Skip to main content
`Ctrl`+`K`
scipy.sparse.

# get_index_dtype

scipy.sparse.get_index_dtype(arrays=(), maxval=None, check_contents=False)

  Based on input (integer) arrays a, determine a suitable index data type that can hold the data in the arrays.

  Parameters:
    arraystuple of array_like

      Input arrays whose types/contents to check

    maxvalfloat, optional

      Maximum value needed

    check_contentsbool, optional

      Whether to check the values in the arrays and not just their types. Default: False (check only the types)

  Returns:
    dtypedtype

      Suitable index data type (int32 or int64)

  Examples

      >>> import numpy as np
      >>> from scipy import sparse
      >>> # select index dtype based on shape
      >>> shape = (3, 3)
      >>> idx_dtype = sparse.get_index_dtype(maxval=max(shape))
      >>> data = [1.1, 3.0, 1.5]
      >>> indices = np.array([0, 1, 0], dtype=idx_dtype)
      >>> indptr = np.array([0, 2, 3, 3], dtype=idx_dtype)
      >>> A = sparse.csr_array((data, indices, indptr), shape=shape)
      >>> A.indptr.dtype
      dtype('int32')

      >>> # select based on larger of existing arrays and shape
      >>> shape = (3, 3)
      >>> idx_dtype = sparse.get_index_dtype(A.indptr, maxval=max(shape))
      >>> idx_dtype
      <class 'numpy.int32'>