scipy.sparse.
load_npz#
- scipy.sparse.load_npz(file)[source]#
Load a sparse array/matrix from a file using
.npz
format.- Parameters:
- filestr or file-like object
Either the file name (string) or an open file (file-like object) where the data will be loaded.
- Returns:
- resultcsc_array, csr_array, bsr_array, dia_array or coo_array
A sparse array/matrix containing the loaded data.
- Raises:
- OSError
If the input file does not exist or cannot be read.
See also
scipy.sparse.save_npz
Save a sparse array/matrix to a file using
.npz
format.numpy.load
Load several arrays from a
.npz
archive.
Examples
Store sparse array/matrix to disk, and load it again:
>>> import numpy as np >>> import scipy as sp >>> sparse_array = sp.sparse.csc_array([[0, 0, 3], [4, 0, 0]]) >>> sparse_array <Compressed Sparse Column sparse array of dtype 'int64' with 2 stored elements and shape (2, 3)> >>> sparse_array.toarray() array([[0, 0, 3], [4, 0, 0]], dtype=int64)
>>> sp.sparse.save_npz('/tmp/sparse_array.npz', sparse_array) >>> sparse_array = sp.sparse.load_npz('/tmp/sparse_array.npz')
>>> sparse_array <Compressed Sparse Column sparse array of dtype 'int64' with 2 stored elements and shape (2, 3)> >>> sparse_array.toarray() array([[0, 0, 3], [4, 0, 0]], dtype=int64)
In this example we force the result to be csr_array from csr_matrix >>> sparse_matrix = sp.sparse.csc_matrix([[0, 0, 3], [4, 0, 0]]) >>> sp.sparse.save_npz(‘/tmp/sparse_matrix.npz’, sparse_matrix) >>> tmp = sp.sparse.load_npz(‘/tmp/sparse_matrix.npz’) >>> sparse_array = sp.sparse.csr_array(tmp)