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_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_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

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