formats_map: dict
default_cache_size: int
caching_enabled: bool

__init__(self, a, shape=None, layout=None, dtype=None, initialize=True, cache_block_data=None, logger=None, init_kwargs=None): 

• a: Any

• shape: Any

• layout: Any

• dtype: Any

• initialize: Any

• cache_block_data: Any

  whether or not

• logger: Logger

  the logger to use for debug purposes

coo_to_cs(shape, vals, ij_inds, memmap=False, assume_sorted=False): 

Reimplementation of scipy’s internal “coo_tocsr” for memory-limited situations Assumes ij_inds are sorted by row then column, which allows vals to be used directly once indptr is computed

• :returns: _

to_state(self, serializer=None): 

Provides just the state that is needed to serialize the object

• serializer: Any

• :returns: _

from_state(state, serializer=None): 

initialize_empty(shape, dtype=None, layout=None, **kw): 

construct_sparse_from_val_inds(a, shape, fmt, cache_block_data=True, logger=None, assume_sorted=False): 

@property
dtype(self): 

@property
diag(self): 

from_diagonal_data(diags, shape=None, **kw): 

asarray(self): 

todense(self): 

ascoo(self): 

ascsr(self): 

ascsc(self): 

@property
data(self): 

format_from_string(fmt): 

@property
fmt(self): 

@property
shape(self): 

@property
ndim(self): 

@property
non_zero_count(self): 

get_caching_status(): 

enable_caches(): 

A method to be overloaded. Subclasses may want to cache things for performance, so we provide a way for them to turn this on

• :returns: _

disable_caches(): 

A method to be overloaded. Subclasses may want to cache things for performance, so we provide a way for them to turn this off

• :returns: _

clear_cache(): 

clear_ravel_caches(): 

set_ravel_cache_size(size): 

find(self): 

@property
block_vals(self): 

@property
block_inds(self): 

@property
block_data(self): 

transpose(self, transp): 

Transposes the array and returns a new one. Not necessarily a cheap operation.

• transp: Iterable[int]

  the transposition to do

• :returns: _

reshape_internal(self, shp): 

reshape(self, shp): 

Had to make this op not in-place because otherwise got scary errors…

• shp: Any

• :returns: _

pad_right(self, amounts): 

squeeze(self): 

resize(self, newsize): 

Returns a resized version of the tensor

• newsize: tuple[int]

• :returns: _

concatenate_coo(self, *others, axis=0): 

concatenate_2d(self, *others, axis=0): 

concatenate(self, *others, axis=0): 

Concatenates multiple arrays along the specified axis This is relatively inefficient in terms of not tracking indices throughout

• other: Any

• axis: Any

• :returns: _

broadcast_values(new_shape, old_shape, vals, inds): 

Implements broadcast_to using COO-style operations to be a little bit more efficient

• shape: Any

• :returns: _

broadcast_to(self, shape): 

Broadcasts to shape

• shape: Any

• :returns: _

expand_and_broadcast_to(self, expansion, new_shape): 

expand_and_pad(self, expansion, padding): 

@property
T(self): 

__matmul__(self, other): 

ascs(self, inplace=False): 

dot(self, b, reverse=False): 

outer(self, other): 

__neg__(self): 

__pos__(self): 

__add__(self, other): 

__iadd__(self, other): 

__radd__(self, other): 

plus(self, other, inplace=False): 

floopy_flop(self): 

__truediv__(self, other): 

__rtruediv__(self, other): 

__rmul__(self, other): 

__mul__(self, other): 

true_multiply(self, other): 

copy(self): 

savez(self, file, compressed=True): 

Saves a SparseArray to a file (must have the npz extension)

• file: Any

• compressed: Any

• :returns: str

  t h e

s a v e d

f i l e

loadz(file): 

Loads a SparseArray from an npz file

• file: Any

• :returns: SparseArray

__getitem__(self, item): 

__setitem__(self, item, val): 

__delitem__(self, item): 

__repr__(self):