ---------- value : Scalar or object The value to look for in the array. start : int, optional The start index where to look for `value`. end : int, optional The end index where to look for `value`. memory_pool : MemoryPool, optional A memory pool for potential memory allocations. Returns ------- index : Int64Scalar The index of the value in the array (-1 if not found). Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.index(4) >>> n_legs.index(4, start=3) File object is malformed, has no modeField.remove_metadata (line 2549)ExtensionType.__arrow_ext_class___ExtensionRegistryNanny.__setstate_cython___ExtensionRegistryNanny.__reduce_cython__Expected storage type {0} but got {1}Expected list of {ndim} np.arrays for SparseCSFTensor.indptrExpected array or chunked array, got Expected an object implementing the Arrow PyCapsule Protocol for streams (i.e. having a `__arrow_c_stream__` method), got Drop one or more columns and return a new Table or RecordBatch. Parameters ---------- columns : str or list[str] Field name(s) referencing existing column(s). Raises ------ KeyError If any of the passed column names do not exist. Returns ------- Table or RecordBatch A tabular object without the column(s). Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Drop one column: >>> table.drop_columns("animals") pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Drop one or more columns: >>> table.drop_columns(["n_legs", "animals"]) pyarrow.Table ... ---- Do not call Field's constructor directly, use `pyarrow.field` instead. Dimensions of the table or record batch: (#rows, #columns). Returns ------- (int, int) Number of rows and number of columns. Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table.shape (4, 2) Dictionary (categorical, or simply encoded) type. Parameters ---------- index_type : DataType value_type : DataType ordered : bool Returns ------- type : DictionaryType Examples -------- Create an instance of dictionary type: >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()) DictionaryType(dictionary) Use dictionary type to create an array: >>> pa.array(["a", "b", None, "d"], pa.dictionary(pa.int64(), pa.utf8())) ... -- dictionary: [ "a", "b", "d" ] -- indices: [ 0, 1, null, 2 ] DictionaryType.value_type.__get__ (line 509)DictionaryType.index_type.__get__ (line 496) Declare a grouping over the columns of the table. Resulting grouping can then be used to perform aggregations with a subsequent ``aggregate()`` method. Parameters ---------- keys : str or list[str] Name of the columns that should be used as the grouping key. use_threads : bool, default True Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed. Returns ------- TableGroupBy See Also -------- TableGroupBy.aggregate Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.group_by('year').aggregate([('n_legs', 'sum')]) pyarrow.Table year: int64 n_legs_sum: int64 ---- year: [[2020,2022,2021,2019]] n_legs_sum: [[2,6,104,5]] DataType.bit_width.__get__ (line 250)DataType._import_from_c_capsule Create variable-length or fixed size binary type. Parameters ---------- length : int, optional, default -1 If length == -1 then return a variable length binary type. If length is greater than or equal to 0 then return a fixed size binary type of width `length`. Examples -------- Create an instance of a variable-length binary type: >>> import pyarrow as pa >>> pa.binary() DataType(binary) and use the variable-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary()) [ 666F6F, 626172, 62617A ] Create an instance of a fixed-size binary type: >>> pa.binary(3) FixedSizeBinaryType(fixed_size_binary[3]) and use the fixed-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary(3)) [ 666F6F, 626172, 62617A ] Create single-precision floating point type. Examples -------- Create an instance of float32 type: >>> import pyarrow as pa >>> pa.float32() DataType(float) >>> print(pa.float32()) float Create an array with float32 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float32()) [ 0, 1, 2 ] Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None), which deletes any existing metadata. Parameters ---------- metadata : dict, default None Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Constructing a Table with pyarrow schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> table= pa.table(df, my_schema) >>> table.schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... Create a shallow copy of a Table with deleted schema metadata: >>> table.replace_schema_metadata().schema n_legs: int64 animals: string Create a shallow copy of a Table with new schema metadata: >>> metadata={"animals": "Which animal"} >>> table.replace_schema_metadata(metadata = metadata).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Which animal' Create shallow copy of record batch by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata Parameters ---------- metadata : dict, default None Returns ------- shallow_copy : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) Constructing a RecordBatch with schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64())], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema) >>> batch.schema n_legs: int64 -- schema metadata -- n_legs: 'Number of legs per animal' Shallow copy of a RecordBatch with deleted schema metadata: >>> batch.replace_schema_metadata().schema n_legs: int64 Create pyarrow.Array instance from a Python object. Parameters ---------- obj : sequence, iterable, ndarray, pandas.Series, Arrow-compatible array If both type and size are specified may be a single use iterable. If not strongly-typed, Arrow type will be inferred for resulting array. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_device_array__`` method) can be passed as well. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the data. mask : array[bool], optional Indicate which values are null (True) or not null (False). size : int64, optional Size of the elements. If the input is larger than size bail at this length. For iterators, if size is larger than the input iterator this will be treated as a "max size", but will involve an initial allocation of size followed by a resize to the actual size (so if you know the exact size specifying it correctly will give you better performance). from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. If passed, the mask tasks precedence, but if a value is unmasked (not-null), but still null according to pandas semantics, then it is null. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. safe : bool, default True Check for overflows or other unsafe conversions. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- array : pyarrow.Array or pyarrow.ChunkedArray A ChunkedArray instead of an Array is returned if: - the object data overflowed binary storage. - the object's ``__arrow_array__`` protocol method returned a chunked array. Notes ----- Timezone will be preserved in the returned array for timezone-aware data, else no timezone will be returned for naive timestamps. Internally, UTC values are stored for timezone-aware data with the timezone set in the data type. Pandas's DateOffsets and dateutil.relativedelta.relativedelta are by default converted as MonthDayNanoIntervalArray. relativedelta leapdays are ignored as are all absolute fields on both objects. datetime.timedelta can also be converted to MonthDayNanoIntervalArray but this requires passing MonthDayNanoIntervalType explicitly. Converting to dictionary array will promote to a wider integer type for indices if the number of distinct values cannot be represented, even if the index type was explicitly set. This means that if there are more than 127 values the returned dictionary array's index type will be at least pa.int16() even if pa.int8() was passed to the function. Note that an explicit index type will not be demoted even if it is wider than required. Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> pa.array(pd.Series([1, 2])) [ 1, 2 ] >>> pa.array(["a", "b", "a"], type=pa.dictionary(pa.int8(), pa.string())) ... -- dictionary: [ "a", "b" ] -- indices: [ 0, 1, 0 ] >>> import numpy as np >>> pa.array(pd.Series([1, 2]), mask=np.array([0, 1], dtype=bool)) [ 1, null ] >>> arr = pa.array(range(1024), type=pa.dictionary(pa.int8(), pa.int64())) >>> arr.type.index_type DataType(int16) Create new table with columns renamed to provided names. Parameters ---------- names : list[str] or dict[str, str] List of new column names or mapping of old column names to new column names. If a mapping of old to new column names is passed, then all columns which are found to match a provided old column name will be renamed to the new column name. If any column names are not found in the mapping, a KeyError will be raised. Raises ------ KeyError If any of the column names passed in the names mapping do not exist. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> new_names = ["n", "name"] >>> table.rename_columns(new_names) pyarrow.Table n: int64 name: string ---- n: [[2,4,5,100]] name: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> new_names = {"n_legs": "n", "animals": "name"} >>> table.rename_columns(new_names) pyarrow.Table n: int64 name: string ---- n: [[2,4,5,100]] name: [["Flamingo","Horse","Brittle stars","Centipede"]] Create new schema without metadata, if any Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Create a new schema with removing the metadata from the original: >>> schema.remove_metadata() n_legs: int64 animals: string Create new record batch with columns renamed to provided names. Parameters ---------- names : list[str] or dict[str, str] List of new column names or mapping of old column names to new column names. If a mapping of old to new column names is passed, then all columns which are found to match a provided old column name will be renamed to the new column name. If any column names are not found in the mapping, a KeyError will be raised. Raises ------ KeyError If any of the column names passed in the names mapping do not exist. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> new_names = ["n", "name"] >>> batch.rename_columns(new_names) pyarrow.RecordBatch n: int64 name: string ---- n: [2,4,5,100] name: ["Flamingo","Horse","Brittle stars","Centipede"] >>> new_names = {"n_legs": "n", "animals": "name"} >>> batch.rename_columns(new_names) pyarrow.RecordBatch n: int64 name: string ---- n: [2,4,5,100] name: ["Flamingo","Horse","Brittle stars","Centipede"] Create new field without metadata, if any Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} Create new field by removing the metadata from the existing one: >>> field_new = field.remove_metadata() >>> field_new.metadata Create new Table with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New table without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.remove_column(1) pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Create new RecordBatch with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New record batch without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.remove_column(1) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] Create large variable-length binary type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer binary(). Examples -------- Create an instance of large variable-length binary type: >>> import pyarrow as pa >>> pa.large_binary() DataType(large_binary) and use the type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.large_binary()) [ 666F6F, 626172, 62617A ] Create large UTF8 variable-length string type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_string() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_string()) [ "foo", "bar", ... "foo", "bar" ] Create instance of unsigned uint16 type. Examples -------- Create an instance of unsigned int16 type: >>> import pyarrow as pa >>> pa.uint16() DataType(uint16) >>> print(pa.uint16()) uint16 Create an array with unsigned int16 type: >>> pa.array([0, 1, 2], type=pa.uint16()) [ 0, 1, 2 ] Create instance of unsigned int8 type. Examples -------- Create an instance of unsigned int8 type: >>> import pyarrow as pa >>> pa.uint8() DataType(uint8) >>> print(pa.uint8()) uint8 Create an array with unsigned int8 type: >>> pa.array([0, 1, 2], type=pa.uint8()) [ 0, 1, 2 ] Create instance of timestamp type with resolution and optional time zone. Parameters ---------- unit : str one of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond] tz : str, default None Time zone name. None indicates time zone naive Examples -------- Create an instance of timestamp type: >>> import pyarrow as pa >>> pa.timestamp('us') TimestampType(timestamp[us]) >>> pa.timestamp('s', tz='America/New_York') TimestampType(timestamp[s, tz=America/New_York]) >>> pa.timestamp('s', tz='+07:30') TimestampType(timestamp[s, tz=+07:30]) Use timestamp type when creating a scalar object: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('s', tz='UTC')) >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('us')) Returns ------- timestamp_type : TimestampType Create instance of signed int64 type. Examples -------- Create an instance of int64 type: >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> print(pa.int64()) int64 Create an array with int64 type: >>> pa.array([0, 1, 2], type=pa.int64()) [ 0, 1, 2 ] Create instance of signed int32 type. Examples -------- Create an instance of int32 type: >>> import pyarrow as pa >>> pa.int32() DataType(int32) >>> print(pa.int32()) int32 Create an array with int32 type: >>> pa.array([0, 1, 2], type=pa.int32()) [ 0, 1, 2 ] Create instance of signed int16 type. Examples -------- Create an instance of int16 type: >>> import pyarrow as pa >>> pa.int16() DataType(int16) >>> print(pa.int16()) int16 Create an array with int16 type: >>> pa.array([0, 1, 2], type=pa.int16()) [ 0, 1, 2 ] Create instance of opaque extension type. Parameters ---------- storage_type : DataType The underlying data type. type_name : str The name of the type in the external system. vendor_name : str The name of the external system. Examples -------- Create an instance of an opaque extension type: >>> import pyarrow as pa >>> type = pa.opaque(pa.binary(), "other", "jdbc") >>> type OpaqueType(extension) Inspect the data type: >>> type.storage_type DataType(binary) >>> type.type_name 'other' >>> type.vendor_name 'jdbc' Create a table with an opaque array: >>> arr = [None, b"foobar"] >>> storage = pa.array(arr, pa.binary()) >>> other = pa.ExtensionArray.from_storage(type, storage) >>> pa.table([other], names=["unknown_col"]) pyarrow.Table unknown_col: extension ---- unknown_col: [[null,666F6F626172]] Returns ------- type : OpaqueType Create instance of null type. Examples -------- Create an instance of a null type: >>> import pyarrow as pa >>> pa.null() DataType(null) >>> print(pa.null()) null Create a ``Field`` type with a null type and a name: >>> pa.field('null_field', pa.null()) pyarrow.Field Create instance of boolean type. Examples -------- Create an instance of a boolean type: >>> import pyarrow as pa >>> pa.bool_() DataType(bool) >>> print(pa.bool_()) bool Create a ``Field`` type with a boolean type and a name: >>> pa.field('bool_field', pa.bool_()) pyarrow.Field Create instance of bool8 extension type. Examples -------- Create an instance of bool8 extension type: >>> import pyarrow as pa >>> type = pa.bool8() >>> type Bool8Type(extension) Inspect the data type: >>> type.storage_type DataType(int8) Create a table with a bool8 array: >>> arr = [-1, 0, 1, 2, None] >>> storage = pa.array(arr, pa.int8()) >>> other = pa.ExtensionArray.from_storage(type, storage) >>> pa.table([other], names=["unknown_col"]) pyarrow.Table unknown_col: extension ---- unknown_col: [[-1,0,1,2,null]] Returns ------- type : Bool8Type Create instance of a duration type with unit resolution. Parameters ---------- unit : str One of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.DurationType Examples -------- Create an instance of duration type: >>> import pyarrow as pa >>> pa.duration('us') DurationType(duration[us]) >>> pa.duration('s') DurationType(duration[s]) Create an array with duration type: >>> pa.array([0, 1, 2], type=pa.duration('s')) [ 0, 1, 2 ] Create double-precision floating point type. Examples -------- Create an instance of float64 type: >>> import pyarrow as pa >>> pa.float64() DataType(double) >>> print(pa.float64()) double Create an array with float64 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float64()) [ 0, 1, 2 ] Create decimal type with precision and scale and 128-bit width. Arrow decimals are fixed-point decimal numbers encoded as a scaled integer. The precision is the number of significant digits that the decimal type can represent; the scale is the number of digits after the decimal point (note the scale can be negative). As an example, ``decimal128(7, 3)`` can exactly represent the numbers 1234.567 and -1234.567 (encoded internally as the 128-bit integers 1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567. ``decimal128(5, -3)`` can exactly represent the number 12345000 (encoded internally as the 128-bit integer 12345), but neither 123450000 nor 1234500. If you need a precision higher than 38 significant digits, consider using ``decimal256``. Parameters ---------- precision : int Must be between 1 and 38 scale : int Returns ------- decimal_type : Decimal128Type Examples -------- Create an instance of decimal type: >>> import pyarrow as pa >>> pa.decimal128(5, 2) Decimal128Type(decimal128(5, 2)) Create an array with decimal type: >>> import decimal >>> a = decimal.Decimal('123.45') >>> pa.array([a], pa.decimal128(5, 2)) [ 123.45 ] Create an Arrow output stream. Parameters ---------- source : str, Path, buffer, file-like object The source to open for writing. compression : str optional, default 'detect' The compression algorithm to use for on-the-fly compression. If "detect" and source is a file path, then compression will be chosen based on the file extension. If None, no compression will be applied. Otherwise, a well-known algorithm name must be supplied (e.g. "gzip"). buffer_size : int, default None If None or 0, no buffering will happen. Otherwise the size of the temporary write buffer. Examples -------- Create a writable NativeFile from a pyarrow Buffer: >>> import pyarrow as pa >>> data = b"buffer data" >>> empty_obj = bytearray(11) >>> buf = pa.py_buffer(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read(6) ... b'buffer' or from a memoryview object: >>> buf = memoryview(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read() ... b'buffer data' Create a writable NativeFile from a string or file path: >>> with pa.output_stream('example_second.txt') as stream: ... stream.write(b'Write some data') ... 15 >>> with pa.input_stream('example_second.txt') as stream: ... stream.read() ... b'Write some data' Create an Arrow input stream. Parameters ---------- source : str, Path, buffer, or file-like object The source to open for reading. compression : str optional, default 'detect' The compression algorithm to use for on-the-fly decompression. If "detect" and source is a file path, then compression will be chosen based on the file extension. If None, no compression will be applied. Otherwise, a well-known algorithm name must be supplied (e.g. "gzip"). buffer_size : int, default None If None or 0, no buffering will happen. Otherwise the size of the temporary read buffer. Examples -------- Create a readable BufferReader (NativeFile) from a Buffer or a memoryview object: >>> import pyarrow as pa >>> buf = memoryview(b"some data") >>> with pa.input_stream(buf) as stream: ... stream.read(4) ... b'some' Create a readable OSFile (NativeFile) from a string or file path: >>> import gzip >>> with gzip.open('example.gz', 'wb') as f: ... f.write(b'some data') ... 9 >>> with pa.input_stream('example.gz') as stream: ... stream.read() ... b'some data' Create a readable PythonFile (NativeFile) from a a Python file object: >>> with open('example.txt', mode='w') as f: ... f.write('some text') ... 9 >>> with pa.input_stream('example.txt') as stream: ... stream.read(6) ... b'some t' Create a variable-length binary view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.binary_view() DataType(binary_view) Create a strongly-typed Array instance with all elements null. Parameters ---------- size : int Array length. type : pyarrow.DataType, default None Explicit type for the array. By default use NullType. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.nulls(10) 10 nulls >>> pa.nulls(3, pa.uint32()) [ null, null, null ] Create a pyarrow.Table from a Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of arrays or chunked arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__``, ``__arrow_c_device_array__`` or ``__arrow_c_stream__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the Arrow Table. