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DEPRECATED: Deserialize Python object from Buffer or other Python
object supporting the buffer protocol.
.. deprecated:: 2.0
The custom serialization functionality is deprecated in pyarrow 2.0,
and will be removed in a future version. Use the standard library
``pickle`` or the IPC functionality of pyarrow (see :ref:`ipc` for
more).
This only can interact with data produced by pyarrow.serialize or
pyarrow.serialize_to.
Parameters
----------
obj : pyarrow.Buffer or Python object supporting buffer protocol
context : SerializationContext
Custom serialization and deserialization context.
Returns
-------
deserialized : object
deserialize_components(components, SerializationContext context=None)
DEPRECATED: Reconstruct Python object from output of
SerializedPyObject.to_components.
.. deprecated:: 2.0
The custom serialization functionality is deprecated in pyarrow 2.0,
and will be removed in a future version. Use the standard library
``pickle`` or the IPC functionality of pyarrow (see :ref:`ipc` for
more).
Parameters
----------
components : dict
Output of SerializedPyObject.to_components
context : SerializationContext, default None
Returns
-------
object : the Python object that was originally serialized
deserialize_from(source, base, SerializationContext context=None)
DEPRECATED: Deserialize a Python sequence from a file.
.. deprecated:: 2.0
The custom serialization functionality is deprecated in pyarrow 2.0,
and will be removed in a future version. Use the standard library
``pickle`` or the IPC functionality of pyarrow (see :ref:`ipc` for
more).
This only can interact with data produced by pyarrow.serialize or
pyarrow.serialize_to.
Parameters
----------
source : NativeFile
File to read the sequence from.
base : object
This object will be the base object of all the numpy arrays
contained in the sequence.
context : SerializationContext
Custom serialization and deserialization context.
Returns
-------
object
Python object for the deserialized sequence.
_read_serialized(source, base=None) read_serialized(source, base=None)
DEPRECATED: Read serialized Python sequence from file-like object.
.. deprecated:: 2.0
The custom serialization functionality is deprecated in pyarrow 2.0,
and will be removed in a future version. Use the standard library
``pickle`` or the IPC functionality of pyarrow (see :ref:`ipc` for
more).
Parameters
----------
source : NativeFile
File to read the sequence from.
base : object
This object will be the base object of all the numpy arrays
contained in the sequence.
Returns
-------
serialized : the serialized data
serialize_to(value, sink, SerializationContext context=None)
DEPRECATED: Serialize a Python sequence to a file.
.. deprecated:: 2.0
The custom serialization functionality is deprecated in pyarrow 2.0,
and will be removed in a future version. Use the standard library
``pickle`` or the IPC functionality of pyarrow (see :ref:`ipc` for
more).
Parameters
----------
value : object
Python object for the sequence that is to be serialized.
sink : NativeFile or file-like
File the sequence will be written to.
context : SerializationContext
Custom serialization and deserialization context, uses a default
context with some standard type handlers if not specified.
_serialize(value, SerializationContext context=None) serialize(value, SerializationContext context=None)
DEPRECATED: Serialize a general Python sequence for transient storage
and transport.
.. deprecated:: 2.0
The custom serialization functionality is deprecated in pyarrow 2.0,
and will be removed in a future version. Use the standard library
``pickle`` or the IPC functionality of pyarrow (see :ref:`ipc` for
more).
Notes
-----
This function produces data that is incompatible with the standard
Arrow IPC binary protocol, i.e. it cannot be used with ipc.open_stream or
ipc.open_file. You can use deserialize, deserialize_from, or
deserialize_components to read it.
Parameters
----------
value : object
Python object for the sequence that is to be serialized.
context : SerializationContext
Custom serialization and deserialization context, uses a default
context with some standard type handlers if not specified.
Returns
-------
serialized : SerializedPyObject
SerializedPyObject.__setstate_cython__(self, __pyx_state) SerializedPyObject.__reduce_cython__(self) SerializedPyObject.to_components(self, memory_pool=None)
Return the decomposed dict representation of the serialized object
containing a collection of Buffer objects which maximize opportunities
for zero-copy.
Parameters
----------
memory_pool : MemoryPool default None
Pool to use for necessary allocations.
Returns
SerializedPyObject.from_components(components)
Reconstruct SerializedPyObject from output of
SerializedPyObject.to_components.
SerializedPyObject.to_buffer(self, nthreads=1)
Write serialized data as Buffer.
SerializedPyObject.deserialize(self, SerializationContext context=None)
Convert back to Python object.
SerializedPyObject.write_to(self, sink)
Write serialized object to a sink.
_get_default_context() SerializationContext.__setstate_cython__(self, __pyx_state) SerializationContext.__reduce_cython__(self) SerializationContext.deserialize_components(self, what)
Call pyarrow.deserialize_components and pass this SerializationContext.
SerializationContext.deserialize(self, what)
Call pyarrow.deserialize and pass this SerializationContext.
SerializationContext.serialize_to(self, value, sink)
Call pyarrow.serialize_to and pass this SerializationContext.
SerializationContext.serialize(self, obj)
Call pyarrow.serialize and pass this SerializationContext.
SerializationContext._deserialize_callback(self, serialized_obj) SerializationContext._serialize_callback(self, obj) SerializationContext.register_type(self, type_, type_id, pickle=False, custom_serializer=None, custom_deserializer=None)
EXPERIMENTAL: Add type to the list of types we can serialize.
Parameters
----------
type\_ : type
The type that we can serialize.
type_id : string
A string used to identify the type.
pickle : bool
True if the serialization should be done with pickle.
False if it should be done efficiently with Arrow.
custom_serializer : callable
This argument is optional, but can be provided to
serialize objects of the class in a particular way.
custom_deserializer : callable
This argument is optional, but can be provided to
deserialize objects of the class in a particular way.
SerializationContext.clone(self)
Return copy of this SerializationContext.
Returns
-------
clone : SerializationContext
SerializationContext.set_pickle(self, serializer, deserializer)
Set the serializer and deserializer to use for objects that are to be
pickled.
Parameters
----------
serializer : callable
The serializer to use (e.g., pickle.dumps or cloudpickle.dumps).
deserializer : callable
The deserializer to use (e.g., pickle.dumps or cloudpickle.dumps).
DeserializationCallbackError.__init__(self, message, type_id) SerializationCallbackError.__init__(self, message, example_object) is_named_tuple(cls)
Return True if cls is a namedtuple and False otherwise.
