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scikit-learn 1.3.2
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# `sklearn.metrics.pairwise`.manhattan_distances

sklearn.metrics.pairwise.manhattan_distances(X, Y=None, *, sum_over_features='deprecated')

  Compute the L1 distances between the vectors in X and Y.

  With sum_over_features equal to False it returns the componentwise distances.

  Read more in the User Guide.

  Parameters:
    X{array-like, sparse matrix} of shape (n_samples_X, n_features)

      An array where each row is a sample and each column is a feature.

    Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None

      An array where each row is a sample and each column is a feature. If `None`, method uses `Y=X`.

    sum_over_features bool, default=True

      If True the function returns the pairwise distance matrix else it returns the componentwise L1 pairwise-distances. Not supported for sparse matrix inputs.

      Deprecated since version 1.2: `sum_over_features` was deprecated in version 1.2 and will be removed in 1.4.

  Returns:
    D ndarray of shape (n_samples_X * n_samples_Y, n_features) or (n_samples_X, n_samples_Y)

      If sum_over_features is False shape is (n_samples_X * n_samples_Y, n_features) and D contains the componentwise L1 pairwise-distances (ie. absolute difference), else shape is (n_samples_X, n_samples_Y) and D contains the pairwise L1 distances.

  Notes

  When X and/or Y are CSR sparse matrices and they are not already in canonical format, this function modifies them in-place to make them canonical.

  Examples

      >>> from sklearn.metrics.pairwise import manhattan_distances
      >>> manhattan_distances([[3]], [[3]])
      array([[0.]])
      >>> manhattan_distances([[3]], [[2]])
      array([[1.]])
      >>> manhattan_distances([[2]], [[3]])
      array([[1.]])
      >>> manhattan_distances([[1, 2], [3, 4]],         [[1, 2], [0, 3]])
      array([[0., 2.],
             [4., 4.]])