pyarrow.SparseCSCMatrix#

class pyarrow.SparseCSCMatrix#

Bases: _Weakrefable

A sparse CSC (Compressed Sparse Column) matrix.

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

Examples

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

Return true if sparse tensors contains exactly equal data

Parameters:

otherSparseCSCMatrix: The other tensor to compare for equality.

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

Convert numpy.ndarray to arrow::SparseCSCMatrix

Parameters:

objnumpy.ndarray: Data used to populate the rows.
dim_nameslist[str], optional: Names of the dimensions.

Returns:

pyarrow.SparseCSCMatrix

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

Create arrow::SparseCSCMatrix 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.

static from_scipy(obj, dim_names=None)#

Convert scipy.sparse.csc_array or scipy.sparse.csc_matrix to arrow::SparseCSCMatrix

Parameters:

objscipy.sparse.csc_array or scipy.sparse.csc_matrix: The scipy matrix that should be converted.
dim_nameslist, optional: Names of the dimensions.

static from_tensor(obj)#

Convert arrow::Tensor to arrow::SparseCSCMatrix

Parameters:

objTensor: The dense tensor that should be converted.

is_mutable#

ndim#

non_zero_length#

shape#

size#

to_numpy(self)#: Convert arrow::SparseCSCMatrix to numpy.ndarrays with zero copy

to_scipy(self)#: Convert arrow::SparseCSCMatrix to scipy.sparse.csc_array

to_tensor(self)#: Convert arrow::SparseCSCMatrix to arrow::Tensor

type#