csc_array — SciPy v1.15.2 Manual (original) (raw)
scipy.sparse.
class scipy.sparse.csc_array(arg1, shape=None, dtype=None, copy=False, *, maxprint=None)[source]#
Compressed Sparse Column array.
This can be instantiated in several ways:
csc_array(D)
where D is a 2-D ndarray
csc_array(S)
with another sparse array or matrix S (equivalent to S.tocsc())
csc_array((M, N), [dtype])
to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype=’d’.
csc_array((data, (row_ind, col_ind)), [shape=(M, N)])
where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].
csc_array((data, indices, indptr), [shape=(M, N)])
is the standard CSC representation where the row indices for column i are stored in indices[indptr[i]:indptr[i+1]]and their corresponding values are stored indata[indptr[i]:indptr[i+1]]. If the shape parameter is not supplied, the array dimensions are inferred from the index arrays.
Notes
Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSC format
- efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
- efficient column slicing
- fast matrix vector products (CSR, BSR may be faster)
Disadvantages of the CSC format
- slow row slicing operations (consider CSR)
- changes to the sparsity structure are expensive (consider LIL or DOK)
Canonical format
- Within each column, indices are sorted by row.
- There are no duplicate entries.
Examples
import numpy as np from scipy.sparse import csc_array csc_array((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8)
row = np.array([0, 2, 2, 0, 1, 2]) col = np.array([0, 0, 1, 2, 2, 2]) data = np.array([1, 2, 3, 4, 5, 6]) csc_array((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 6]])
indptr = np.array([0, 2, 3, 6]) indices = np.array([0, 2, 2, 0, 1, 2]) data = np.array([1, 2, 3, 4, 5, 6]) csc_array((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 6]])
Attributes:
dtypedtype
Data type of the array
shape2-tuple
Shape of the array
ndimint
Number of dimensions (this is always 2)
Number of stored values, including explicit zeros.
Number of stored values.
data
CSC format data array of the array
indices
CSC format index array of the array
indptr
CSC format index pointer array of the array
Whether the indices are sorted
Whether the array/matrix has sorted indices and no duplicates
Transpose.
Methods
| __len__() | |
|---|---|
| arcsin() | Element-wise arcsin. |
| arcsinh() | Element-wise arcsinh. |
| arctan() | Element-wise arctan. |
| arctanh() | Element-wise arctanh. |
| argmax([axis, out, explicit]) | Return indices of maximum elements along an axis. |
| argmin([axis, out, explicit]) | Return indices of minimum elements along an axis. |
| asformat(format[, copy]) | Return this array/matrix in the passed format. |
| astype(dtype[, casting, copy]) | Cast the array/matrix elements to a specified type. |
| ceil() | Element-wise ceil. |
| check_format([full_check]) | Check whether the array/matrix respects the CSR or CSC format. |
| conj([copy]) | Element-wise complex conjugation. |
| conjugate([copy]) | Element-wise complex conjugation. |
| copy() | Returns a copy of this array/matrix. |
| count_nonzero([axis]) | Number of non-zero entries, equivalent to |
| deg2rad() | Element-wise deg2rad. |
| diagonal([k]) | Returns the kth diagonal of the array/matrix. |
| dot(other) | Ordinary dot product |
| eliminate_zeros() | Remove zero entries from the array/matrix |
| expm1() | Element-wise expm1. |
| floor() | Element-wise floor. |
| log1p() | Element-wise log1p. |
| max([axis, out, explicit]) | Return the maximum of the array/matrix or maximum along an axis. |
| maximum(other) | Element-wise maximum between this and another array/matrix. |
| mean([axis, dtype, out]) | Compute the arithmetic mean along the specified axis. |
| min([axis, out, explicit]) | Return the minimum of the array/matrix or maximum along an axis. |
| minimum(other) | Element-wise minimum between this and another array/matrix. |
| multiply(other) | Point-wise multiplication by array/matrix, vector, or scalar. |
| nanmax([axis, out, explicit]) | Return the maximum, ignoring any Nans, along an axis. |
| nanmin([axis, out, explicit]) | Return the minimum, ignoring any Nans, along an axis. |
| nonzero() | Nonzero indices of the array/matrix. |
| power(n[, dtype]) | This function performs element-wise power. |
| prune() | Remove empty space after all non-zero elements. |
| rad2deg() | Element-wise rad2deg. |
| reshape(self, shape[, order, copy]) | Gives a new shape to a sparse array/matrix without changing its data. |
| resize(*shape) | Resize the array/matrix in-place to dimensions given by shape |
| rint() | Element-wise rint. |
| setdiag(values[, k]) | Set diagonal or off-diagonal elements of the array/matrix. |
| sign() | Element-wise sign. |
| sin() | Element-wise sin. |
| sinh() | Element-wise sinh. |
| sort_indices() | Sort the indices of this array/matrix in place |
| sorted_indices() | Return a copy of this array/matrix with sorted indices |
| sqrt() | Element-wise sqrt. |
| sum([axis, dtype, out]) | Sum the array/matrix elements over a given axis. |
| sum_duplicates() | Eliminate duplicate entries by adding them together |
| tan() | Element-wise tan. |
| tanh() | Element-wise tanh. |
| toarray([order, out]) | Return a dense ndarray representation of this sparse array/matrix. |
| tobsr([blocksize, copy]) | Convert this array/matrix to Block Sparse Row format. |
| tocoo([copy]) | Convert this array/matrix to COOrdinate format. |
| tocsc([copy]) | Convert this array/matrix to Compressed Sparse Column format. |
| tocsr([copy]) | Convert this array/matrix to Compressed Sparse Row format. |
| todense([order, out]) | Return a dense representation of this sparse array. |
| todia([copy]) | Convert this array/matrix to sparse DIAgonal format. |
| todok([copy]) | Convert this array/matrix to Dictionary Of Keys format. |
| tolil([copy]) | Convert this array/matrix to List of Lists format. |
| trace([offset]) | Returns the sum along diagonals of the sparse array/matrix. |
| transpose([axes, copy]) | Reverses the dimensions of the sparse array/matrix. |
| trunc() | Element-wise trunc. |