`sklearn.utils`.shuffle¶

sklearn.utils.shuffle(*arrays, **options)[source]¶

Shuffle arrays or sparse matrices in a consistent way

This is a convenience alias to resample(*arrays, replace=False) to do random permutations of the collections.

Parameters:

*arrays : sequence of indexable data-structures

Indexable data-structures can be arrays, lists, dataframes or scipy sparse matrices with consistent first dimension.

random_state : int, RandomState instance or None, optional (default=None)

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

n_samples : int, None by default

Number of samples to generate. If left to None this is automatically set to the first dimension of the arrays.

Returns:

shuffled_arrays : sequence of indexable data-structures

Sequence of shuffled views of the collections. The original arrays are not impacted.

See also

sklearn.utils.resample

Examples

It is possible to mix sparse and dense arrays in the same run:

>>> X = np.array([[1., 0.], [2., 1.], [0., 0.]])
>>> y = np.array([0, 1, 2])

>>> from scipy.sparse import coo_matrix
>>> X_sparse = coo_matrix(X)

>>> from sklearn.utils import shuffle
>>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
>>> X
array([[ 0.,  0.],
       [ 2.,  1.],
       [ 1.,  0.]])

>>> X_sparse                   
<3x2 sparse matrix of type '<... 'numpy.float64'>'
    with 3 stored elements in Compressed Sparse Row format>

>>> X_sparse.toarray()
array([[ 0.,  0.],
       [ 2.,  1.],
       [ 1.,  0.]])

>>> y
array([2, 1, 0])

>>> shuffle(y, n_samples=2, random_state=0)
array([0, 1])