Signed integer type, compatible with C char.
Character code: 'b'. Canonical name: np.byte. Alias on this platform: np.int8: 8-bit signed integer (-128 to 127).
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.


TensorFlow variant of NumPy's isclose.
See the NumPy documentation for numpy.isclose.


TensorFlow variant of NumPy's iscomplex.
See the NumPy documentation for numpy.iscomplex.


TensorFlow variant of NumPy's iscomplexobj.
See the NumPy documentation for numpy.iscomplexobj.


TensorFlow variant of NumPy's isfinite.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.isfinite.


TensorFlow variant of NumPy's isinf.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.isinf.


TensorFlow variant of NumPy's isnan.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.isnan.


TensorFlow variant of NumPy's isneginf.
Unsupported arguments: out.
See the NumPy documentation for numpy.isneginf.


TensorFlow variant of NumPy's isposinf.
Unsupported arguments: out.
See the NumPy documentation for numpy.isposinf.


TensorFlow variant of NumPy's isreal.
See the NumPy documentation for numpy.isreal.


TensorFlow variant of NumPy's isrealobj.
See the NumPy documentation for numpy.isrealobj.


TensorFlow variant of NumPy's isscalar.
Unsupported arguments: element.
See the NumPy documentation for numpy.isscalar.


Returns True if first argument is a typecode lower/equal in type hierarchy.
arg1, arg2 : dtype_like dtype or string representing a typecode.
out : bool
issubsctype, issubclass_ numpy.core.numerictypes : Overview of numpy type hierarchy.


TensorFlow variant of NumPy's ix_.
See the NumPy documentation for numpy.ix_.


TensorFlow variant of NumPy's kron.
See the NumPy documentation for numpy.kron.


TensorFlow variant of NumPy's lcm.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.lcm.


TensorFlow variant of NumPy's less.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.less.


TensorFlow variant of NumPy's less_equal.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.less_equal.


TensorFlow variant of NumPy's linspace.
See the NumPy documentation for numpy.linspace.


TensorFlow variant of NumPy's log.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.log.


TensorFlow variant of NumPy's log10.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.log10.


TensorFlow variant of NumPy's log1p.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.log1p.


TensorFlow variant of NumPy's log2.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.log2.


TensorFlow variant of NumPy's logaddexp.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.logaddexp.


TensorFlow variant of NumPy's logaddexp2.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.logaddexp2.


TensorFlow variant of NumPy's logical_and.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.logical_and.


TensorFlow variant of NumPy's logical_not.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.logical_not.


TensorFlow variant of NumPy's logical_or.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.logical_or.


TensorFlow variant of NumPy's logical_xor.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.logical_xor.


TensorFlow variant of NumPy's logspace.
See the NumPy documentation for numpy.logspace.


TensorFlow variant of NumPy's matmul.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.matmul.


TensorFlow variant of NumPy's max.
Unsupported arguments: out, initial, where.
See the NumPy documentation for numpy.max.


TensorFlow variant of NumPy's maximum.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.maximum.


TensorFlow variant of NumPy's mean.
Unsupported arguments: out.
See the NumPy documentation for numpy.mean.


TensorFlow variant of NumPy's meshgrid.
Unsupported arguments: copy, sparse, indexing.
This currently requires copy=True and sparse=False.
See the NumPy documentation for numpy.meshgrid.


TensorFlow variant of NumPy's min.
Unsupported arguments: out, initial, where.
See the NumPy documentation for numpy.min.


TensorFlow variant of NumPy's minimum.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.minimum.


TensorFlow variant of NumPy's mod.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.mod.


TensorFlow variant of NumPy's moveaxis.
Raises ValueError if source, destination not in (-ndim(a), ndim(a)).
See the NumPy documentation for numpy.moveaxis.


TensorFlow variant of NumPy's multiply.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.multiply.


TensorFlow variant of NumPy's nanmean.
Unsupported arguments: out.
See the NumPy documentation for numpy.nanmean.


TensorFlow variant of NumPy's nanprod.
Unsupported arguments: out.
See the NumPy documentation for numpy.nanprod.


TensorFlow variant of NumPy's nansum.
Unsupported arguments: out.
See the NumPy documentation for numpy.nansum.


