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¶

pyarrow.interchange.from_dataframe

Compute Functions