_deprecate_serialization(name) read_record_batch(obj, Schema schema, DictionaryMemo dictionary_memo=None)
Read RecordBatch from message, given a known schema. If reading data from a
complete IPC stream, use ipc.open_stream instead
Parameters
----------
obj : Message or Buffer-like
schema : Schema
dictionary_memo : DictionaryMemo, optional
If message contains dictionaries, must pass a populated
DictionaryMemo
Returns
-------
batch : RecordBatch
read_schema(obj, DictionaryMemo dictionary_memo=None)
Read Schema from message or buffer
Parameters
----------
obj : buffer or Message
dictionary_memo : DictionaryMemo, optional
Needed to be able to reconstruct dictionary-encoded fields
with read_record_batch
Returns
-------
schema : Schema
read_message(source)
Read length-prefixed message from file or buffer-like object
Parameters
----------
source : pyarrow.NativeFile, file-like object, or buffer-like object
Returns
-------
message : Message
read_tensor(source)
Read pyarrow.Tensor from pyarrow.NativeFile object from current
position. If the file source supports zero copy (e.g. a memory map), then
this operation does not allocate any memory. This function not assume that
the stream is aligned
Parameters
----------
source : pyarrow.NativeFile
Returns
-------
tensor : Tensor
write_tensor(Tensor tensor, NativeFile dest)
Write pyarrow.Tensor to pyarrow.NativeFile object its current position.
Parameters
----------
tensor : pyarrow.Tensor
dest : pyarrow.NativeFile
Returns
-------
bytes_written : int
Total number of bytes written to the file
get_record_batch_size(RecordBatch batch)
Return total size of serialized RecordBatch including metadata and padding.
Parameters
----------
batch : RecordBatch
The recordbatch for which we want to know the size.
get_tensor_size(Tensor tensor)
Return total size of serialized Tensor including metadata and padding.
Parameters
----------
tensor : Tensor
The tensor for which we want to known the size.
_RecordBatchFileReader.__setstate_cython__(self, __pyx_state) _RecordBatchFileReader.__reduce_cython__(self) _RecordBatchFileReader.__exit__(self, exc_type, exc_value, traceback) _RecordBatchFileReader.__enter__(self) _RecordBatchFileReader.read_all(self)
Read all record batches as a pyarrow.Table
_RecordBatchFileReader.get_batch(self, int i) _RecordBatchFileReader._open(self, source, footer_offset=None) _RecordBatchFileWriter.__setstate_cython__(self, __pyx_state) _RecordBatchFileWriter.__reduce_cython__(self) _RecordBatchFileWriter._open(self, sink, Schema schema, IpcWriteOptions options=IpcWriteOptions()) _RecordBatchStreamReader.__setstate_cython__(self, __pyx_state) _RecordBatchStreamReader.__reduce_cython__(self) _RecordBatchStreamReader._open(self, source) RecordBatchReader.__setstate_cython__(self, __pyx_state) RecordBatchReader.__reduce_cython__(self) RecordBatchReader.from_batches(schema, batches)
Create RecordBatchReader from an iterable of batches.
Parameters
----------
schema : Schema
The shared schema of the record batches
batches : Iterable[RecordBatch]
The batches that this reader will return.
Returns
-------
reader : RecordBatchReader
RecordBatchReader._import_from_c(uintptr_t in_ptr)
Import RecordBatchReader from a C ArrowArrayStream struct,
given its pointer.
Parameters
----------
in_ptr: int
The raw pointer to a C ArrowArrayStream struct.
This is a low-level function intended for expert users.
RecordBatchReader._export_to_c(self, uintptr_t out_ptr)
Export to a C ArrowArrayStream struct, given its pointer.
Parameters
----------
out_ptr: int
The raw pointer to a C ArrowArrayStream struct.
Be careful: if you don't pass the ArrowArrayStream struct to a
consumer, array memory will leak. This is a low-level function
intended for expert users.
RecordBatchReader.__exit__(self, exc_type, exc_val, exc_tb) RecordBatchReader.__enter__(self) RecordBatchReader.read_all(self)
Read all record batches as a pyarrow.Table.
RecordBatchReader.read_next_batch(self)
Read next RecordBatch from the stream.
Raises
------
StopIteration:
At end of stream.
RecordBatchReader.get_next_batch(self) _ReadPandasMixin.read_pandas(self, **options)
Read contents of stream to a pandas.DataFrame.
Read all record batches as a pyarrow.Table then convert it to a
pandas.DataFrame using Table.to_pandas.
Parameters
----------
**options : arguments to forward to Table.to_pandas
Returns
-------
df : pandas.DataFrame
_RecordBatchStreamWriter.__setstate_cython__(self, __pyx_state) _RecordBatchStreamWriter.__reduce_cython__(self) _RecordBatchStreamWriter._open(self, sink, Schema schema, IpcWriteOptions options=IpcWriteOptions()) _CRecordBatchWriter.__setstate_cython__(self, __pyx_state) _CRecordBatchWriter.__reduce_cython__(self) _CRecordBatchWriter.__exit__(self, exc_type, exc_val, exc_tb) _CRecordBatchWriter.__enter__(self) _CRecordBatchWriter.close(self)
Close stream and write end-of-stream 0 marker.
_CRecordBatchWriter.write_table(self, Table table, max_chunksize=None, **kwargs)
Write Table to stream in (contiguous) RecordBatch objects.
Parameters
----------
table : Table
max_chunksize : int, default None
Maximum size for RecordBatch chunks. Individual chunks may be
smaller depending on the chunk layout of individual columns.
_CRecordBatchWriter.write_batch(self, RecordBatch batch)
Write RecordBatch to stream.
Parameters
----------
batch : RecordBatch
_CRecordBatchWriter.write(self, table_or_batch)
Write RecordBatch or Table to stream.
Parameters
----------
table_or_batch : {RecordBatch, Table}
MessageReader.__setstate_cython__(self, __pyx_state) MessageReader.__reduce_cython__(self) MessageReader.read_next_message(self)
Read next Message from the stream.
Raises
------
StopIteration : at end of stream
MessageReader.open_stream(source)
Open stream from source.
Parameters
----------
source : a readable source, like an InputStream
Message.__setstate_cython__(self, __pyx_state) Message.__reduce_cython__(self) Message.serialize(self, alignment=8, memory_pool=None)
Write message as encapsulated IPC message
Parameters
----------
alignment : int, default 8
Byte alignment for metadata and body
memory_pool : MemoryPool, default None
Uses default memory pool if not specified
Returns
-------
serialized : Buffer
Message.serialize_to(self, NativeFile sink, alignment=8, memory_pool=None)
Write message to generic OutputStream
Parameters
----------
sink : NativeFile
alignment : int, default 8
Byte alignment for metadata and body
memory_pool : MemoryPool, default None
Uses default memory pool if not specified
Message.equals(self, Message other)
Returns True if the message contents (metadata and body) are identical
Parameters
----------
other : Message
Returns
-------
are_equal : bool
IpcWriteOptions.__setstate_cython__(self, __pyx_state) IpcWriteOptions.__reduce_cython__(self) output_stream(source, compression=u'detect', buffer_size=None)
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.
input_stream(source, compression=u'detect', buffer_size=None)
Create an Arrow input stream.