Equivalent of numpy.ndarray backed by TensorFlow tensors.
This does not support all features of NumPy ndarrays e.g. strides and memory order since, unlike NumPy, the backing storage is not a raw memory buffer.
or if there are any differences in behavior.
This has a few key differences from the Python buffer object used in NumPy arrays.
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TensorFlow variant of NumPy's clip.
Unsupported arguments: out, kwargs.
See the NumPy documentation for numpy.clip.
View source
View source
TensorFlow variant of NumPy's ravel.
Unsupported arguments: order.
See the NumPy documentation for numpy.ravel.
View source
View source
View source
TensorFlow variant of NumPy's transpose.
See the NumPy documentation for numpy.transpose.
View source
TensorFlow variant of NumPy's absolute.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.absolute.
View source
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Implementation of ndarray.getitem.
View source
View source
TensorFlow variant of NumPy's logical_not.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.logical_not.
View source
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TensorFlow variant of NumPy's negative.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.negative.


TensorFlow variant of NumPy's nextafter.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.nextafter.


TensorFlow variant of NumPy's nonzero.
See the NumPy documentation for numpy.nonzero.


TensorFlow variant of NumPy's not_equal.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.not_equal.


Any Python object.
Character code: 'O'.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Call self as a function.
Return key in self.
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return len(self).
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.


TensorFlow variant of NumPy's ones.
Unsupported arguments: order.
See the NumPy documentation for numpy.ones.


TensorFlow variant of NumPy's ones_like.
Unsupported arguments: order, subok, shape.
See the NumPy documentation for numpy.ones_like.


TensorFlow variant of NumPy's outer.
Unsupported arguments: out.
See the NumPy documentation for numpy.outer.


TensorFlow variant of NumPy's pad.
Only supports modes 'constant', 'reflect' and 'symmetric' currently.
See the NumPy documentation for numpy.pad.


TensorFlow variant of NumPy's polyval.
See the NumPy documentation for numpy.polyval.


TensorFlow variant of NumPy's positive.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.positive.


TensorFlow variant of NumPy's power.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.power.


TensorFlow variant of NumPy's prod.
Unsupported arguments: out, initial, where.
See the NumPy documentation for numpy.prod.


TensorFlow variant of NumPy's promote_types.
See the NumPy documentation for numpy.promote_types.


TensorFlow variant of NumPy's ptp.
Unsupported arguments: out.
See the NumPy documentation for numpy.ptp.


TensorFlow variant of NumPy's rad2deg.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.rad2deg.


Public API for tf.experimental.numpy.random namespace.
rand(...): TensorFlow variant of NumPy's random.rand.
randint(...): TensorFlow variant of NumPy's random.randint.
randn(...): TensorFlow variant of NumPy's random.randn.
random(...): TensorFlow variant of NumPy's random.random.
seed(...): TensorFlow variant of NumPy's random.seed.
uniform(...): TensorFlow variant of NumPy's random.uniform.


TensorFlow variant of NumPy's random.rand.
See the NumPy documentation for numpy.random.rand.


TensorFlow variant of NumPy's random.randint.
See the NumPy documentation for numpy.random.randint.


TensorFlow variant of NumPy's random.randn.
Returns samples from a normal distribution.
Uses tf.random_normal.
Args: *args: The shape of the output array.
Returns: An ndarray with shape args and dtype float64.
See the NumPy documentation for numpy.random.randn.


TensorFlow variant of NumPy's random.random.
See the NumPy documentation for numpy.random.random.


TensorFlow variant of NumPy's random.seed.
Sets the seed for the random number generator.
Uses tf.set_random_seed.
Args: s: an integer.
See the NumPy documentation for numpy.random.seed.


TensorFlow variant of NumPy's random.uniform.
See the NumPy documentation for numpy.random.uniform.


TensorFlow variant of NumPy's ravel.
Main aliases
tf.experimental.numpy.ndarray.ravel
Unsupported arguments: order.
See the NumPy documentation for numpy.ravel.


TensorFlow variant of NumPy's real.
See the NumPy documentation for numpy.real.


TensorFlow variant of NumPy's reciprocal.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.reciprocal.


TensorFlow variant of NumPy's remainder.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.remainder.


TensorFlow variant of NumPy's repeat.
See the NumPy documentation for numpy.repeat.


TensorFlow variant of NumPy's reshape.
order argument can only b 'C' or 'F'.
See the NumPy documentation for numpy.reshape.


TensorFlow variant of NumPy's result_type.
See the NumPy documentation for numpy.result_type.


TensorFlow variant of NumPy's roll.
See the NumPy documentation for numpy.roll.


TensorFlow variant of NumPy's rot90.
See the NumPy documentation for numpy.rot90.