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. If passed, the output will have exactly this schema (raising an error when columns are not found in the data and ignoring additional data not specified in the schema, when data is a dict or DataFrame). metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). nthreads : int, default None For pandas.DataFrame inputs: if greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). Returns ------- Table See Also -------- Table.from_arrays, Table.from_pandas, Table.from_pydict Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from a python dictionary: >>> pa.table({"n_legs": n_legs, "animals": animals}) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays: >>> pa.table([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.table([n_legs, animals], names=names, metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.table(df) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from pandas DataFrame with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.table(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: '{"index_columns": [], "column_indexes": [{"name": null, ... Construct a Table from chunked arrays: >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] Create a pyarrow.Scalar instance from a Python object. Parameters ---------- value : Any Python object coercible to arrow's type system. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the value. from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- scalar : pyarrow.Scalar Examples -------- >>> import pyarrow as pa >>> pa.scalar(42) >>> pa.scalar("string") >>> pa.scalar([1, 2]) >>> pa.scalar([1, 2], type=pa.list_(pa.int16())) Create a pyarrow.RecordBatch from another Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of Arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_device_array__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the RecordBatch. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). Returns ------- RecordBatch See Also -------- RecordBatch.from_arrays, RecordBatch.from_pandas, table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from a python dictionary: >>> pa.record_batch({"n_legs": n_legs, "animals": animals}) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch({"n_legs": n_legs, "animals": animals}).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Creating a RecordBatch from a list of arrays with names: >>> pa.record_batch([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Creating a RecordBatch from a list of arrays with names and metadata: >>> my_metadata={"n_legs": "How many legs does an animal have?"} >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'How many legs does an animal have?' Creating a RecordBatch from a pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.record_batch(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> pa.record_batch(df).to_pandas() year month day n_legs animals 0 2020 3 1 2 Flamingo 1 2022 5 5 4 Horse 2 2021 7 9 5 Brittle stars 3 2022 9 13 100 Centipede Creating a RecordBatch from a pandas DataFrame with schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.record_batch(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... >>> pa.record_batch(df, my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Create a pyarrow.Field instance. Parameters ---------- name : str or bytes Name of the field. Alternatively, you can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). type : pyarrow.DataType Arrow datatype of the field. nullable : bool, default True Whether the field's values are nullable. metadata : dict, default None Optional field metadata, the keys and values must be coercible to bytes. Returns ------- field : pyarrow.Field Examples -------- Create an instance of pyarrow.Field: >>> import pyarrow as pa >>> pa.field('key', pa.int32()) pyarrow.Field >>> pa.field('key', pa.int32(), nullable=False) pyarrow.Field >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field pyarrow.Field >>> field.metadata {b'key': b'Something important'} Use the field to create a struct type: >>> pa.struct([field]) StructType(struct) Create a file of the given size and memory-map it. Parameters ---------- path : str The file path to create, on the local filesystem. size : int The file size to create. Returns ------- mmap : MemoryMappedFile Examples -------- Create a file with a memory map: >>> import pyarrow as pa >>> with pa.create_memory_map('example_mmap_create.dat', 27) as mmap: ... mmap.write(b'Create a memory-mapped file') ... mmap.read_at(10, 9) ... 27 b'memory-map' Create a Tensor from a numpy array. Parameters ---------- obj : numpy.ndarray The source numpy array dim_names : list, optional Names of each dimension of the Tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) type: int32 shape: (2, 3) strides: (12, 4) Create UTF8 variable-length string type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.string()) [ "foo", "bar", "baz" ] Create StructType instance from fields. A struct is a nested type parameterized by an ordered sequence of types (which can all be distinct), called its fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Each field must have a UTF8-encoded name, and these field names are part of the type metadata. Examples -------- Create an instance of StructType from an iterable of tuples: >>> import pyarrow as pa >>> fields = [ ... ('f1', pa.int32()), ... ('f2', pa.string()), ... ] >>> struct_type = pa.struct(fields) >>> struct_type StructType(struct) Retrieve a field from a StructType: >>> struct_type[0] pyarrow.Field >>> struct_type['f1'] pyarrow.Field Create an instance of StructType from an iterable of Fields: >>> fields = [ ... pa.field('f1', pa.int32()), ... pa.field('f2', pa.string(), nullable=False), ... ] >>> pa.struct(fields) StructType(struct) Returns ------- type : DataType Create MapType instance from key and item data types or fields. Parameters ---------- key_type : DataType or Field item_type : DataType or Field keys_sorted : bool Returns ------- map_type : DataType Examples -------- Create an instance of MapType: >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()) MapType(map) >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True) MapType(map) Use MapType to create an array: >>> data = [[{'key': 'a', 'value': 1}, {'key': 'b', 'value': 2}], [{'key': 'c', 'value': 3}]] >>> pa.array(data, type=pa.map_(pa.string(), pa.int32(), keys_sorted=True)) [ keys: [ "a", "b" ] values: [ 1, 2 ], keys: [ "c" ] values: [ 3 ] ] Create ListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of ListViewType: >>> import pyarrow as pa >>> pa.list_view(pa.string()) ListViewType(list_view) Create ListType instance from child data type or field. Parameters ---------- value_type : DataType or Field list_size : int, optional, default -1 If length == -1 then return a variable length list type. If length is greater than or equal to 0 then return a fixed size list type. Returns ------- list_type : DataType Examples -------- Create an instance of ListType: >>> import pyarrow as pa >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.int32(), 2) FixedSizeListType(fixed_size_list[2]) Use the ListType to create a scalar: >>> pa.scalar(['foo', None], type=pa.list_(pa.string(), 2)) or an array: >>> pa.array([[1, 2], [3, 4]], pa.list_(pa.int32(), 2)) [ [ 1, 2 ], [ 3, 4 ] ] Create LargeListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of LargeListViewType: >>> import pyarrow as pa >>> pa.large_list_view(pa.int8()) LargeListViewType(large_list_view) Create LargeListType instance from child data type or field. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2**31 elements, you should prefer list_(). Parameters ---------- value_type : DataType or Field Returns ------- list_type : DataType Examples -------- Create an instance of LargeListType: >>> import pyarrow as pa >>> pa.large_list(pa.int8()) LargeListType(large_list) Use the LargeListType to create an array: >>> pa.array([[-1, 3]] * 5, type=pa.large_list(pa.int8())) [ [ -1, 3 ], [ -1, 3 ], ... Converting to Python dictionary is not supported when duplicate field names are present Convert to an iterator of ChunkArrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> for i in n_legs.iterchunks(): ... print(i.null_count) ... 0 1 Convert to a list of single-chunked arrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.chunks [ [ 2, 2, null ], [ 4, 5, 100 ]] Convert to a list of native Python objects. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.to_pylist() [2, 2, 4, 4, None, 100] Convert to a :class:`~pyarrow.Tensor`. RecordBatches that can be converted have fields of type signed or unsigned integer or float, including all bit-widths. ``null_to_nan`` is ``False`` by default and this method will raise an error in case any nulls are present. RecordBatches with nulls can be converted with ``null_to_nan`` set to ``True``. In this case null values are converted to ``NaN`` and integer type arrays are promoted to the appropriate float type. Parameters ---------- null_to_nan : bool, default False Whether to write null values in the result as ``NaN``. row_major : bool, default True Whether resulting Tensor is row-major or column-major memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Examples -------- >>> import pyarrow as pa >>> batch = pa.record_batch( ... [ ... pa.array([1, 2, 3, 4, None], type=pa.int32()), ... pa.array([10, 20, 30, 40, None], type=pa.float32()), ... ], names = ["a", "b"] ... ) >>> batch pyarrow.RecordBatch a: int32 b: float ---- a: [1,2,3,4,null] b: [10,20,30,40,null] Convert a RecordBatch to row-major Tensor with null values written as ``NaN``s >>> batch.to_tensor(null_to_nan=True) type: double shape: (5, 2) strides: (16, 8) >>> batch.to_tensor(null_to_nan=True).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) Convert a RecordBatch to column-major Tensor >>> batch.to_tensor(null_to_nan=True, row_major=False) type: double shape: (5, 2) strides: (8, 40) >>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) Convert the Table to a RecordBatchReader. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- RecordBatchReader Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatchReader: >>> table.to_reader() >>> reader = table.to_reader() >>> reader.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... >>> reader.read_all() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Convert the Table or RecordBatch to a dict or OrderedDict. Returns ------- dict Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"]) >>> table.to_pydict() {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']} Convert arrow::Tensor to numpy.ndarray with zero copy Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.to_numpy() array([[ 2, 2, 4], [ 4, 5, 100]], dtype=int32) Convert Table to a list of RecordBatch objects. