pyarrow.Tensor#

class pyarrow.Tensor#

Bases: _Weakrefable

A n-dimensional array a.k.a Tensor.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
<pyarrow.Tensor>
type: int32
shape: (2, 3)
strides: (12, 4)

__init__(*args, **kwargs)#

Methods

__init__(*args,**kwargs)
dim_name(self,i)	Returns the name of the i-th tensor dimension.
equals(self,Tensorother)	Return true if the tensors contains exactly equal data.
from_dlpack(x,/,*[,device,copy])	Construct a Tensor from an object implementing the DLPack protocol.
from_numpy(obj[,dim_names])	Create a Tensor from a numpy array.
to_numpy(self)	Convert arrow::Tensor to numpy.ndarray with zero copy

Attributes

dim_names	Names of this tensor dimensions.
is_contiguous	Is this tensor contiguous in memory.
is_mutable	Is this tensor mutable or immutable.
ndim	The dimension (n) of this tensor.
shape	The shape of this tensor.
size	The size of this tensor.
strides	Strides of this tensor.
type

dim_name(self, i)#

Returns the name of the i-th tensor dimension.

Parameters:

iint

The physical index of the tensor dimension.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.dim_name(0)
'dim1'
>>> tensor.dim_name(1)
'dim2'

dim_names#

Names of this tensor dimensions.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.dim_names
['dim1', 'dim2']

equals(self, Tensor other)#

Return true if the tensors contains exactly equal data.

Parameters:

otherTensor

The other tensor to compare for equality.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> y = np.array([[2, 2, 4], [4, 5, 10]], np.int32)
>>> tensor2 = pa.Tensor.from_numpy(y, dim_names=["a","b"])
>>> tensor.equals(tensor)
True
>>> tensor.equals(tensor2)
False

static from_dlpack(x, /, *, device=None, copy=None)#

Construct a Tensor from an object implementing the DLPack protocol.

Parameters:

xobject

The input object containing array data, following the DLPack protocol (has a __dlpack__ method).

devicetuple[enum.Enum, int], optional

Designates where the resulting Tensor should reside, in the format returned by Tensor.__dlpack_device__(). When None, the output Tensor occupies the same device as the source. Default: None.

copybool, optional

Controls duplication behavior. True mandates copying; False prohibits copying and raises BufferError if unavoidable; None duplicates only when necessary. Default: None.

Returns:

Tensor

A Tensor housing the data from the input object, potentially as a copy or view.

static from_numpy(obj, dim_names=None)#

Create a Tensor from a numpy array.

Parameters:

objnumpy.ndarray

The source numpy array

dim_nameslist, optional

Names of each dimension of the Tensor.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
<pyarrow.Tensor>
type: int32
shape: (2, 3)
strides: (12, 4)

is_contiguous#

Is this tensor contiguous in memory.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.is_contiguous
True

is_mutable#

Is this tensor mutable or immutable.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.is_mutable
True

ndim#

The dimension (n) of this tensor.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.ndim
2

shape#

The shape of this tensor.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.shape
(2, 3)

size#

The size of this tensor.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.size
6

strides#

Strides of this tensor.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.strides
(12, 4)

to_numpy(self)#

Convert arrow::Tensor to numpy.ndarray with zero copy

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
>>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
>>> tensor.to_numpy()
array([[  2,   2,   4],
       [  4,   5, 100]], dtype=int32)

type#