tf.experimental.numpy.issubdtype
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Returns True if first argument is a typecode lower/equal in type hierarchy.
tf.experimental.numpy.issubdtype(
arg1, arg2
)
This is like the builtin :func:issubclass, but for dtype\ s.
Parameters
arg1, arg2 : dtype_like
dtype or object coercible to one
Returns
out : bool
See Also
:ref:arrays.scalars : Overview of the numpy type hierarchy.
issubsctype, issubclass_
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.string_)
True
np.issubdtype('i4', np.signedinteger)
True
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.experimental.numpy.issubdtype\n\n\u003cbr /\u003e\n\nReturns True if first argument is a typecode lower/equal in type hierarchy. \n\n tf.experimental.numpy.issubdtype(\n arg1, arg2\n )\n\nThis is like the builtin :func:`issubclass`, but for `dtype`\\\\ s.\n\nParameters\n----------\n\narg1, arg2 : dtype_like\n`dtype` or object coercible to one\n\nReturns\n-------\n\nout : bool\n\nSee Also\n--------\n\n:ref:`arrays.scalars` : Overview of the numpy type hierarchy.\nissubsctype, issubclass_\n\nExamples\n--------\n\n`issubdtype` can be used to check the type of arrays: \n\n ints = np.array([1, 2, 3], dtype=np.int32)\n np.issubdtype(ints.dtype, np.integer)\n True\n np.issubdtype(ints.dtype, np.floating)\n False\n\n floats = np.array([1, 2, 3], dtype=np.float32)\n np.issubdtype(floats.dtype, np.integer)\n False\n np.issubdtype(floats.dtype, np.floating)\n True\n\nSimilar types of different sizes are not subdtypes of each other: \n\n np.issubdtype(np.float64, np.float32)\n False\n np.issubdtype(np.float32, np.float64)\n False\n\nbut both are subtypes of `floating`: \n\n np.issubdtype(np.float64, np.floating)\n True\n np.issubdtype(np.float32, np.floating)\n True\n\nFor convenience, dtype-like objects are allowed too: \n\n np.issubdtype('S1', np.string_)\n True\n np.issubdtype('i4', np.signedinteger)\n True"]]