Parameters
----------
source : str, Path, buffer, 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.
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 : int64_t, default None
If not specified, will be computed if the codec is able to determine
the uncompressed buffer size.
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 : int64_t, default None
If not specified, will be computed if the codec is able to
determine the uncompressed buffer size.
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
_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 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 : 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
FixedSizeBufferWriter.__setstate_cython__(self, __pyx_state) FixedSizeBufferWriter.__reduce_cython__(self) FixedSizeBufferWriter.set_memcopy_threshold(self, int64_t threshold) FixedSizeBufferWriter.set_memcopy_blocksize(self, int64_t blocksize) FixedSizeBufferWriter.set_memcopy_threads(self, int num_threads) 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
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
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) PythonFile.readline(self, size=None) PythonFile.truncate(self, pos=None) NativeFile.__setstate_cython__(self, __pyx_state) NativeFile.__reduce_cython__(self) NativeFile.upload(self, stream, buffer_size=None)
Pipe file-like object to file
NativeFile.download(self, stream_or_path, buffer_size=None)
Read file completely to local path (rather than reading completely into
memory). First seeks to the beginning of the file.
NativeFile.writelines(self, lines) NativeFile.truncate(self)
NOT IMPLEMENTED
NativeFile.read_buffer(self, nbytes=None) 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".
NativeFile.readinto(self, b)
Read into the supplied buffer
Parameters
-----------
b: any python object supporting buffer interface
Returns
--------
number of bytes written
NativeFile.readall(self) NativeFile.read1(self, nbytes=None)
Read and return up to n bytes.
Alias for read, needed to match the IOBase interface. 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.read(self, nbytes=None)
Read indicated number of bytes from file, or read all remaining bytes
if no argument passed
Parameters
----------
nbytes : int, default None
Returns
-------
data : bytes
NativeFile.write(self, data)
Write byte from any object implementing buffer protocol (bytes,
bytearray, ndarray, pyarrow.Buffer)
Parameters
----------
data : bytes-like object or exporter of buffer protocol
Returns
-------
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
-------
new_position : 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.
SparseCSFTensor.__setstate_cython__(self, __pyx_state) SparseCSFTensor.__reduce_cython__(self) SparseCSFTensor.dim_name(self, i) SparseCSFTensor.equals(self, SparseCSFTensor other)
Return true if sparse tensors contains exactly equal data
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(type cls, obj, dim_names=None)
Convert numpy.ndarray to arrow::SparseCSFTensor
SparseCSCMatrix.__setstate_cython__(self, __pyx_state) SparseCSCMatrix.__reduce_cython__(self) SparseCSCMatrix.dim_name(self, i) SparseCSCMatrix.equals(self, SparseCSCMatrix other)
Return true if sparse tensors contains exactly equal data
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(type cls, obj, dim_names=None)
Convert numpy.ndarray to arrow::SparseCSCMatrix
SparseCSRMatrix.__setstate_cython__(self, __pyx_state) SparseCSRMatrix.__reduce_cython__(self) SparseCSRMatrix.dim_name(self, i) SparseCSRMatrix.equals(self, SparseCSRMatrix other)
Return true if sparse tensors contains exactly equal data.
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(type 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.
SparseCOOTensor.__setstate_cython__(self, __pyx_state) SparseCOOTensor.__reduce_cython__(self) SparseCOOTensor.dim_name(self, i) SparseCOOTensor.equals(self, SparseCOOTensor other)
Return true if sparse tensors contains exactly equal data.
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(type cls, obj, dim_names=None)
Convert numpy.ndarray to arrow::SparseCOOTensor
Tensor.__setstate_cython__(self, __pyx_state) Tensor.__reduce_cython__(self) Tensor.dim_name(self, i) Tensor.equals(self, Tensor other)
Return true if the tensors contains exactly equal data
Tensor.to_numpy(self)
Convert arrow::Tensor to numpy.ndarray with zero copy
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.
_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, bool promote=False, MemoryPool memory_pool=None)
Concatenate pyarrow.Table objects.
If promote==False, 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==True, 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``.
Parameters
----------
tables : iterable of pyarrow.Table objects
Pyarrow tables to concatenate into a single Table.
promote : bool, default False
If True, concatenate tables with null-filling and null type promotion.
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool.
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 : pandas.DataFrame, dict, list
A DataFrame, mapping of strings to Arrays or Python lists, or list of
arrays or chunked arrays.
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 (may use up to system CPU count threads)
For pandas.DataFrame inputs: if greater than 1, convert columns to
Arrow in parallel using indicated number of threads.
Returns
-------
Table
See Also
--------
Table.from_arrays, Table.from_pandas, Table.from_pydict
record_batch(data, names=None, schema=None, metadata=None)
Create a pyarrow.RecordBatch from another Python data structure or sequence
of arrays.
Parameters
----------
data : pandas.DataFrame, list
A DataFrame or list of arrays or chunked arrays.
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
_reconstruct_table(arrays, schema)
Internal: reconstruct pa.Table from pickled components.
Table.drop(self, columns)
Drop one or more columns and return a new table.
Parameters
----------
columns : list of str
List of field names referencing existing columns.
Raises
------
KeyError
If any of the passed columns name are not existing.
Returns
-------
Table
New table without the columns.
Table.rename_columns(self, names)
Create new table with columns renamed to provided names.
Parameters
----------
names : list of str
List of new column names.
Returns
-------
Table
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.
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.
Table.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, list of Array, or values coercible to arrays
Column data.
Returns
-------
Table
New table with the passed column added.
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.
Table.__sizeof__(self) Table.itercolumns(self)
Iterator over all columns in their numerical order.
Yields
------
ChunkedArray
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.column(self, i)
Select a column by its column name, or numeric index.
Parameters
----------
i : int or string
The index or name of the column to retrieve.
Returns
-------
ChunkedArray
Table._ensure_integer_index(self, i)
Ensure integer index (convert string column name to integer if needed).
Table.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
Table.to_pydict(self)
Convert the Table to a dict or OrderedDict.
Returns
-------
dict
Table._to_pandas(self, options, categories=None, ignore_metadata=False, types_mapper=None) Table.to_batches(self, max_chunksize=None, **kwargs)
Convert Table to list of (contiguous) RecordBatch objects.
Parameters
----------
max_chunksize : int, default None
Maximum size for RecordBatch chunks. Individual chunks may be
smaller depending on the chunk layout of individual columns.
