numpy.issubdtype#

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

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

Note

This function relies on the scalar type hierarchy. That works in practice for NumPy dtypes and some user defined ones, but does not generalize. isdtype has slightly clearer semantics, although NumPy 2.6 or later is required for it to work with user defined dtypes in general.

Parameters:

arg1, arg2dtype_like: dtype or object coercible to one

Returns:

outbool

See also

isdtype: Similar function with clearer defined semantics.
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