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:

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 or RandomState instance Control the shuffling for reproducible behavior. 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.

*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 or RandomState instance

Control the shuffling for reproducible behavior.

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.

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])