numpy.can_cast¶

numpy.can_cast(from_, to, 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:

Parameters:	from_ : dtype, dtype specifier, scalar, or array Data type, scalar, or array to cast from. to : 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.

from_ : dtype, dtype specifier, scalar, or array

Data type, scalar, or array to cast from.

to : 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, complex)
True
>>> np.can_cast(complex, 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