Returns
-------
list of RecordBatch
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
Table.from_pydict(mapping, schema=None, metadata=None)
Construct a Table 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
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
Table.from_pandas(type 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 (may use up to system CPU count threads)
If greater than 1, convert columns to Arrow in parallel using
indicated number of 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 pandas as pd
>>> import pyarrow as pa
>>> df = pd.DataFrame({
... 'int': [1, 2],
... 'str': ['a', 'b']
... })
>>> pa.Table.from_pandas(df)
Table.cast(self, Schema target_schema, bool safe=True)
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.
Returns
-------
Table
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
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
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
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
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
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
Table.drop_null(self)
Remove missing values from a Table.
See :func:`pyarrow.compute.drop_null` for full usage.
Table.take(self, indices)
Select records from a Table. See :func:`pyarrow.compute.take` for full
usage.
Table.filter(self, mask, null_selection_behavior=u'drop')
Select records from a Table. See :func:`pyarrow.compute.filter` for
full usage.
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
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
-------
ChunkedArray (index/column) or Table (slice)
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.to_string(self, *, show_metadata=False, preview_cols=0)
Return human-readable string representation of Table.
Parameters
----------
show_metadata : bool, default True
Display Field-level and Schema-level KeyValueMetadata.
preview_cols : int, default 0
Display values of the columns for the first N columns.
Returns
-------
str
table_to_blocks(options, Table table, categories, extension_columns) _reconstruct_record_batch(columns, schema)
Internal: reconstruct RecordBatch from pickled components.
RecordBatch._import_from_c(uintptr_t 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, uintptr_t out_ptr, uintptr_t 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.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
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
RecordBatch.from_pandas(type 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 (may use up to system CPU count threads)
If greater than 1, convert columns to Arrow in parallel using
indicated number of threads
columns : list, optional
List of column to be converted. If None, use all columns.
Returns
-------
pyarrow.RecordBatch
RecordBatch._to_pandas(self, options, **kwargs) RecordBatch.to_pydict(self)
Convert the RecordBatch to a dict or OrderedDict.
Returns
-------
dict
RecordBatch.drop_null(self)
Remove missing values from a RecordBatch.
See pyarrow.compute.drop_null for full usage.
RecordBatch.take(self, indices)
Select records from a RecordBatch. See pyarrow.compute.take for full
usage.
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
RecordBatch.filter(self, mask, null_selection_behavior=u'drop')
Select record from a record batch. See pyarrow.compute.filter for full
usage.
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
RecordBatch.serialize(self, memory_pool=None)
Write RecordBatch to Buffer as encapsulated IPC message.
Parameters
----------
memory_pool : MemoryPool, default None
Uses default memory pool if not specified
Returns
-------
serialized : Buffer
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
-------
value : Array (index/column) or RecordBatch (slice)
RecordBatch.__sizeof__(self) 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.column(self, i)
Select single column from record batch
Parameters
----------
i : int or string
The index or name of the column to retrieve.
Returns
-------
column : pyarrow.Array
RecordBatch._ensure_integer_index(self, i)
Ensure integer index (convert string column name to integer if needed).
RecordBatch.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
-------
pyarrow.Field
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
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.to_string(self, show_metadata=False) RecordBatch.__reduce__(self) RecordBatch.from_pydict(mapping, schema=None, metadata=None)
Construct a 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
-------
RecordBatch
chunked_array(arrays, type=None)
Construct chunked array from list of array-like objects
Parameters
----------
arrays : Array, list of Array, or values coercible to arrays
Must all be the same data type. Can be empty only if type also passed.
type : DataType or string coercible to DataType
Returns
-------
ChunkedArray
ChunkedArray.to_pylist(self)
Convert to a list of native Python objects.
ChunkedArray.iterchunks(self) ChunkedArray.chunk(self, i)
Select a chunk by its index
Parameters
----------
i : int
Returns
-------
pyarrow.Array
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
ChunkedArray.drop_null(self)
Remove missing values from a chunked array.
See pyarrow.compute.drop_null for full description.
ChunkedArray.take(self, indices)
Select values from a chunked array. See pyarrow.compute.take for full
usage.
ChunkedArray.index(self, value, start=None, end=None, *, memory_pool=None)
Find the first index of a value.
See pyarrow.compute.index for full usage.
ChunkedArray.filter(self, mask, null_selection_behavior=u'drop')
Select values from a chunked array. See pyarrow.compute.filter for full
usage.
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
ChunkedArray.value_counts(self)
Compute counts of unique elements in array.
Returns
-------
An array of structs
ChunkedArray.unique(self)
Compute distinct elements in array
Returns
-------
pyarrow.Array
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
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[ChunkedArray]
ChunkedArray.dictionary_encode(self, null_encoding=u'mask')
Compute dictionary-encoded representation of array
Returns
-------
pyarrow.ChunkedArray
Same chunking as the input, all chunks share a common dictionary.
ChunkedArray.cast(self, target_type, safe=True)
Cast array values to another data type
See pyarrow.compute.cast for usage
ChunkedArray.__array__(self, dtype=None) ChunkedArray.to_numpy(self)
Return a NumPy copy of this array (experimental).
Returns
-------
array : numpy.ndarray
ChunkedArray._to_pandas(self, options, **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
ChunkedArray.fill_null(self, fill_value)
See pyarrow.compute.fill_null docstring for usage.
ChunkedArray.is_valid(self)
Return boolean array indicating the non-null values.
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
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.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) ChunkedArray.to_string(self, *, int indent=0, int window=10, 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 at the begin and end
of the array when the arrays is bigger than the window.
The other elements will be ellipsed.
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.
ChunkedArray.length(self) ChunkedArray.__reduce__(self) 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.
ExtensionArray.to_numpy(self, **kwargs)
Convert extension array to a numpy ndarray.
See Also
--------
Array.to_numpy
ExtensionArray._to_pandas(self, options, **kwargs) 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
StructArray.from_arrays(arrays, names=None, fields=None, mask=None, memory_pool=None)
Construct StructArray from collection of arrays representing
each field in the struct.
Either field names or field instances 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.
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.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.
from_pandas : bool, default False
If True, the indices should be treated as though they originated in
a pandas.Categorical (null encoded as -1).
ordered : bool, default False
Set to True if the category values are ordered.
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.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.
UnionArray.child(self, int pos) FixedSizeListArray.flatten(self)
Unnest this FixedSizeListArray by one level.
Returns
-------
result : Array
FixedSizeListArray.from_arrays(values, int32_t list_size)
Construct FixedSizeListArray from array of values and a list length.
Parameters
----------
values : Array (any type)
list_size : int
The fixed length of the lists.
