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# numpy.can_cast

`numpy.``can_cast`(from, totype, casting = 'safe')

  Returns True if cast between data types can occur according to the casting rule. If from is a scalar or array scalar, also returns True if the scalar value can be cast without overflow or truncation to an integer.

  Parameters: | from : dtype, dtype specifier, scalar, or array Data type, scalar, or array to cast from. totype : dtype or dtype specifier Data type to cast to. casting : {‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional Controls what kind of data casting may occur. ‘no’ means the data types should not be cast at all. ‘equiv’ means only byte-order changes are allowed. ‘safe’ means only casts which can preserve values are allowed. ‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed. ‘unsafe’ means any data conversions may be done.
  Returns: | out : bool True if cast can occur according to the casting rule.

  See also

  `dtype`, `result_type`

  Notes

  Starting in NumPy 1.9, can_cast function now returns False in ‘safe’ casting mode for integer/float dtype and string dtype if the string dtype length is not long enough to store the max integer/float value converted to a string. Previously can_cast in ‘safe’ mode returned True for integer/float dtype and a string dtype of any length.

  Examples

  Basic examples

      >>> np.can_cast(np.int32, np.int64)
      True
      >>> np.can_cast(np.float64, np.complex)
      True
      >>> np.can_cast(np.complex, np.float)
      False

      >>> np.can_cast('i8', 'f8')
      True
      >>> np.can_cast('i8', 'f4')
      False
      >>> np.can_cast('i4', 'S4')
      False

  Casting scalars

      >>> np.can_cast(100, 'i1')
      True
      >>> np.can_cast(150, 'i1')
      False
      >>> np.can_cast(150, 'u1')
      True

      >>> np.can_cast(3.5e100, np.float32)
      False
      >>> np.can_cast(1000.0, np.float32)
      True

  Array scalar checks the value, array does not

      >>> np.can_cast(np.array(1000.0), np.float32)
      True
      >>> np.can_cast(np.array([1000.0]), np.float32)
      False

  Using the casting rules

      >>> np.can_cast('i8', 'i8', 'no')
      True
      >>> np.can_cast('<i8', '>i8', 'no')
      False

      >>> np.can_cast('<i8', '>i8', 'equiv')
      True
      >>> np.can_cast('<i4', '>i8', 'equiv')
      False

      >>> np.can_cast('<i4', '>i8', 'safe')
      True
      >>> np.can_cast('<i8', '>i4', 'safe')
      False

      >>> np.can_cast('<i8', '>i4', 'same_kind')
      True
      >>> np.can_cast('<i8', '>u4', 'same_kind')
      False

      >>> np.can_cast('<i8', '>u4', 'unsafe')
      True