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- list[RecordBatch] Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatch: >>> table.to_batches()[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Convert a Table to a list of RecordBatches: >>> table.to_batches(max_chunksize=2)[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse >>> table.to_batches(max_chunksize=2)[1].to_pandas() n_legs animals 0 5 Brittle stars 1 100 Centipede Construct pyarrow.Schema from collection of fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). metadata : dict, default None Keys and values must be coercible to bytes. Examples -------- Create a Schema from iterable of tuples: >>> import pyarrow as pa >>> pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()), ... pa.field('some_required_string', pa.string(), nullable=False) ... ]) some_int: int32 some_string: string some_required_string: string not null Create a Schema from iterable of Fields: >>> pa.schema([ ... pa.field('some_int', pa.int32()), ... pa.field('some_string', pa.string()) ... ]) some_int: int32 some_string: string Returns ------- schema : pyarrow.Schema Construct chunked array from list of array-like objects Parameters ---------- arrays : Array, list of Array, or array-like Must all be the same data type. Can be empty only if type also passed. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method) can be passed as well. type : DataType or string coercible to DataType Returns ------- ChunkedArray Examples -------- >>> import pyarrow as pa >>> pa.chunked_array([], type=pa.int8()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] Construct a Table or RecordBatch from Arrow arrays or columns. Parameters ---------- mapping : dict or Mapping A mapping of strings to Arrays or Python lists. schema : Schema, default None If not passed, will be inferred from the Mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> pydict = {'n_legs': n_legs, 'animals': animals} Construct a Table from a dictionary of arrays: >>> pa.Table.from_pydict(pydict) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_pydict(pydict).schema n_legs: int64 animals: string Construct a Table from a dictionary of arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a dictionary of arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pydict(pydict, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a sequence or iterator of Arrow RecordBatches. Parameters ---------- batches : sequence or iterator of RecordBatch Sequence of RecordBatch to be converted, all schemas must be equal. schema : Schema, default None If not passed, will be inferred from the first RecordBatch. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] >>> batch = pa.record_batch([n_legs, animals], names=names) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Construct a Table from a RecordBatch: >>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a sequence of RecordBatches: >>> pa.Table.from_batches([batch, batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a StructArray. Each field in the StructArray will become a column in the resulting ``Table``. Parameters ---------- struct_array : StructArray or ChunkedArray Array to construct the table from. Returns ------- pyarrow.Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.Table.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 Construct a Table from Arrow arrays. Parameters ---------- arrays : list of pyarrow.Array or pyarrow.ChunkedArray Equal-length arrays that should form the table. names : list of str, optional Names for the table columns. If not passed, schema must be passed. schema : Schema, default None Schema for the created table. If not passed, names must be passed. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from arrays: >>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"animals": "Name of the animal species"}) >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Name of the animal species' Construct a RecordBatch from multiple pyarrow.Arrays Parameters ---------- arrays : list of pyarrow.Array One for each field in RecordBatch names : list of str, optional Names for the batch fields. If not passed, schema must be passed schema : Schema, default None Schema for the created batch. If not passed, names must be passed metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from pyarrow Arrays using names: >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Construct a RecordBatch from pyarrow Arrays using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct ListViewArray from arrays of int32 offsets, sizes, and values. Parameters ---------- offsets : Array (int32 type) sizes : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : ListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] Construct ListArray from arrays of int32 offsets and values. Parameters ---------- offsets : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_array : ListArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], [ 3, 4 ] ] >>> # nulls in the offsets array become null lists >>> offsets = pa.array([0, None, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], null, [ 3, 4 ] ] Construct LargeListViewArray from arrays of int64 offsets and values. Parameters ---------- offsets : Array (int64 type) sizes : Array (int64 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : LargeListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] Construct FixedSizeListArray from array of values and a list length. Parameters ---------- values : Array (any type) list_size : int The fixed length of the lists. type : DataType, optional If not specified, a default ListType with the values' type and `list_size` length is used. mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- FixedSizeListArray Examples -------- Create from a values array and a list size: >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> arr = pa.FixedSizeListArray.from_arrays(values, 2) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] Or create from a values array, list size and matching type: >>> typ = pa.list_(pa.field("values", pa.int64()), 2) >>> arr = pa.FixedSizeListArray.from_arrays(values,type=typ) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] Concrete class for Uuid extension scalar. Concrete class for Arrow arrays of UUID data type. Concatenate the given arrays. The contents of the input arrays are copied into the returned array. Raises ------ ArrowInvalid If not all of the arrays have the same type. Parameters ---------- arrays : iterable of pyarrow.Array Arrays to concatenate, must be identically typed. memory_pool : MemoryPool, default None For memory allocations. If None, the default pool is used. Examples -------- >>> import pyarrow as pa >>> arr1 = pa.array([2, 4, 5, 100]) >>> arr2 = pa.array([2, 4]) >>> pa.concat_arrays([arr1, arr2]) [ 2, 4, 5, 100, 2, 4 ] Concatenate pyarrow.Table objects. If promote_options="none", a zero-copy concatenation will be performed. The schemas of all the Tables must be the same (except the metadata), otherwise an exception will be raised. The result Table will share the metadata with the first table. If promote_options="default", any null type arrays will be casted to the type of other arrays in the column of the same name. If a table is missing a particular field, null values of the appropriate type will be generated to take the place of the missing field. The new schema will share the metadata with the first table. Each field in the new schema will share the metadata with the first table which has the field defined. Note that type promotions may involve additional allocations on the given ``memory_pool``. If promote_options="permissive", the behavior of default plus types will be promoted to the common denominator that fits all the fields. Parameters ---------- tables : iterable of pyarrow.Table objects Pyarrow tables to concatenate into a single Table. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. promote_options : str, default none Accepts strings "none", "default" and "permissive". **kwargs : dict, optional Examples -------- >>> import pyarrow as pa >>> t1 = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> t2 = pa.table([ ... pa.array([2, 4]), ... pa.array(["Parrot", "Dog"]) ... ], names=['n_legs', 'animals']) >>> pa.concat_tables([t1,t2]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Parrot","Dog"]] Compute zero-copy slice of this ChunkedArray Parameters ---------- offset : int, default 0 Offset from start of array to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.slice(2,2) [ [ 4 ], [ 4 ] ] Compute distinct elements in array Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.unique() [ 2, 4, 5, 100 ] Compute dictionary-encoded representation of array. See :func:`pyarrow.compute.dictionary_encode` for full usage. Parameters ---------- null_encoding : str, default "mask" How to handle null entries. Returns ------- encoded : ChunkedArray A dictionary-encoded version of this array. Examples -------- >>> import pyarrow as pa >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> animals.dictionary_encode() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] Compute counts of unique elements in array. Returns ------- An array of structs Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.value_counts() -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] CompressedOutputStream.__reduce_cython__Column {} does not exist in schemaCodec.minimum_compression_levelCodec.maximum_compression_levelCodec.default_compression_levelChunkedArray.unify_dictionariesChunkedArray.to_string (line 118)ChunkedArray.fill_null (line 409)ChunkedArray data type was NULL Check if contents of two tables are equal. Parameters ---------- other : pyarrow.Table Table to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names=["n_legs", "animals"] >>> table = pa.Table.from_arrays([n_legs, animals], names=names) >>> table_0 = pa.Table.from_arrays([]) >>> table_1 = pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata={"n_legs": "Number of legs per animal"}) >>> table.equals(table) True >>> table.equals(table_0) False >>> table.equals(table_1) True >>> table.equals(table_1, check_metadata=True) False Check if contents of two record batches are equal. Parameters ---------- other : pyarrow.RecordBatch RecordBatch to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch_0 = pa.record_batch([]) >>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch.equals(batch) True >>> batch.equals(batch_0) False >>> batch.equals(batch_1) True >>> batch.equals(batch_1, check_metadata=True) False Cast table values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast table values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> table.cast(target_schema=my_schema) pyarrow.Table n_legs: duration[s] animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Cast record batch values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast batch values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> batch.cast(target_schema=my_schema) pyarrow.RecordBatch n_legs: duration[s] animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Cast array values to another data type See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, None Type to cast array to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- cast : Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Change the data type of an array: >>> n_legs_seconds = n_legs.cast(pa.duration('s')) >>> n_legs_seconds.type DurationType(duration[s]) Cannot specify a mask or a size when passing an object that is converted with the __arrow_array__ protocol.Can only instantiate subclasses of ExtensionTypeCalling .data on ChunkedArray is provided for compatibility after Column was removed, simply drop this attribute_CRecordBatchWriter.