Returns
-------
FixedSizeListArray
MapArray.from_arrays(offsets, keys, items, MemoryPool pool=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)
pool : MemoryPool
Returns
-------
map_array : MapArray
LargeListArray.from_arrays(offsets, values, MemoryPool pool=None)
Construct LargeListArray from arrays of int64 offsets and values.
Parameters
----------
offsets : Array (int64 type)
values : Array (any type)
pool : MemoryPool
Returns
-------
list_array : LargeListArray
ListArray.from_arrays(offsets, values, MemoryPool pool=None)
Construct ListArray from arrays of int32 offsets and values.
Parameters
----------
offsets : Array (int32 type)
values : Array (any type)
pool : MemoryPool
Returns
-------
list_array : ListArray
Examples
--------
>>> values = pa.array([1, 2, 3, 4])
>>> offsets = pa.array([0, 2, 4])
>>> pa.ListArray.from_arrays(offsets, values)
[
[
0,
1
],
[
2,
3
]
]
# nulls in the offsets array become null lists
>>> offsets = pa.array([0, None, 2, 4])
>>> pa.ListArray.from_arrays(offsets, values)
[
[
0,
1
],
null,
[
2,
3
]
]
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
--------
>>> 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
--------
>>> arr = pa.array([[1, 2, 3], [], None, [4]],
... type=pa.list_(pa.int32()))
>>> arr.value_parent_indices()
[
0,
0,
0,
3
]
BaseListArray.flatten(self)
Unnest this ListArray/LargeListArray by one level.
The returned Array is logically a concatenation of all the sub-lists
in this Array.
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.
Returns
-------
result : Array
MonthDayNanoIntervalArray.to_pylist(self)
Convert to a list of native Python objects.
pyarrow.MonthDayNano is used as the native representation.
Returns
-------
lst : list
Array._import_from_c(uintptr_t 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, uintptr_t out_ptr, uintptr_t 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.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 (experimental).
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.
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) Array._to_pandas(self, options, **kwargs) Array.index(self, value, start=None, end=None, *, memory_pool=None)
Find the first index of a value.
See pyarrow.compute.index for full usage.
Array.filter(self, Array mask, *, null_selection_behavior=u'drop')
Select values from an array. See pyarrow.compute.filter for full usage.
Array.drop_null(self)
Remove missing values from an array.
Array.take(self, indices)
Select values from an array. See pyarrow.compute.take for full usage.
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 : RecordBatch
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 pyarrow.compute.fill_null for usage.
Array.is_valid(self)
Return BooleanArray indicating the non-null values.
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) Array.format(self, **kwargs) Array.to_string(self, *, int indent=0, int window=10, bool skip_new_lines=False)
Render a "pretty-printed" string representation of the Array.
Parameters
----------
indent : int
How much to indent right the content of the array,
by default ``0``.
window : int
How many items to preview at the begin and end
of the array when the arrays is bigger than the window.
The other elements will be ellipsed.
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.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
-------
An array of structs
Array.dictionary_encode(self, null_encoding=u'mask')
Compute dictionary-encoded representation of array.
Array.unique(self)
Compute distinct elements in array.
Array.sum(self, **kwargs)
Sum the values in a numerical array.
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, safe=True)
Cast array values to another data type
See pyarrow.compute.cast for usage
Array.diff(self, Array other)
Compare contents of this array against another one.
Return string containing the result of arrow::Diff comparing contents
of this array against the other array.
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, types_mapper=None)
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 is not passed.
strings_to_categorical : bool, default False
Encode string (UTF8) and binary types to pandas.Categorical.
categories: list, default empty
List of fields that should be returned as pandas.Categorical. Only
applies to table-like data structures.
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[ns] dtype.
timestamp_as_object : bool, default False
Cast non-nanosecond timestamps (np.datetime64) to objects. This is
useful if you have timestamps that don't fit in the normal date
range of nanosecond timestamps (1678 CE-2262 CE).
If False, all timestamps are converted to datetime64[ns] dtype.
use_threads: bool, default True
Whether to parallelize the conversion using multiple threads.
deduplicate_objects : bool, default False
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.
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.
Returns
-------
pandas.Series or pandas.DataFrame depending on type of object
_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 is 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 is 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 or Series
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.
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
-----
Localized timestamps will currently be returned as UTC (pandas's native
representation). Timezone-naive data will be implicitly interpreted as
UTC.
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()))
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.
DictionaryScalar.as_py(self)
Return this encoded value as a Python object.
DictionaryScalar.__reduce__(self) DictionaryScalar._reconstruct(type cls, 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.
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) Scalar.cast(self, target_type)
Attempt a safe cast to 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
schema(fields, metadata=None)
Construct pyarrow.Schema from collection of fields.
Parameters
----------
fields : iterable of Fields or tuples, or mapping of strings to DataTypes
metadata : dict, default None
Keys and values must be coercible to bytes.
Examples
--------
>>> import pyarrow as pa
>>> pa.schema([
... ('some_int', pa.int32()),
... ('some_string', pa.string())
... ])
some_int: int32
some_string: string
>>> 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) -> DataType type_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
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
--------
>>> import pyarrow as pa
>>> fields = [
... ('f1', pa.int32()),
... ('f2', pa.string()),
... ]
>>> struct_type = pa.struct(fields)
>>> struct_type
StructType(struct)
>>> 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
map_(key_type, item_type, keys_sorted=False) -> MapType
Create MapType instance from key and item data types or fields.
Parameters
----------
key_type : DataType
item_type : DataType
keys_sorted : bool
Returns
-------
map_type : DataType
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
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
large_utf8()
Alias for large_string().
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().
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().
binary(int length=-1)
Create variable-length 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`.
utf8()
Alias for string().
string()
Create UTF8 variable-length string type.
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
float64()
Create double-precision floating point type.
float32()
Create single-precision floating point type.
float16()
Create half-precision floating point type.
date64()
Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01).
date32()
Create instance of 32-bit date (days since UNIX epoch 1970-01-01).
month_day_nano_interval()
Create instance of an interval type representing months, days and
nanoseconds between two dates.
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
--------
>>> import pyarrow as pa
>>> pa.duration('us')
DurationType(duration[us])
>>> pa.duration('s')
DurationType(duration[s])
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
--------
>>> 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])
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.
uint64()
Create instance of unsigned uint64 type.
int32()
Create instance of signed int32 type.
uint32()
Create instance of unsigned uint32 type.
int16()
Create instance of signed int16 type.
uint16()
Create instance of unsigned uint16 type.
int8()
Create instance of signed int8 type.
uint8()
Create instance of unsigned int8 type.
bool_()
Create instance of boolean type.
null()
Create instance of null type.
field(name, type, bool nullable=True, metadata=None)
Create a pyarrow.Field instance.
Parameters
----------
name : str or bytes
Name of the field.
