# 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:
i`int`

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:
other`Tensor`

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:
obj`numpy.ndarray`

The source numpy array

dim_names`list`, 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#