__setstate_cython___CRecordBatchWriter.__reduce_cython__ Byte width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().byte_width 8 BufferedOutputStream.__setstate_cython__Buffer size must be larger than zeroBool8Array.from_numpy (line 4601)BaseListArray.flatten (line 2339) Append column at end of columns. Parameters ---------- field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- Table or RecordBatch New table or record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Append column at the end: >>> year = [2021, 2022, 2019, 2021] >>> table.append_column('year', [year]) pyarrow.Table n_legs: int64 animals: string year: int64 ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] year: [[2021,2022,2019,2021]] Append a field at the end of the schema. In contrast to Python's ``list.append()`` it does return a new object, leaving the original Schema unmodified. Parameters ---------- field : Field Returns ------- schema: Schema New object with appended field. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Append a field 'extra' at the end of the schema: >>> schema_new = schema.append(pa.field('extra', pa.bool_())) >>> schema_new n_legs: int64 animals: string extra: bool Original schema is unmodified: >>> schema n_legs: int64 animals: string Alias for string(). Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.utf8() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.utf8()) [ "foo", "bar", "baz" ] Alias for large_string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_utf8() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_utf8()) [ "foo", "bar", ... "foo", "bar" ] Add column to Table at position. A new table is returned with the column added, the original table object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> table.add_column(0,"year", [year]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2021,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Original table is left unchanged: >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Add column to RecordBatch at position i. A new record batch is returned with the column added, the original record batch object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> batch.add_column(0,"year", year) pyarrow.RecordBatch year: int64 n_legs: int64 animals: string ---- year: [2021,2022,2019,2021] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Original record batch is left unchanged: >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Add a field at position i to the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Insert a new field on the second position: >>> schema.insert(1, pa.field('extra', pa.bool_())) n_legs: int64 extra: bool animals: string A grouping of columns in a table on which to perform aggregations. Parameters ---------- table : pyarrow.Table Input table to execute the aggregation on. keys : str or list[str] Name of the grouped columns. use_threads : bool, default True Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed. Examples -------- >>> import pyarrow as pa >>> t = pa.table([ ... pa.array(["a", "a", "b", "b", "c"]), ... pa.array([1, 2, 3, 4, 5]), ... ], names=["keys", "values"]) Grouping of columns: >>> pa.TableGroupBy(t,"keys") Perform aggregations: >>> pa.TableGroupBy(t,"keys").aggregate([("values", "sum")]) pyarrow.Table keys: string values_sum: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] A copy of this field with the replaced type Parameters ---------- new_type : pyarrow.DataType Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing type of an existing one: >>> field_new = field.with_type(pa.int64()) >>> field_new pyarrow.Field transfer_bandwidth_mib_per_sec's constructor directly, use one of the `__pyx_unpickle__PandasConvertible type: {0.type} shape: {0.shape} type: {0.type} shape: {0.shape} type: {0.type} shape: {0.shape} type: {0.type} shape: {0.shape}get_batch_with_custom_metadata Write RecordBatch to Buffer as encapsulated IPC message, which does not include a Schema. To reconstruct a RecordBatch from the encapsulated IPC message Buffer returned by this function, a Schema must be passed separately. See Examples. Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> buf = batch.serialize() >>> buf Reconstruct RecordBatch from IPC message Buffer and original Schema >>> pa.ipc.read_record_batch(buf, batch.schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] UnionArray does not have child {} Total number of bytes consumed by the elements of the chunked array. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.nbytes 49 The type code to indicate each data type in this union. Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.type_codes [0, 1] The timestamp time zone, if any, or None. Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('s', tz='UTC') >>> t.tz 'UTC' The time unit ('s' or 'ms'). Examples -------- >>> import pyarrow as pa >>> t = pa.time32('ms') >>> t.unit 'ms' The mode of the union ("dense" or "sparse"). Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.mode 'sparse' The field nullability. Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.nullable True >>> f2.nullable False The field name. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field.name 'key' The field for list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_field pyarrow.Field The field for items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_field pyarrow.Field The dimension (n) of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.ndim 2 Test if this field is equal to the other Parameters ---------- other : pyarrow.Field check_metadata : bool, default False Whether Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.equals(f2) False >>> f1.equals(f1) True _Tabular.drop_columns (line 2346)Table.schema.__get__ (line 5138)Table.rename_columns (line 5459)Table.nbytes.__get__ (line 5226)Table.combine_chunks (line 4468) Select values from the chunked array. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the chunked array with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : Array or ChunkedArray An array of the same type, with only the elements selected by the boolean mask. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> mask = pa.array([True, False, None, True, False, True]) >>> n_legs.filter(mask) [ [ 2 ], [ 4, 100 ] ] >>> n_legs.filter(mask, null_selection_behavior="emit_null") [ [ 2, null ], [ 4, 100 ] ] Schema.with_metadata (line 3386)Schema.types.__get__ (line 2906)Schema.names.__get__ (line 2878)RunEndEncodedArray.from_arrays Return whether the contents of two chunked arrays are equal. Parameters ---------- other : pyarrow.ChunkedArray Chunked array to compare against. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> n_legs.equals(n_legs) True >>> n_legs.equals(animals) False Return the underlying array of values which backs the ListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- ListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, None, 6]]) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] Return array of same length as list child values array where each output value is the index of the parent list array slot containing each child value. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_parent_indices() [ 0, 0, 0, 3 ] Render a "pretty-printed" string representation of the ChunkedArray Parameters ---------- indent : int How much to indent right the content of the array, by default ``0``. window : int How many items to preview within each chunk at the begin and end of the chunk when the chunk is bigger than the window. The other elements will be ellipsed. container_window : int How many chunks to preview at the begin and end of the array when the array is bigger than the window. The other elements will be ellipsed. This setting also applies to list columns. skip_new_lines : bool If the array should be rendered as a single line of text or if each element should be on its own line. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_string(skip_new_lines=True) '[[2,2,4],[4,5,100]]' RecordBatchReader.from_batchesRecordBatchReader._export_to_c_RecordBatchFileWriter.__reduce_cython___RecordBatchFileReader.read_all_RecordBatchFileReader.__reduce_cython____Pyx_EnumMeta.__setstate_cython__ Number of null entries Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.null_count 1 Null pointer (value before cast = NativeFile._download_nothreadsMapArray.from_arrays (line 3257) Lists the field names. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())]) >>> struct_type.names ['a', 'b', 'c'] ListViewType.value_field.__get__ (line 631)LargeListViewArray.sizes.__get__ (line 3220)LargeListViewArray.from_arraysLargeListType.value_type.__get__ (line 597)IpcReadOptions.__reduce_cython__ Flatten this field. If a struct field, individual child fields will be returned with their names prefixed by the parent's name. Returns ------- fields : List[pyarrow.Field] Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('bar', pa.float64(), nullable=False) >>> f2 = pa.field('foo', pa.int32()).with_metadata({"key": "Something important"}) >>> ff = pa.field('ff', pa.struct([f1, f2]), nullable=False) Flatten a struct field: >>> ff pyarrow.Field not null> >>> ff.flatten() [pyarrow.Field, pyarrow.Field] Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> month = pa.array([4, 6]) >>> table = pa.Table.from_arrays([struct,month], ... names = ["a", "month"]) >>> table pyarrow.Table a: struct child 0, animals: string child 1, n_legs: int64 child 2, year: int64 month: int64 ---- a: [ -- is_valid: all not null -- child 0 type: string ["Parrot",null] -- child 1 type: int64 [2,4] -- child 2 type: int64 [null,2022]] month: [[4,6]] Flatten the columns with struct field: >>> table.flatten() pyarrow.Table a.animals: string a.n_legs: int64 a.year: int64 month: int64 ---- a.animals: [["Parrot",null]] a.n_legs: [[2,4]] a.year: [[null,2022]] month: [[4,6]] Flatten this ChunkedArray into a single non-chunked array. