pyarrow.SparseCSRMatrix#

class pyarrow.SparseCSRMatrix#

Bases: _Weakrefable

A sparse CSR (Compressed Sparse Row) matrix.

CSR format stores a sparse matrix by compressing the row information. It uses three arrays: data (non-zero values), indices (column indices), and indptr (row pointers that indicate where each row starts in the data array).

Examples

>>> import pyarrow as pa
>>> import numpy as np
>>> dense_matrix = np.array([[1, 0, 2], [0, 0, 3]], dtype=np.float64)
>>> sparse_csr = pa.SparseCSRMatrix.from_dense_numpy(dense_matrix)
>>> sparse_csr
<pyarrow.SparseCSRMatrix>
type: double
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,SparseCSRMatrixother)	Return true if sparse tensors contains exactly equal data.
from_dense_numpy(cls,obj[,dim_names])	Convert numpy.ndarray to arrow::SparseCSRMatrix
from_numpy(data,indptr,indices,shape[,...])	Create arrow::SparseCSRMatrix from numpy.ndarrays.
from_scipy(obj[,dim_names])	Convert scipy.sparse.csr_array or scipy.sparse.csr_matrix to arrow::SparseCSRMatrix.
from_tensor(obj)	Convert arrow::Tensor to arrow::SparseCSRMatrix.
to_numpy(self)	Convert arrow::SparseCSRMatrix to numpy.ndarrays with zero copy.
to_scipy(self)	Convert arrow::SparseCSRMatrix to scipy.sparse.csr_array.
to_tensor(self)	Convert arrow::SparseCSRMatrix to arrow::Tensor.

Attributes

dim_names
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, SparseCSRMatrix other)#

Return true if sparse tensors contains exactly equal data.

Parameters:

otherSparseCSRMatrix

The other tensor to compare for equality.

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

Convert numpy.ndarray to arrow::SparseCSRMatrix

Parameters:

objnumpy.ndarray

The dense numpy array that should be converted.

dim_nameslist, optional

The names of the dimensions.

Returns:

pyarrow.SparseCSRMatrix

static from_numpy(data, indptr, indices, shape, dim_names=None)#

Create arrow::SparseCSRMatrix from numpy.ndarrays.

Parameters:

datanumpy.ndarray

Data used to populate the sparse matrix.

indptrnumpy.ndarray

Range of the rows, The i-th row spans from indptr[i] to indptr[i+1] in the data.

indicesnumpy.ndarray

Column indices of the corresponding non-zero values.

shapetuple

Shape of the matrix.

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.float64)
>>> indptr = np.array([0, 2, 3], dtype=np.int64)
>>> indices = np.array([0, 2, 1], dtype=np.int64)
>>> sparse_csr = pa.SparseCSRMatrix.from_numpy(
...     data, indptr, indices, shape=(2, 3))
>>> sparse_csr
<pyarrow.SparseCSRMatrix>
type: double
shape: (2, 3)

static from_scipy(obj, dim_names=None)#

Convert scipy.sparse.csr_array or scipy.sparse.csr_matrix to arrow::SparseCSRMatrix.

Parameters:

objscipy.sparse.csr_array or scipy.sparse.csr_matrix

The scipy matrix that should be converted.

dim_nameslist, optional

Names of the dimensions.

static from_tensor(obj)#

Convert arrow::Tensor to arrow::SparseCSRMatrix.

Parameters:

objTensor

The dense tensor that should be converted.

is_mutable#

ndim#

non_zero_length#

shape#

size#

to_numpy(self)#

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

to_scipy(self)#

Convert arrow::SparseCSRMatrix to scipy.sparse.csr_array.

to_tensor(self)#

Convert arrow::SparseCSRMatrix to arrow::Tensor.

type#