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scipy.sparse.csr_matrix
class .csr_matrix(arg1, shape=None, dtype=None, copy=False)[source]
Compressed Sparse Row matrix
This can be instantiated in several ways:
csr_matrix(D)
with a dense matrix or rank-2 ndarray D
csr_matrix(S)
with another sparse matrix S (equivalent to ())
csr_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
csr_matrix((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].
csr_matrix((data, indices, indptr), [shape=(M, N)])
is the standard CSR representation where the column indices for row i are stored in indices[indptr[i]:indptr[i+1]] and their corresponding values are stored in data[indptr[i]:indptr[i+1]]. If the shape parameter is not supplied, the matrix dimensions are inferred from the index arrays.
Notes
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSR format
- efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
- efficient row slicing
- fast matrix vector products
Disadvantages of the CSR format
- slow column slicing operations (consider CSC)
- changes to the sparsity structure are expensive (consider LIL or DOK)
Examples
>>>
>>> import numpy as np
>>> from import csr_matrix
>>> csr_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>>
>>> row = ([0, 0, 1, 2, 2, 2])
>>> col = ([0, 2, 2, 0, 1, 2])
>>> data = ([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
>>>
>>> indptr = ([0, 2, 3, 6])
>>> indices = ([0, 2, 2, 0, 1, 2])
>>> data = ([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
As an example of how to construct a CSR matrix incrementally, the following snippet builds a term-document matrix from texts:
>>>
>>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]]
>>> indptr = [0]
>>> indices = []
>>> data = []
>>> vocabulary = {}
>>> for d in docs:
... for term in d:
... index = (term, len(vocabulary))
... (index)
... (1)
... (len(indices))
...
>>> csr_matrix((data, indices, indptr), dtype=int).toarray()
array([[2, 1, 0, 0],
[0, 1, 1, 1]])
Attributes
| nnz | Number of stored values, including explicit zeros. |
| has_sorted_indices | Determine whether the matrix has sorted indices |
| dtype | (dtype) Data type of the matrix |
| shape | (2-tuple) Shape of the matrix |
| ndim | (int) Number of dimensions (this is always 2) |
| data | CSR format data array of the matrix |
| indices | CSR format index array of the matrix |
| indptr | CSR format index pointer array of the matrix |
Methods
| arcsin() | Element-wise arcsin. |
| arcsinh() | Element-wise arcsinh. |
| arctan() | Element-wise arctan. |
| arctanh() | Element-wise arctanh. |
| asformat(format) | Return this matrix in a given sparse format |
| asfptype() | Upcast matrix to a floating point format (if necessary) |
| astype(t) | |
| ceil() | Element-wise ceil. |
| check_format([full_check]) | check whether the matrix format is valid |
| conj() | |
| conjugate() | |
| copy() | |
| count_nonzero() | Number of non-zero entries, equivalent to |
| deg2rad() | Element-wise deg2rad. |
| diagonal() | Returns the main diagonal of the matrix |
| dot(other) | Ordinary dot product |
| eliminate_zeros() | Remove zero entries from the matrix |
| expm1() | Element-wise expm1. |
| floor() | Element-wise floor. |
| getH() | |
| get_shape() | |
| getcol(i) | Returns a copy of column i of the matrix, as a (m x 1) CSR matrix (column vector). |
| getformat() | |
| getmaxprint() | |
| getnnz([axis]) | Number of stored values, including explicit zeros. |
| getrow(i) | Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector). |
| log1p() | Element-wise log1p. |
| max([axis, out]) | Return the maximum of the matrix or maximum along an axis. |
| maximum(other) | |
| mean([axis, dtype, out]) | Compute the arithmetic mean along the specified axis. |
| min([axis, out]) | Return the minimum of the matrix or maximum along an axis. |
| minimum(other) | |
| multiply(other) | Point-wise multiplication by another matrix, vector, or scalar. |
| nonzero() | nonzero indices |
| power(n[, dtype]) | This function performs element-wise power. |
| prune() | Remove empty space after all non-zero elements. |
| rad2deg() | Element-wise rad2deg. |
| reshape(shape[, order]) | Gives a new shape to a sparse matrix without changing its data. |
| rint() | Element-wise rint. |
| set_shape(shape) | |
| setdiag(values[, k]) | Set diagonal or off-diagonal elements of the array. |
| sign() | Element-wise sign. |
| sin() | Element-wise sin. |
| sinh() | Element-wise sinh. |
| sort_indices() | Sort the indices of this matrix in place |
| sorted_indices() | Return a copy of this matrix with sorted indices |
| sqrt() | Element-wise sqrt. |
| sum([axis, dtype, out]) | Sum the matrix elements over a given axis. |
| sum_duplicates() | Eliminate duplicate matrix entries by adding them together |
| tan() | Element-wise tan. |
| tanh() | Element-wise tanh. |
| toarray([order, out]) | See the docstring for . |
| tobsr([blocksize, copy]) | Convert this matrix to Block Sparse Row format. |
| tocoo([copy]) | Convert this matrix to COOrdinate format. |
| tocsc([copy]) | |
| tocsr([copy]) | Convert this matrix to Compressed Sparse Row format. |
| todense([order, out]) | Return a dense matrix representation of this matrix. |
| todia([copy]) | Convert this matrix to sparse DIAgonal format. |
| todok([copy]) | Convert this matrix to Dictionary Of Keys format. |
| tolil([copy]) | Convert this matrix to LInked List format. |
| transpose([axes, copy]) | Reverses the dimensions of the sparse matrix. |
| trunc() | Element-wise trunc. |