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.combine_chunks() [ 2, 2, 4, 4, 5, 100 ] FixedSizeListArray.from_arraysField "{}" does not exist in schema_ExtensionRegistryNanny.release_registryDictionaryMemo.__reduce_cython__Decimal256Type.precision.__get__ (line 1473)Decimal128Type.precision.__get__ (line 1424) Create instance of signed int8 type. Examples -------- Create an instance of int8 type: >>> import pyarrow as pa >>> pa.int8() DataType(int8) >>> print(pa.int8()) int8 Create an array with int8 type: >>> pa.array([0, 1, 2], type=pa.int8()) [ 0, 1, 2 ] Create instance of fixed shape tensor extension type with shape and optional names of tensor dimensions and indices of the desired logical ordering of dimensions. Parameters ---------- value_type : DataType Data type of individual tensor elements. shape : tuple or list of integers The physical shape of the contained tensors. dim_names : tuple or list of strings, default None Explicit names to tensor dimensions. permutation : tuple or list integers, default None Indices of the desired ordering of the original dimensions. The indices contain a permutation of the values ``[0, 1, .., N-1]`` where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor. For more information on this parameter see :ref:`fixed_shape_tensor_extension`. Examples -------- Create an instance of fixed shape tensor extension type: >>> import pyarrow as pa >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2]) >>> tensor_type FixedShapeTensorType(extension) Inspect the data type: >>> tensor_type.value_type DataType(int32) >>> tensor_type.shape [2, 2] Create a table with fixed shape tensor extension array: >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]] >>> storage = pa.array(arr, pa.list_(pa.int32(), 4)) >>> tensor = pa.ExtensionArray.from_storage(tensor_type, storage) >>> pa.table([tensor], names=["tensor_array"]) pyarrow.Table tensor_array: extension ---- tensor_array: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]] Create an instance of fixed shape tensor extension type with names of tensor dimensions: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... dim_names=['C', 'H', 'W']) >>> tensor_type.dim_names ['C', 'H', 'W'] Create an instance of fixed shape tensor extension type with permutation: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... permutation=[0, 2, 1]) >>> tensor_type.permutation [0, 2, 1] Returns ------- type : FixedShapeTensorType Create instance of an interval type representing months, days and nanoseconds between two dates. Examples -------- Create an instance of an month_day_nano_interval type: >>> import pyarrow as pa >>> pa.month_day_nano_interval() DataType(month_day_nano_interval) Create a scalar with month_day_nano_interval type: >>> pa.scalar((1, 15, -30), type=pa.month_day_nano_interval()) Create instance of 64-bit time (time of day) type with unit resolution. Parameters ---------- unit : str One of 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.Time64Type Examples -------- >>> import pyarrow as pa >>> pa.time64('us') Time64Type(time64[us]) >>> pa.time64('ns') Time64Type(time64[ns]) Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01). Examples -------- Create an instance of 64-bit date type: >>> import pyarrow as pa >>> pa.date64() DataType(date64[ms]) Create a scalar with 64-bit date type: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.date64()) Create instance of 32-bit time (time of day) type with unit resolution. Parameters ---------- unit : str one of 's' [second], or 'ms' [millisecond] Returns ------- type : pyarrow.Time32Type Examples -------- >>> import pyarrow as pa >>> pa.time32('s') Time32Type(time32[s]) >>> pa.time32('ms') Time32Type(time32[ms]) Create instance of 32-bit date (days since UNIX epoch 1970-01-01). Examples -------- Create an instance of 32-bit date type: >>> import pyarrow as pa >>> pa.date32() DataType(date32[day]) Create a scalar with 32-bit date type: >>> from datetime import date >>> pa.scalar(date(2012, 1, 1), type=pa.date32()) Create half-precision floating point type. Examples -------- Create an instance of float16 type: >>> import pyarrow as pa >>> pa.float16() DataType(halffloat) >>> print(pa.float16()) halffloat Create an array with float16 type: >>> arr = np.array([1.5, np.nan], dtype=np.float16) >>> a = pa.array(arr, type=pa.float16()) >>> a [ 15872, 32256 ] Note that unlike other float types, if you convert this array to a python list, the types of its elements will be ``np.float16`` >>> [type(val) for val in a.to_pylist()] [, ] Create an Array instance whose slots are the given scalar. Parameters ---------- value : Scalar-like object Either a pyarrow.Scalar or any python object coercible to a Scalar. size : int Number of times to repeat the scalar in the output Array. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.repeat(10, 3) [ 10, 10, 10 ] >>> pa.repeat([1, 2], 2) [ [ 1, 2 ], [ 1, 2 ] ] >>> pa.repeat("string", 3) [ "string", "string", "string" ] >>> pa.repeat(pa.scalar({'a': 1, 'b': [1, 2]}), 2) -- is_valid: all not null -- child 0 type: int64 [ 1, 1 ] -- child 1 type: list [ [ 1, 2 ], [ 1, 2 ] ] Convert to a pandas-compatible NumPy array or DataFrame, as appropriate Parameters ---------- memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. categories : list, default empty List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures. strings_to_categorical : bool, default False Encode string (UTF8) and binary types to pandas.Categorical. zero_copy_only : bool, default False Raise an ArrowException if this function call would require copying the underlying data. integer_object_nulls : bool, default False Cast integers with nulls to objects date_as_object : bool, default True Cast dates to objects. If False, convert to datetime64 dtype with the equivalent time unit (if supported). Note: in pandas version < 2.0, only datetime64[ns] conversion is supported. timestamp_as_object : bool, default False Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful in pandas version 1.x if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). Non-nanosecond timestamps are supported in pandas version 2.0. If False, all timestamps are converted to datetime64 dtype. use_threads : bool, default True Whether to parallelize the conversion using multiple threads. deduplicate_objects : bool, default True Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower. ignore_metadata : bool, default False If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present safe : bool, default True For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not. split_blocks : bool, default False If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use. self_destruct : bool, default False EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program. Note that you may not see always memory usage improvements. For example, if multiple columns share an underlying allocation, memory can't be freed until all columns are converted. maps_as_pydicts : str, optional, default `None` Valid values are `None`, 'lossy', or 'strict'. The default behavior (`None`), is to convert Arrow Map arrays to Python association lists (list-of-tuples) in the same order as the Arrow Map, as in [(key1, value1), (key2, value2), ...]. If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts. This can change the ordering of (key, value) pairs, and will deduplicate multiple keys, resulting in a possible loss of data. If 'lossy', this key deduplication results in a warning printed when detected. If 'strict', this instead results in an exception being raised when detected. types_mapper : function, default None A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or ``None`` if the default conversion should be used for that type. If you have a dictionary mapping, you can pass ``dict.get`` as function. coerce_temporal_nanoseconds : bool, default False Only applicable to pandas version >= 2.0. A legacy option to coerce date32, date64, duration, and timestamp time units to nanoseconds when converting to pandas. This is the default behavior in pandas version 1.x. Set this option to True if you'd like to use this coercion when using pandas version >= 2.0 for backwards compatibility (not recommended otherwise). Returns ------- pandas.Series or pandas.DataFrame depending on type of object Examples -------- >>> import pyarrow as pa >>> import pandas as pd Convert a Table to pandas DataFrame: >>> table = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> table.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(table.to_pandas(), pd.DataFrame) True Convert a RecordBatch to pandas DataFrame: >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(batch.to_pandas(), pd.DataFrame) True Convert a Chunked Array to pandas Series: >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_pandas() 0 2 1 2 2 4 3 4 4 5 5 100 dtype: int64 >>> isinstance(n_legs.to_pandas(), pd.Series) True Convert pandas.DataFrame to an Arrow RecordBatch Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the RecordBatch. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``RecordBatch``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) Convert pandas DataFrame to RecordBatch: >>> import pyarrow as pa >>> pa.RecordBatch.from_pandas(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_pandas(df, schema=my_schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch specifying columns: >>> pa.RecordBatch.from_pandas(df, columns=["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] Convert numpy tensors (ndarrays) to a fixed shape tensor extension array. The first dimension of ndarray will become the length of the fixed shape tensor array. If input array data is not contiguous a copy will be made. Parameters ---------- obj : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = np.array( ... [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]], ... dtype=np.float32) >>> pa.FixedShapeTensorArray.from_numpy_ndarray(arr) [ [ 1, 2, 3, 4, 5, 6 ], [ 1, 2, 3, 4, 5, 6 ] ] Convert numpy array to a bool8 extension array without making a copy. The input array must be 1-dimensional, with either bool_ or int8 dtype. Parameters ---------- obj : numpy.ndarray Returns ------- bool8_array : Bool8Array Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = np.array([True, False, True], dtype=np.bool_) >>> pa.Bool8Array.from_numpy(arr) [ 1, 0, 1 ] Convert NumPy dtype to pyarrow.DataType. Parameters ---------- dtype : the numpy dtype to convert Examples -------- Create a pyarrow DataType from NumPy dtype: >>> import pyarrow as pa >>> import numpy as np >>> pa.from_numpy_dtype(np.dtype('float16')) DataType(halffloat) >>> pa.from_numpy_dtype('U') DataType(string) >>> pa.from_numpy_dtype(bool) DataType(bool) >>> pa.from_numpy_dtype(np.str_) DataType(string) Construct a RecordBatch from a StructArray. Each field in the StructArray will become a column in the resulting ``RecordBatch``. Parameters ---------- struct_array : StructArray Array to construct the record batch from. