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 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.

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])

Examples using sklearn.utils.shuffle

Model Complexity Influence

Prediction Latency

Color Quantization using K-Means

Empirical evaluation of the impact of k-means initialization

Gradient Boosting regression