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, Tensor other)	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#