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.RecordBatch.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 Construct MapArray from arrays of int32 offsets and key, item arrays. Parameters ---------- offsets : array-like or sequence (int32 type) keys : array-like or sequence (any type) items : array-like or sequence (any type) type : DataType, optional If not specified, a default MapArray with the keys' and items' type is used. pool : MemoryPool mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- map_array : MapArray Examples -------- First, let's understand the structure of our dataset when viewed in a rectangular data model. The total of 5 respondents answered the question "How much did you like the movie x?". The value -1 in the integer array means that the value is missing. The boolean array represents the null bitmask corresponding to the missing values in the integer array. >>> import pyarrow as pa >>> movies_rectangular = np.ma.masked_array([ ... [10, -1, -1], ... [8, 4, 5], ... [-1, 10, 3], ... [-1, -1, -1], ... [-1, -1, -1] ... ], ... [ ... [False, True, True], ... [False, False, False], ... [True, False, False], ... [True, True, True], ... [True, True, True], ... ]) To represent the same data with the MapArray and from_arrays, the data is formed like this: >>> offsets = [ ... 0, # -- row 1 start ... 1, # -- row 2 start ... 4, # -- row 3 start ... 6, # -- row 4 start ... 6, # -- row 5 start ... 6, # -- row 5 end ... ] >>> movies = [ ... "Dark Knight", # ---------------------------------- row 1 ... "Dark Knight", "Meet the Parents", "Superman", # -- row 2 ... "Meet the Parents", "Superman", # ----------------- row 3 ... ] >>> likings = [ ... 10, # -------- row 1 ... 8, 4, 5, # --- row 2 ... 10, 3 # ------ row 3 ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 [] 4 [] dtype: object If the data in the empty rows needs to be marked as missing, it's possible to do so by modifying the offsets argument, so that we specify `None` as the starting positions of the rows we want marked as missing. The end row offset still has to refer to the existing value from keys (and values): >>> offsets = [ ... 0, # ----- row 1 start ... 1, # ----- row 2 start ... 4, # ----- row 3 start ... None, # -- row 4 start ... None, # -- row 5 start ... 6, # ----- row 5 end ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 None 4 None dtype: object Compare contents of this array against another one. Return a string containing the result of diffing this array (on the left side) against the other array (on the right side). Parameters ---------- other : Array The other array to compare this array with. Returns ------- diff : str A human-readable printout of the differences. Examples -------- >>> import pyarrow as pa >>> left = pa.array(["one", "two", "three"]) >>> right = pa.array(["two", None, "two-and-a-half", "three"]) >>> print(left.diff(right)) # doctest: +SKIP @@ -0, +0 @@ -"one" @@ -2, +1 @@ +null +"two-and-a-half" ChunkedArray.to_numpy (line 490)ChunkedArray.is_valid (line 377)ChunkedArray.dictionary_encode_CRecordBatchWriter.write_table_CRecordBatchWriter.write_batchBufferReader.__setstate_cython__ Bit width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().bit_width 64 Add metadata as dict of string keys and values to Field Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) Create new field by adding metadata to existing one: >>> field_new = field.with_metadata({"key": "Something important"}) >>> field_new pyarrow.Field >>> field_new.metadata {b'key': b'Something important'} _unregister_py_extension_typespyarrow.interchange.dataframepyarrow.Message(uninitialized)child is deprecated, use fieldTensor.shape.__get__ (line 250)_Tabular.itercolumns (line 2037)_Tabular.from_pylist (line 1973)_Tabular.from_pydict (line 1906)_Tabular._ensure_integer_indexTable.replace_schema_metadataTable.remove_column (line 5366)StringBuilder.__reduce_cython__Schema._import_from_c_capsuleRecordBatch.num_columns.__get__ (line 2639)RecordBatch.from_struct_array_RecordBatchStreamWriter._open_RecordBatchStreamReader._openRecordBatchReader.from_stream_RecordBatchFileReader.__enter__PyExtensionType.set_auto_load_PandasAPIShim.__reduce_cython__Only stream=None is supported.MessageReader.__reduce_cython__MemoryManager.__reduce_cython__ListViewType.value_type.__get__ (line 644)LargeStringArray.from_buffersLargeListViewType.value_field.__get__ (line 679)Incompatible storage type for FixedSizeListType.value_field.__get__ (line 814)FixedShapeTensorType.__reduce__Field.with_nullable (line 2642)Field.with_metadata (line 2515)DictionaryScalar._reconstructChunkedArray.num_chunks.__get__ (line 1249)ChunkedArray.null_count.__get__ (line 210)ChunkedArray.is_null (line 318)ChunkedArray.flatten (line 659)ChunkedArray.__arrow_c_stream__only valid on writable filesonly valid on seekable filesonly valid on readable files_make_shape_or_strides_buffer_import_from_c_device_capsulefixed_shape_tensor (line 5314)default_memory_pool (line 124)benchmark_PandasObjectIsNullType_INTERVAL_MONTH_DAY_NANOTensor.size.__get__ (line 234)Tensor.ndim.__get__ (line 218)Table.from_batches (line 4926)Table.__get__..genexprStructArray._flattened_fieldSchema.get_all_field_indicesSchema.from_pandas (line 3030)Schema.empty_table (line 2963)RecordBatch.select (line 3204)RecordBatch.equals (line 3155)RecordBatch.__arrow_c_stream___RecordBatchFileReader.__exit____Pyx_EnumMeta.__reduce_cython__PythonFile.__setstate_cython___PandasAPIShim.is_categoricalOpaqueType.__arrow_ext_class__NativeFile._upload_nothreadsNativeFile.__setstate_cython__MemoryPool.__setstate_cython__Invalid time unit for time64: Invalid time unit for time32: Indices must be integer typeI/O operation on closed fileFixedSizeBinaryType.__reduce__Field.name.__get__ (line 2482)Field "{}" exists {} times in schemaField._import_from_c_capsuleExtensionScalar.from_storageExpected a pointer value, got Expected a non-empty ndarrayDictionaryArray.from_buffersChunkedArray.unique (line 778)ChunkedArray.is_nan (line 352)ChunkedArray.filter (line 921)ChunkedArray.equals (line 446)ChunkedArray.chunk (line 1267)BufferReader.__reduce_cython__BaseExtensionType.wrap_arrayArray._import_from_c_capsuleArray.__arrow_c_device_array____pyx_unpickle___Pyx_EnumMeta__pyx_unpickle__PandasAPIShim_py_extension_type_auto_load_handle_arrow_array_protocolcreate_memory_map (line 1157)coerce_temporal_nanoseconds_Tabular.to_pylist (line 2284)_Tabular.to_pydict (line 2258)_Tabular.drop_null (line 1842)Table.get_total_buffer_sizeTable.from_pandas (line 4688)Table.from_arrays (line 4768)StringBuilder.append_valuesStopToken.__setstate_cython__SparseCSRMatrix.from_tensorSparseCSFTensor.from_tensorSparseCSCMatrix.from_tensorSparseCOOTensor.from_tensorRecordBatch.to_struct_arrayRecordBatch.slice (line 3098)RecordBatch._is_initializedRecordBatch.__arrow_c_array___RecordBatchFileWriter._open_RecordBatchFileReader._open_ReadPandasMixin.read_pandasPlease implement {0}.__reduce___PandasConvertible.to_pandas_PandasAPIShim.is_datetimetz_PandasAPIShim.is_data_frame_PandasAPIShim.is_array_likeOffset must be non-negativeNativeFile._assert_writableNativeFile._assert_seekableNativeFile._assert_readableMust pass decompressed_sizeMask must not contain nullsLength must be non-negativeExtensionArray.from_storageExpected int index, got type 'DictionaryArray.from_arraysChunkedArray.take (line 1034)ChunkedArray.slice (line 863)ChunkedArray.length (line 98)ChunkedArray.index (line 986)ChunkedArray.combine_chunksBufferedOutputStream.detachBufferOutputStream.getvalueBool8Type.__arrow_ext_class__Batch number {0} out of rangeBaseListArray.value_lengthsArray.get_total_buffer_sizeArray._import_from_c_devicesupports_compression_level_register_py_extension_typemax_ideal_request_size_mibfrom_numpy_dtype (line 5684)default_cpu_memory_managerUuidType.__arrow_ext_class__Table.to_batches (line 4999)Table.set_column (line 5400)Table.add_column (line 5292)StructType.get_field_indexSparseCSRMatrix.from_scipySparseCSRMatrix.from_numpySparseCSFTensor.from_numpySparseCSCMatrix.from_scipySparseCSCMatrix.from_numpySparseCOOTensor.from_scipySparseCOOTensor.from_numpySchema.serialize (line 3422)RunEndEncodedType.__reduce__RecordBatch.rename_columnsRecordBatch.cast (line 3258)RecordBatch._import_from_cRecordBatchReader.read_allPythonFile.__reduce_cython___PandasAPIShim.pandas_dtypeOperation on closed writerOperation on closed readerNativeFile.__reduce_cython__MonthDayNanoIntervalScalarMemoryPool.bytes_allocatedMemoryPool.__reduce_cython__Mask must be boolean dtypeLargeListViewType.__reduce__LargeListArray.from_arraysFixedSizeListType.__reduce__Failed to allocate {0} bytesExpected list or tuple, got {}, {}ChunkedArray.cast (line 560)_CRecordBatchWriter.__enter__BufferedInputStream.detachunregister_extension_typetime_to_first_byte_millissupported_memory_backends, please pass it explicitlynum_replaced_dictionariesminimum_compression_levelmaximum_compression_levellarge_list_view (line 4905)default_compression_levelUnionType.field (line 1146)Tensor.from_numpy (line 61)_Tabular.sort_by (line 2095)_Tabular.__setstate_cython__Table.to_reader (line 5069)Table.join_asof (line 5711)StructType.field (line 956)StructScalar._as_py_tupleStopToken.__reduce_cython__SparseCSRMatrix.to_tensorSparseCSFTensor.to_tensorSparseCSCMatrix.to_tensorSparseCOOTensor.to_tensorSignalStopHandler.__enter__Scalar type not supportedScalar data type was NULLRunEndEncodedScalar.as_pyRecordBatch.remove_column_RecordBatchWithMetadataRecordBatchReader.__enter___PandasAPIShim.infer_dtypeNot an ArrowSchema objectMonthDayNanoIntervalArrayMessage.__setstate_cython__MessageReader.open_streamMemoryPool.release_unusedListViewArray.from_arraysKeyValueMetadata.__reduce__Invalid value of whence: {0}Field.with_type (line 2575)Field.with_name (line 2610)DictionaryScalar.__reduce__DataType.__arrow_c_schema__ChunkedArray.value_countsCacheOptions._reconstruct_CRecordBatchWriter.__exit__Array._export_to_c_devicetranscoding_input_stream_reconstruct_record_batchpyarrow.vendored.versionis_extension_array_dtypeget_rangeindex_attributedownload..bg_writeTensor.dim_name (line 148)Tensor.