pyarrow.SparseCOOTensor#

class pyarrow.SparseCOOTensor#

Bases: _Weakrefable

A sparse COO (COOrdinate) tensor.

COO format stores a sparse tensor as a collection of (indices, values) pairs. The indices specify the coordinates of non-zero elements, and the values contain the actual data at those coordinates.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> dense_tensor = np.array([[0, 1, 0], [2, 0, 3]], dtype=np.float32)
>>> sparse_coo = pa.SparseCOOTensor.from_dense_numpy(dense_tensor)
>>> sparse_coo
<pyarrow.SparseCOOTensor>
type: float
shape: (2, 3)

__init__(*args, **kwargs)#

Methods

__init__(*args,**kwargs)
dim_name(self,i)	Returns the name of the i-th tensor dimension.
equals(self,SparseCOOTensorother)	Return true if sparse tensors contains exactly equal data.
from_dense_numpy(cls,obj[,dim_names])	Convert numpy.ndarray to arrow::SparseCOOTensor
from_numpy(data,coords,shape[,dim_names])	Create arrow::SparseCOOTensor from numpy.ndarrays
from_pydata_sparse(obj[,dim_names])	Convert pydata/sparse.COO to arrow::SparseCOOTensor.
from_scipy(obj[,dim_names])	Convert scipy.sparse.coo_array or scipy.sparse.coo_matrix to arrow::SparseCOOTensor
from_tensor(obj)	Convert arrow::Tensor to arrow::SparseCOOTensor.
to_numpy(self)	Convert arrow::SparseCOOTensor to numpy.ndarrays with zero copy.
to_pydata_sparse(self)	Convert arrow::SparseCOOTensor to pydata/sparse.COO.
to_scipy(self)	Convert arrow::SparseCOOTensor to scipy.sparse.coo_array.
to_tensor(self)	Convert arrow::SparseCOOTensor to arrow::Tensor.

Attributes

dim_names
has_canonical_format
is_mutable
ndim
non_zero_length
shape
size
type

dim_name(self, i)#

Returns the name of the i-th tensor dimension.

Parameters:

iint

The physical index of the tensor dimension.

Returns:

str

dim_names#

equals(self, SparseCOOTensor other)#

Return true if sparse tensors contains exactly equal data.

Parameters:

otherSparseCOOTensor

The other tensor to compare for equality.

classmethod from_dense_numpy(cls, obj, dim_names=None)#

Convert numpy.ndarray to arrow::SparseCOOTensor

Parameters:

objnumpy.ndarray

Data used to populate the rows.

dim_nameslist[str], optional

Names of the dimensions.

Returns:

pyarrow.SparseCOOTensor

static from_numpy(data, coords, shape, dim_names=None)#

Create arrow::SparseCOOTensor from numpy.ndarrays

Parameters:

datanumpy.ndarray

Data used to populate the rows.

coordsnumpy.ndarray

Coordinates of the data.

shapetuple

Shape of the tensor.

dim_nameslist, optional

Names of the dimensions.

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> data = np.array([1.0, 2.0, 3.0], dtype=np.float32)
>>> coords = np.array([[0, 1], [1, 0], [1, 2]], dtype=np.int64)
>>> sparse_coo = pa.SparseCOOTensor.from_numpy(data, coords, shape=(2, 3))
>>> sparse_coo
<pyarrow.SparseCOOTensor>
type: float
shape: (2, 3)

static from_pydata_sparse(obj, dim_names=None)#

Convert pydata/sparse.COO to arrow::SparseCOOTensor.

Parameters:

objpydata.sparse.COO

The sparse multidimensional array that should be converted.

dim_nameslist, optional

Names of the dimensions.

static from_scipy(obj, dim_names=None)#

Convert scipy.sparse.coo_array or scipy.sparse.coo_matrix to arrow::SparseCOOTensor

Parameters:

objscipy.sparse.coo_array or scipy.sparse.coo_matrix

The scipy array or matrix that should be converted.

dim_nameslist, optional

Names of the dimensions.

static from_tensor(obj)#

Convert arrow::Tensor to arrow::SparseCOOTensor.

Parameters:

objTensor

The tensor that should be converted.

has_canonical_format#

is_mutable#

ndim#

non_zero_length#

shape#

size#

to_numpy(self)#

Convert arrow::SparseCOOTensor to numpy.ndarrays with zero copy.

to_pydata_sparse(self)#

Convert arrow::SparseCOOTensor to pydata/sparse.COO.

to_scipy(self)#

Convert arrow::SparseCOOTensor to scipy.sparse.coo_array.

to_tensor(self)#

Convert arrow::SparseCOOTensor to arrow::Tensor.

type#