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, SparseCOOTensor other)	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.

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#