__setstate_cython___Tabular.filter (line 2185)_Tabular.column (line 1730)Table.unify_dictionariesTable.group_by (line 5551)StringViewBuilder.finishStringViewBuilder.appendStringArray.from_buffersSparseCSRMatrix was NULLSparseCSRMatrix.to_scipySparseCSRMatrix.to_numpySparseCSRMatrix.dim_nameSparseCSFTensor was NULLSparseCSFTensor.to_numpySparseCSFTensor.dim_nameSparseCSCMatrix was NULLSparseCSCMatrix.to_scipySparseCSCMatrix.to_numpySparseCSCMatrix.dim_nameSparseCOOTensor was NULLSparseCOOTensor.to_scipySparseCOOTensor.to_numpySparseCOOTensor.dim_nameSignalStopHandler.__exit__RecordBatch._export_to_cRecordBatchReader.__exit__PyExtensionType.__reduce___PandasAPIShim.get_values_PandasAPIShim.data_frameOSFile.__setstate_cython__KeyValueMetadata.to_dictKeyValueMetadata.get_allDevice.__setstate_cython__DataType.to_pandas_dtypeDataType.equals (line 347)ChunkedArray._assert_cpuChunk index out of range._CRecordBatchWriter.write_CRecordBatchWriter.closeArrowNotImplementedErrorArray data type was NULLreplace_schema_metadataregister_extension_typepyarrow/pandas-shim.pxioutput_stream (line 2831)null_selection_behaviormonth_day_nano_interval.from_*` functions instead.download..cleanupconcat_tables (line 6179)concat_arrays (line 4730)chunked_array (line 1457)arrow.py_extension_typeWrapping scalar of type Tensor.to_numpy (line 96)_Tabular.field (line 1877)_Tabular._is_initialized_Tabular.__reduce_cython__Table.from_struct_arrayTable.flatten (line 4402)StructArray.from_arraysSchema.remove (line 3305)Schema.insert (line 3267)Schema.equals (line 2991)Schema.append (line 3228)Schema.__arrow_c_schema__RecordBatch.from_pandasRecordBatch.from_arraysRecordBatchWithMetadata_RecordBatchStreamWriter_RecordBatchStreamReaderRecordBatchReader.close_PandasAPIShim.is_sparse_PandasAPIShim.is_series_PandasAPIShim.is_ge_v21PYARROW_IGNORE_TIMEZONENativeFile._assert_openMessage.__reduce_cython__MemoryMappedFile.resizeMemoryMappedFile.filenoMemoryMappedFile.createKeyValueMetadata.valuesKeyValueMetadata.equalsInvalid promote options: Field.flatten (line 2681)Expected sparse.COO, got {}DictionaryType.__reduce__DictionaryEncodeOptionsDecimal256Type.__reduce__Decimal128Type.__reduce__DataType expected, got {!r}DataType._import_from_cCodec.__setstate_cython__ChunkedArray.iterchunksChunkedArray._to_pandasBool8Array.from_storageArrowSerializationErrorArray.dictionary_encodewritable file expectedupload..bg_writestrings_to_categoricalrecord_batch (line 5840)readable file expectednum_dictionary_batcheslog_memory_allocationslarge_string (line 4657)large_binary (line 4629)input_stream (line 2745)fixed_size_binary_typeenable_signal_handlersemit_dictionary_deltas__arrow_ext_scalar_class__UnionArray.from_sparseType_FIXED_SIZE_BINARYTimestampType.__reduce__Tensor.equals (line 118)Tensor.__reduce_cython___Tabular.take (line 2146)Table.select (line 4288)Table.equals (line 4575)Table.__arrow_c_stream__TableGroupBy.aggregateSparseCSRMatrix.equalsSparseCSFTensor.equalsSparseCSCMatrix.equalsSparseCOOTensor.equalsSchema.remove_metadataSchema.get_field_indexSchema.field (line 3076)ScalarAggregateOptionsResizableBuffer.resizeRecordBatch.set_columnRecordBatch.add_columnRecordBatch._to_pandasRecordBatchReader.cast_PandasAPIShim.is_index_PandasAPIShim.is_ge_v3OSFile.__reduce_cython__Not a metadata version: NativeFile.read_bufferMemoryMappedFile._openLargeListType.__reduce__KeyValueMetadata.valueKeyValueMetadata.itemsInvalid union mode {0!r}FixedShapeTensorScalarField.equals (line 2420)Field.__arrow_c_schema__ExtensionType.__reduce___ExtensionRegistryNannyDictionaryScalar.as_pyDevice.__reduce_cython__Decimal256Scalar.as_pyDecimal128Scalar.as_py-D array to bool8 arrayCompressedOutputStreamChunkedArray.to_stringChunkedArray.to_pylistChunkedArray.fill_nullChunkedArray.drop_nullBinaryScalar.as_buffer{0.__class__.__name__}({1}){0.__class__.__name__}({0})total_allocated_bytesstring_view (line 4729)set_memcopy_thresholdset_memcopy_blocksize__pyx_unpickle__Tabularpyarrow.pandas_compatpyarrow/benchmark.pxinum_dictionary_deltas_ndarray_to_arrow_typejemalloc_set_decay_ms__init__..genexpr_import_from_c_capsuleget_total_buffer_sizeget_record_batch_sizeget_all_field_indicesbinary_view (line 4714)__arrow_ext_deserialize__UnionArray.from_denseTimestampScalar.as_py_Tabular.remove_column_Tabular.append_columnTable.to_struct_arrayTable.slice (line 4223)Table._is_initializedSchema._import_from_cRecordBatch.to_tensorRecordBatch.serialize_RecordBatchFileWriter_RecordBatchFileReaderNativeFile.writelinesNativeFile.get_streamMockOutputStream.sizeMemoryPool.max_memoryMask must be 1D arrayListViewType.__reduce__ListArray.from_arraysKeyValueMetadata.keysHalfFloatScalar.as_pyFixedSizeBufferWriterFixedSizeBinaryScalarFixedShapeTensorArrayField.remove_metadataExtensionScalar.as_pyDataType._export_to_cCompressedInputStreamCodec.__reduce_cython__ChunkedArray was NULLChunkedArray.validateChunkedArray.to_numpyChunkedArray.is_validChunkedArray.__sizeof__ChunkedArray.__reduce__CacheOptions.__reduce__Bool8Array.from_numpyBaseListArray.flattenArrowCancelled.__init__Array.__dlpack_device__Array.__arrow_c_array__value_parent_indicesuse_pandas_sentinelsshow_schema_metadataset_timezone_db_pathmimalloc_memory_poolmemory_map (line 1115)large_utf8 (line 4687)large_list (line 4810)jemalloc_memory_poolitems..genexpris_threading_enabledinteger_object_nulls_import_from_c_devicehave_signal_refcyclehas_canonical_formatfrom_network_metricsfind_physical_offsetfind_physical_lengthensure_native_endian_ensure_integer_indexdictionary (line 5018)decimal128 (line 4441)__arrow_c_device_array__UnsupportedOperationUnknown enum value: '%s'UnknownExtensionTypeType_RUN_END_ENCODEDType_LARGE_LIST_VIEWType_FIXED_SIZE_LISTTransformInputStream_Tabular.drop_columnsTable.rename_columnsTable.join (line 5594)Table.combine_chunksTable.cast (line 4624)StringBuilder.finishStringBuilder.appendSchema.with_metadataSchema.set (line 3336)Schema.field_by_nameRecordBatch.validateRecordBatch.__sizeof__RecordBatch.__reduce__PythonFile.readlines_PandasAPIShim.seriesNon-fixed width typeNativeFile.readlinesMessage.serialize_toMapArray.from_arraysKeyValueMetadata.keyFixedSizeBinaryArrayFixedShapeTensorTypeField._import_from_cDurationScalar.as_pyDeviceAllocationTypeChunkedArray.is_nullChunkedArray.flattenChunkedArray.__array__BufferedOutputStreamArray._import_from_c to requested schema timestamp (line 4095)timestamp_as_objectshow_field_metadataset_memcopy_threadsset_io_thread_countpyarrow/builder.pxilogging_memory_poollist_view (line 4867)list_parent_indicesindex out of bounds_get_pandas_type_mapget_datetimetz_typedetected_simd_leveldefault_memory_pooldeduplicate_objectsdataframe_to_arrays compression_level=batch_with_metadata__arrow_ext_serialize__allow_none_for_type_Tabular.itercolumns_Tabular.from_pylist_Tabular.from_pydict_Tabular.__dataframe__Table.remove_columnTableGroupBy.__init__StructType.__reduce__StructScalar.__iter__StructArray.flattenSchema.add_metadataSchema._export_to_cRunEndEncodedScalarRecordBatch.copy_toRecordBatch._column__Pyx_FlagBase.__repr____Pyx_EnumBase.__repr__PythonFile.truncatePythonFile.readline_PandasAPIShim.is_v1OpaqueType.__reduce__NotImplementedErrorNo type alias for {0}NativeFile.writableNativeFile.truncateNativeFile.seekableNativeFile.readlineNativeFile.readintoNativeFile.readableNativeFile.metadataNativeFile.downloadLargeListViewScalarInvalid file mode: {0}FixedSizeListScalarFixedSizeBinaryTypeField.with_nullableField.with_metadataExpected Schema, got {}End of Arrow streamDEFAULT_BUFFER_SIZEChunkedArray.uniqueChunkedArray.lengthChunkedArray.is_nanChunkedArray.formatChunkedArray.filterChunkedArray.equalsChunkedArray.__iter__BufferedInputStreamBooleanScalar.as_pyBool8Array.to_numpyArrowKeyError.__str__Array.diff (line 943)unify_dictionaries' to pointer addresstable_to_dataframesystem_memory_poolpyarrow/tensor.pxipyarrow/scalar.pxipyarrow/memory.pxipyarrow/device.pxipyarrow/config.pxipyarrow/compat.pxinum_record_batchesnon_default_kwargsincrementalencoderincrementaldecoder_get_pandas_tz_typefrom_pydata_sparsefrom_numpy_ndarrayfixed_shape_tensor_export_to_c_device_ensure_cuda_loadedduration (line 4240)_download_nothreads_detect_compression_default_chunk_sizedataframe_to_typescline_in_tracebackasyncio.coroutinesUnionType.__reduce__UInt64Scalar.as_pyUInt32Scalar.as_pyUInt16Scalar.as_pyTime64Scalar.as_pyTime32Scalar.as_py_Tabular.add_columnTable.from_batchesStructScalar.itemsStructScalar.as_pyStringScalar.as_pySchema.from_pandasSchema.empty_tableRunEndEncodedArrayRecordBatch.selectRecordBatch.equals__Pyx_FlagBase.__str____Pyx_FlagBase.__new____Pyx_EnumBase.__str____Pyx_EnumBase.__new__NativeFile.readallNativeFile.read_atNativeFile.__enter__ListFlattenOptionsLess than one byteLargeListViewArrayInvalid merge mode: FloatingPointArrayFixedSizeListArrayField._export_to_cExpected Array, got DoubleScalar.as_pyDate64Scalar.as_pyDate32Scalar.as_pyColumn {!r} not foundCodec.is_availableChunkedArray.sliceChunkedArray.indexChunkedArray.chunk_CRecordBatchWriterBuffer._assert_cpuBuffer.__reduce_ex__BufferOutputStreamBool8Type.__reduce__BinaryScalar.as_pyArrowCapacityErrorArray.value_countsArray.from_buffersArray._export_to_cArray._debug_printuse_legacy_formattruncate_metadatatotal_buffer_size to requested type to_pandas_dtype_reconstruct_tableread_record_batchread_next_messagepyarrow/types.pxipyarrow/table.pxipyarrow/error.pxipyarrow/array.pxiproxy_memory_pool_perform_join_asoflist_value_lengthfrom_struct_arrayfloat64 (line 4414)float32 (line 4387)float16 (line 4353)extension_columnsdictionary_encodedictionary_decodedecompressed_size_datetime_from_intcreate_memory_mapcompression_levelc_tensor_ext_typebytes_allocatedUuidType.__reduce__UnionScalar.as_pyUInt8Scalar.as_pyType_SPARSE_UNIONType_LARGE_STRINGType_LARGE_BINARYTranscoder.__init__Transcoder.__call__Tensor.from_numpy_Tabular.to_string_Tabular.to_pylist_Tabular.to_pydict_Tabular.drop_nullTable.from_pandasTable.from_arraysStructType.__iter__StructArray.fieldStringViewBuilderSignalStopHandlerRunEndEncodedTypeRecordBatch.sliceRecordBatchReader_PandasConvertibleNativeFile.uploadNativeFile.isattyNativeFile.filenoNativeFile.__exit__Message.serializeLoggingMemoryPoolListType.__reduce__LargeStringScalarLargeListViewTypeLargeBinaryScalarInvalid time unit: Int64Scalar.as_pyInt32Scalar.as_pyInt16Scalar.as_pyFloatScalar.as_pyFixedSizeListTypeDataType.__reduce__ChunkedArray.takeChunkedArray.sortChunkedArray.castBuffer.to_pybytesBool8Scalar.as_pyBaseExtensionTypeArray.from_pandas_upload_nothreadsuint64 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