numpy.issubdtype#

numpy.issubdtype(arg1, arg2)[source]#

Returns True if first argument is a typecode lower/equal in type hierarchy.

This is like the builtin issubclass, but for dtypes.

Parameters:

arg1, arg2dtype_like: dtype or object coercible to one

Returns:

outbool

See also

Scalars: Overview of the numpy type hierarchy.

Examples

issubdtype can be used to check the type of arrays:

>>> ints = np.array([1, 2, 3], dtype=np.int32)
>>> np.issubdtype(ints.dtype, np.integer)
True
>>> np.issubdtype(ints.dtype, np.floating)
False

>>> floats = np.array([1, 2, 3], dtype=np.float32)
>>> np.issubdtype(floats.dtype, np.integer)
False
>>> np.issubdtype(floats.dtype, np.floating)
True

Similar types of different sizes are not subdtypes of each other:

>>> np.issubdtype(np.float64, np.float32)
False
>>> np.issubdtype(np.float32, np.float64)
False

but both are subtypes of floating:

>>> np.issubdtype(np.float64, np.floating)
True
>>> np.issubdtype(np.float32, np.floating)
True

For convenience, dtype-like objects are allowed too:

>>> np.issubdtype('S1', np.bytes_)
True
>>> np.issubdtype('i4', np.signedinteger)
True