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
unify_schemas(schemas)
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.
- 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.
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(uintptr_t 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, uintptr_t 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
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
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
Schema.add_metadata(self, metadata) Schema.set(self, int i, Field field)
Replace a field at position i in the schema.
Parameters
----------
i: int
field: Field
Returns
-------
schema: Schema
Schema.remove(self, int i)
Remove the field at index i from the schema.
Parameters
----------
i: int
Returns
-------
schema: Schema
Schema.insert(self, int i, Field field)
Add a field at position i to the schema.
Parameters
----------
i: int
field: Field
Returns
-------
schema: Schema
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.
Schema.get_all_field_indices(self, name)
Return sorted list of indices for fields with the given name
Schema.get_field_index(self, name)
Return index of field with given unique name. Returns -1 if not found
or if duplicated
Schema.field_by_name(self, name)
Access a field by its name rather than the column index.
Parameters
----------
name: str
Returns
-------
field: pyarrow.Field
Schema._field(self, int i)
Select a field by its numeric index. Schema.field(self, i)
Select a field by its column name or numeric index.
Parameters
----------
i : int or string
Returns
-------
pyarrow.Field
Schema.from_pandas(type 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']
... })
>>> pa.Schema.from_pandas(df)
int: int64
str: string
__index_level_0__: int64
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
Schema.empty_table(self)
Provide an empty table according to the schema.
Returns
-------
table: pyarrow.Table
Schema.__sizeof__(self) Schema.__reduce__(self) Field._import_from_c(uintptr_t 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, uintptr_t 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]
Field.with_nullable(self, nullable)
A copy of this field with the replaced nullability
Parameters
----------
nullable : bool
Returns
-------
field: pyarrow.Field
Field.with_name(self, name)
A copy of this field with the replaced name
Parameters
----------
name : str
Returns
-------
field : 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
Field.remove_metadata(self)
Create new field without metadata, if any
Returns
-------
field : pyarrow.Field
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
Field.add_metadata(self, metadata) 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
KeyValueMetadata.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) KeyValueMetadata.items(self) KeyValueMetadata.values(self) KeyValueMetadata.keys(self) KeyValueMetadata.value(self, i) KeyValueMetadata.key(self, i) KeyValueMetadata.__reduce__(self) KeyValueMetadata.equals(self, KeyValueMetadata other) unregister_extension_type(type_name)
Unregister a Python extension type.
Parameters
----------
type_name : str
The name of the ExtensionType subclass to unregister.
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.
UnknownExtensionType.__arrow_ext_serialize__(self) PyExtensionType.__arrow_ext_deserialize__(type cls, storage_type, serialized) PyExtensionType.__arrow_ext_serialize__(self) PyExtensionType.__reduce__(self) 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.__arrow_ext_deserialize__(type self, 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
Decimal256Type.__reduce__(self) Decimal128Type.__reduce__(self) FixedSizeBinaryType.__reduce__(self) TimestampType.__reduce__(self) TimestampType.to_pandas_dtype(self)
Return the equivalent NumPy / Pandas dtype.
UnionType.__reduce__(self)
Return a child field by its index.
Iterate over union members, in order.
Like num_fields().
StructType.__reduce__(self)
Return the struct field with the given index or name.
Iterate over struct fields, in order.
Like num_fields().
StructType.get_all_field_indices(self, name)
Return sorted list of indices for fields with the given name
StructType.get_field_index(self, name)
Return index of field with given unique name. Returns -1 if not found
or if duplicated
FixedSizeListType.__reduce__(self) MapType.__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(uintptr_t 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, uintptr_t 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.
DataType.equals(self, other)
Return true if type is equivalent to passed value.
Parameters
----------
other : DataType or string convertible to DataType
Returns
-------
is_equal : bool
DataType.__reduce__(self) _is_primitive(Type type) 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.
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.assert_frame_equal(self, *args, **kwargs) _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.
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.is_index(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.is_series(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.is_data_frame(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.is_sparse(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.is_extension_array_dtype(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.is_datetimetz(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.is_categorical(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ_PandasAPIShim.is_array_like(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.pandas_dtype(self, dtype) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ_PandasAPIShim.infer_dtype(self, obj) ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ _PandasAPIShim.data_frame(self, *args, **kwargs) _PandasAPIShim.series(self, *args, **kwargs) 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) tobytes(o) encode_file_path(path) _pc() 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.
