sklearn.utils.shuffle

sklearn.utils.shuffle(*arrays, random_state=None, n_samples=None)[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
*arrayssequence of indexable data-structures

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

random_stateint or RandomState instance, default=None

Determines random number generation for shuffling the data. Pass an int for reproducible results across multiple function calls. See Glossary.

n_samplesint, default=None

Number of samples to generate. If left to None this is automatically set to the first dimension of the arrays. It should not be larger than the length of arrays.

Returns
shuffled_arrayssequence of indexable data-structures

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

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])