pyarrow.Tensor#

classpyarrow.Tensor#

Bases: _Weakrefable

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

Examples

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

staticfrom_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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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

>>> importpyarrowaspa
>>> importnumpyasnp
>>> 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#