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_ZL59__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_19to_pydata_sparseP7_objectS0_.cold _ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_17to_scipyP7_objectS0_ _ZL20__pyx_n_s_coo_matrix _ZL22__pyx_n_s_scipy_sparse _ZL30__pyx_n_s_has_canonical_format _ZL24__pyx_n_s_sum_duplicates _ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_17to_scipyP7_objectS0_.cold _ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_15to_numpyP7_objectS0_ _ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_15to_numpyP7_objectS0_.cold _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_15to_scipyP7_objectS0_ _ZL20__pyx_n_s_csc_matrix _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_15to_scipyP7_objectS0_.cold _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_13to_numpyP7_objectS0_ _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_13to_numpyP7_objectS0_.cold _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_15to_scipyP7_objectS0_ _ZL20__pyx_n_s_csr_matrix _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_15to_scipyP7_objectS0_.cold _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_13to_numpyP7_objectS0_ _ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_13to_numpyP7_objectS0_.cold _ZL40__pyx_pw_7pyarrow_3lib_6Tensor_7to_numpyP7_objectS0_ _ZL40__pyx_pw_7pyarrow_3lib_6Tensor_7to_numpyP7_objectS0_.cold _ZL44__pyx_pw_7pyarrow_3lib_5Array_76_export_to_cP7_objectS0_S0_ _ZZL44__pyx_pw_7pyarrow_3lib_5Array_76_export_to_cP7_objectS0_S0_E16__pyx_pyargnames _ZL44__pyx_pw_7pyarrow_3lib_5Array_76_export_to_cP7_objectS0_S0_.cold _ZL40__pyx_pw_7pyarrow_3lib_5Array_72validateP7_objectS0_S0_ _ZZL40__pyx_pw_7pyarrow_3lib_5Array_72validateP7_objectS0_S0_E16__pyx_pyargnames _ZL40__pyx_pw_7pyarrow_3lib_5Array_72validateP7_objectS0_S0_.cold _ZL43__pyx_pw_7pyarrow_3lib_5Array_3_debug_printP7_objectS0_ _ZL43__pyx_pw_7pyarrow_3lib_5Array_3_debug_printP7_objectS0_.cold _ZL45__pyx_pw_7pyarrow_3lib_6Schema_54_export_to_cP7_objectS0_ _ZL45__pyx_pw_7pyarrow_3lib_6Schema_54_export_to_cP7_objectS0_.cold _ZL44__pyx_pw_7pyarrow_3lib_5Field_31_export_to_cP7_objectS0_ _ZL44__pyx_pw_7pyarrow_3lib_5Field_31_export_to_cP7_objectS0_.cold _ZL42__pyx_f_7pyarrow_3lib_13ExtensionType_initP37__pyx_obj_7pyarrow_3lib_ExtensionTypeRKSt10shared_ptrIN5arrow8DataTypeEE.cold _ZL47__pyx_pw_7pyarrow_3lib_8DataType_19_export_to_cP7_objectS0_ _ZL47__pyx_pw_7pyarrow_3lib_8DataType_19_export_to_cP7_objectS0_.cold _ZL42__pyx_pw_7pyarrow_3lib_6Schema_52to_stringP7_objectS0_S0_ _ZZL42__pyx_pf_7pyarrow_3lib_6Schema_51to_stringP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectS2_S2_E18__pyx_dict_version _ZZL42__pyx_pf_7pyarrow_3lib_6Schema_51to_stringP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectS2_S2_E23__pyx_dict_cached_value _ZL27__pyx_n_s_truncate_metadata _ZZL42__pyx_pw_7pyarrow_3lib_6Schema_52to_stringP7_objectS0_S0_E16__pyx_pyargnames _ZL42__pyx_pw_7pyarrow_3lib_6Schema_52to_stringP7_objectS0_S0_.cold _ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_13to_stringP7_objectS0_S0_ _ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_12to_stringP36__pyx_obj_7pyarrow_3lib_ChunkedArrayiibE18__pyx_dict_version _ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_12to_stringP36__pyx_obj_7pyarrow_3lib_ChunkedArrayiibE23__pyx_dict_cached_value _ZZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_13to_stringP7_objectS0_S0_E16__pyx_pyargnames _ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_13to_stringP7_objectS0_S0_.cold _ZL41__pyx_pw_7pyarrow_3lib_5Array_32to_stringP7_objectS0_S0_ _ZZL41__pyx_pf_7pyarrow_3lib_5Array_31to_stringP29__pyx_obj_7pyarrow_3lib_ArrayiibE18__pyx_dict_version _ZZL41__pyx_pf_7pyarrow_3lib_5Array_31to_stringP29__pyx_obj_7pyarrow_3lib_ArrayiibE23__pyx_dict_cached_value _ZZL41__pyx_pw_7pyarrow_3lib_5Array_32to_stringP7_objectS0_S0_E16__pyx_pyargnames _ZL41__pyx_pw_7pyarrow_3lib_5Array_32to_stringP7_objectS0_S0_.cold _ZL36__pyx_pw_7pyarrow_3lib_233_serializeP7_objectS0_S0_ _ZZL36__pyx_pf_7pyarrow_3lib_232_serializeP7_objectS0_P44__pyx_obj_7pyarrow_3lib_SerializationContextE18__pyx_dict_version _ZZL36__pyx_pf_7pyarrow_3lib_232_serializeP7_objectS0_P44__pyx_obj_7pyarrow_3lib_SerializationContextE23__pyx_dict_cached_value _ZZL36__pyx_pw_7pyarrow_3lib_233_serializeP7_objectS0_S0_E16__pyx_pyargnames _ZL36__pyx_pw_7pyarrow_3lib_233_serializeP7_objectS0_S0_.cold _ZL47__pyx_pw_7pyarrow_3lib_213get_record_batch_sizeP7_objectS0_ _ZL47__pyx_pw_7pyarrow_3lib_213get_record_batch_sizeP7_objectS0_.cold _ZL41__pyx_pw_7pyarrow_3lib_211get_tensor_sizeP7_objectS0_ _ZL41__pyx_pw_7pyarrow_3lib_211get_tensor_sizeP7_objectS0_.cold _ZL45__pyx_pw_7pyarrow_3lib_185set_io_thread_countP7_objectS0_ _ZL45__pyx_pw_7pyarrow_3lib_185set_io_thread_countP7_objectS0_.cold _ZL50__pyx_pw_7pyarrow_3lib_41unregister_extension_typeP7_objectS0_ _ZZL50__pyx_pf_7pyarrow_3lib_40unregister_extension_typeP7_objectS0_E18__pyx_dict_version _ZZL50__pyx_pf_7pyarrow_3lib_40unregister_extension_typeP7_objectS0_E23__pyx_dict_cached_value _ZL50__pyx_pw_7pyarrow_3lib_41unregister_extension_typeP7_objectS0_.cold _ZL46__pyx_pw_7pyarrow_3lib_35jemalloc_set_decay_msP7_objectS0_ _ZL46__pyx_pw_7pyarrow_3lib_35jemalloc_set_decay_msP7_objectS0_.cold _ZL45__pyx_pw_7pyarrow_3lib_27mimalloc_memory_poolP7_objectS0_ _ZL45__pyx_pw_7pyarrow_3lib_27mimalloc_memory_poolP7_objectS0_.cold _ZL45__pyx_pw_7pyarrow_3lib_25jemalloc_memory_poolP7_objectS0_ _ZL45__pyx_pw_7pyarrow_3lib_25jemalloc_memory_poolP7_objectS0_.cold _ZL37__pyx_pw_7pyarrow_3lib_3set_cpu_countP7_objectS0_ _ZL37__pyx_pw_7pyarrow_3lib_3set_cpu_countP7_objectS0_.cold _ZL53__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_9from_tensorP7_objectS0_S0_ _ZZL53__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_9from_tensorP7_objectS0_S0_E16__pyx_pyargnames _ZL53__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_9from_tensorP7_objectS0_S0_.cold _ZL54__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_11from_tensorP7_objectS0_S0_ _ZZL54__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_11from_tensorP7_objectS0_S0_E16__pyx_pyargnames _ZL54__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_11from_tensorP7_objectS0_S0_.cold _ZL54__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_11from_tensorP7_objectS0_S0_ _ZZL54__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_11from_tensorP7_objectS0_S0_E16__pyx_pyargnames _ZL54__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_11from_tensorP7_objectS0_S0_.cold _ZL54__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_13from_tensorP7_objectS0_S0_ _ZZL54__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_13from_tensorP7_objectS0_S0_E16__pyx_pyargnames _ZL54__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_13from_tensorP7_objectS0_S0_.cold _ZL38__pyx_f_7pyarrow_3lib__ndarray_to_typeP7_objectP32__pyx_obj_7pyarrow_3lib_DataType _ZL38__pyx_f_7pyarrow_3lib__ndarray_to_typeP7_objectP32__pyx_obj_7pyarrow_3lib_DataType.cold _ZL39__pyx_f_7pyarrow_3lib__ndarray_to_arrayP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypebbPN5arrow10MemoryPoolE _ZL39__pyx_f_7pyarrow_3lib__ndarray_to_arrayP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypebbPN5arrow10MemoryPoolE.cold _ZL53__pyx_pw_7pyarrow_3lib_18BufferOutputStream_3getvalueP7_objectS0_ _ZL53__pyx_pw_7pyarrow_3lib_18BufferOutputStream_3getvalueP7_objectS0_.cold _ZL57__pyx_pw_7pyarrow_3lib_17RecordBatchReader_14_export_to_cP7_objectS0_ _ZL57__pyx_pw_7pyarrow_3lib_17RecordBatchReader_14_export_to_cP7_objectS0_.cold _ZL52__pyx_pw_7pyarrow_3lib_17RecordBatchReader_8read_allP7_objectS0_ _ZL52__pyx_pw_7pyarrow_3lib_17RecordBatchReader_8read_allP7_objectS0_.cold _ZL59__pyx_pw_7pyarrow_3lib_17RecordBatchReader_6read_next_batchP7_objectS0_ _ZL59__pyx_pw_7pyarrow_3lib_17RecordBatchReader_6read_next_batchP7_objectS0_.cold _ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_37flushP7_objectS0_ _ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_37flushP7_objectS0_.cold _ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_35seekP7_objectS0_S0_ _ZL36__pyx_kp_u_Invalid_value_of_whence_0 _ZL14__pyx_n_s_tell _ZL18__pyx_n_s_position _ZL16__pyx_n_s_whence _ZZL42__pyx_pw_7pyarrow_3lib_10NativeFile_35seekP7_objectS0_S0_E16__pyx_pyargnames _ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_35seekP7_objectS0_S0_.cold _ZL42__pyx_pw_7pyarrow_3lib_5Table_47to_batchesP7_objectS0_S0_ _ZL43__pyx_kp_u_The_parameter_chunksize_is_depre _ZZL42__pyx_pf_7pyarrow_3lib_5Table_46to_batchesP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E18__pyx_dict_version _ZZL42__pyx_pf_7pyarrow_3lib_5Table_46to_batchesP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E23__pyx_dict_cached_value _ZZL42__pyx_pw_7pyarrow_3lib_5Table_47to_batchesP7_objectS0_S0_E16__pyx_pyargnames _ZL42__pyx_pw_7pyarrow_3lib_5Table_47to_batchesP7_objectS0_S0_.cold _ZL48__pyx_pw_7pyarrow_3lib_13ExtensionType_3__init__P7_objectS0_S0_ _ZZL48__pyx_pf_7pyarrow_3lib_13ExtensionType_2__init__P37__pyx_obj_7pyarrow_3lib_ExtensionTypeP32__pyx_obj_7pyarrow_3lib_DataTypeP7_objectE18__pyx_dict_version 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_ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_40to_numpyP7_objectS0_ _ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_39to_numpyP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version _ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_39to_numpyP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value _ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_39to_numpyP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version_0 _ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_39to_numpyP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value_0 _ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_40to_numpyP7_objectS0_.cold _ZL40__pyx_pw_7pyarrow_3lib_5Array_66to_numpyP7_objectS0_S0_ _ZZL40__pyx_pf_7pyarrow_3lib_5Array_65to_numpyP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E18__pyx_dict_version _ZZL40__pyx_pf_7pyarrow_3lib_5Array_65to_numpyP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E23__pyx_dict_cached_value _ZL15__pyx_n_s_flags _ZL19__pyx_n_s_writeable _ZZL40__pyx_pw_7pyarrow_3lib_5Array_66to_numpyP7_objectS0_S0_E16__pyx_pyargnames _ZL18__pyx_n_s_writable _ZL40__pyx_pw_7pyarrow_3lib_5Array_66to_numpyP7_objectS0_S0_.cold _ZL39__pyx_tp_new_7pyarrow_3lib_BufferReaderP11_typeobjectP7_objectS2_ _ZZL47__pyx_pf_7pyarrow_3lib_12BufferReader___cinit__P36__pyx_obj_7pyarrow_3lib_BufferReaderP7_objectE18__pyx_dict_version _ZZL47__pyx_pf_7pyarrow_3lib_12BufferReader___cinit__P36__pyx_obj_7pyarrow_3lib_BufferReaderP7_objectE23__pyx_dict_cached_value _ZZL48__pyx_pw_7pyarrow_3lib_12BufferReader_1__cinit__P7_objectS0_S0_E16__pyx_pyargnames _ZL39__pyx_tp_new_7pyarrow_3lib_BufferReaderP11_typeobjectP7_objectS2_.cold _ZL53__pyx_pw_7pyarrow_3lib_14ExtensionArray_1from_storageP7_objectS0_S0_ _ZL42__pyx_kp_u_Incompatible_storage_type_0_for _ZZL53__pyx_pw_7pyarrow_3lib_14ExtensionArray_1from_storageP7_objectS0_S0_E16__pyx_pyargnames _ZL53__pyx_pw_7pyarrow_3lib_14ExtensionArray_1from_storageP7_objectS0_S0_.cold _ZL42__pyx_pw_7pyarrow_3lib_239_read_serializedP7_objectS0_S0_ _ZZL42__pyx_pw_7pyarrow_3lib_239_read_serializedP7_objectS0_S0_E16__pyx_pyargnames _ZL42__pyx_pw_7pyarrow_3lib_239_read_serializedP7_objectS0_S0_.cold _ZL38__pyx_pw_7pyarrow_3lib_215write_tensorP7_objectS0_S0_ _ZZL38__pyx_pw_7pyarrow_3lib_215write_tensorP7_objectS0_S0_E16__pyx_pyargnames _ZL38__pyx_pw_7pyarrow_3lib_215write_tensorP7_objectS0_S0_.cold _ZL45__pyx_pw_7pyarrow_3lib_7Message_7serialize_toP7_objectS0_S0_ _ZZL45__pyx_pw_7pyarrow_3lib_7Message_7serialize_toP7_objectS0_S0_E16__pyx_pyargnames _ZL45__pyx_pw_7pyarrow_3lib_7Message_7serialize_toP7_objectS0_S0_.cold _ZL40__pyx_pw_7pyarrow_3lib_197foreign_bufferP7_objectS0_S0_ _ZZL40__pyx_pw_7pyarrow_3lib_197foreign_bufferP7_objectS0_S0_E16__pyx_pyargnames _ZL40__pyx_pw_7pyarrow_3lib_197foreign_bufferP7_objectS0_S0_.cold _ZL35__pyx_pw_7pyarrow_3lib_195py_bufferP7_objectS0_ _ZL35__pyx_pw_7pyarrow_3lib_195py_bufferP7_objectS0_.cold 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