TensorFlow variant of NumPy's round.
Unsupported arguments: out.
See the NumPy documentation for numpy.round.


TensorFlow variant of NumPy's select.
See the NumPy documentation for numpy.select.


TensorFlow variant of NumPy's shape.
See the NumPy documentation for numpy.shape.


TensorFlow variant of NumPy's sign.
See the NumPy documentation for numpy.sign.


TensorFlow variant of NumPy's signbit.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.signbit.


TensorFlow variant of NumPy's sin.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.sin.


TensorFlow variant of NumPy's sinc.
See the NumPy documentation for numpy.sinc.


TensorFlow variant of NumPy's sinh.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.sinh.


TensorFlow variant of NumPy's size.
Unsupported arguments: a.
See the NumPy documentation for numpy.size.


TensorFlow variant of NumPy's sort.
See the NumPy documentation for numpy.sort.


TensorFlow variant of NumPy's split.
See the NumPy documentation for numpy.split.


TensorFlow variant of NumPy's sqrt.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.sqrt.


TensorFlow variant of NumPy's square.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.square.


TensorFlow variant of NumPy's squeeze.
See the NumPy documentation for numpy.squeeze.


TensorFlow variant of NumPy's stack.
Unsupported arguments: out.
See the NumPy documentation for numpy.stack.


TensorFlow variant of NumPy's std.
Unsupported arguments: dtype, out, ddof.
See the NumPy documentation for numpy.std.


bytes(iterable_of_ints) -> bytes
bytes(string, encoding[, errors]) -> bytes bytes(bytes_or_buffer) -> immutable copy of bytes_or_buffer bytes(int) -> bytes object of size given by the parameter initialized with null bytes bytes() -> empty bytes object
Construct an immutable array of bytes from:
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.capitalize() -> copy of B
Return a copy of B with only its first character capitalized (ASCII) and the rest lower-cased.
B.center(width[, fillchar]) -> copy of B
Return B centered in a string of length width. Padding is done using the specified fill character (default is a space).
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.count(sub[, start[, end]]) -> int
Return the number of non-overlapping occurrences of subsection sub in bytes B[start:end]. Optional arguments start and end are interpreted as in slice notation.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Decode the bytes using the codec registered for encoding.
encoding The encoding with which to decode the bytes. errors The error handling scheme to use for the handling of decoding errors. The default is 'strict' meaning that decoding errors raise a UnicodeDecodeError. Other possible values are 'ignore' and 'replace' as well as any other name registered with codecs.register_error that can handle UnicodeDecodeErrors.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.endswith(suffix[, start[, end]]) -> bool
Return True if B ends with the specified suffix, False otherwise. With optional start, test B beginning at that position. With optional end, stop comparing B at that position. suffix can also be a tuple of bytes to try.
B.expandtabs(tabsize=8) -> copy of B
Return a copy of B where all tab characters are expanded using spaces. If tabsize is not given, a tab size of 8 characters is assumed.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.find(sub[, start[, end]]) -> int
Return the lowest index in B where subsection sub is found, such that sub is contained within B[start,end]. Optional arguments start and end are interpreted as in slice notation.
Return -1 on failure.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Create a bytes object from a string of hexadecimal numbers.
Spaces between two numbers are accepted. Example: bytes.fromhex('B9 01EF') -> b'\xb9\x01\xef'.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.hex() -> string
Create a string of hexadecimal numbers from a bytes object. Example: b'\xb9\x01\xef'.hex() -> 'b901ef'.
B.index(sub[, start[, end]]) -> int
Return the lowest index in B where subsection sub is found, such that sub is contained within B[start,end]. Optional arguments start and end are interpreted as in slice notation.
Raises ValueError when the subsection is not found.
B.isalnum() -> bool
Return True if all characters in B are alphanumeric and there is at least one character in B, False otherwise.
B.isalpha() -> bool
Return True if all characters in B are alphabetic and there is at least one character in B, False otherwise.
B.isascii() -> bool
Return True if B is empty or all characters in B are ASCII, False otherwise.
B.isdigit() -> bool
Return True if all characters in B are digits and there is at least one character in B, False otherwise.
B.islower() -> bool
Return True if all cased characters in B are lowercase and there is at least one cased character in B, False otherwise.
B.isspace() -> bool
Return True if all characters in B are whitespace and there is at least one character in B, False otherwise.
B.istitle() -> bool
Return True if B is a titlecased string and there is at least one character in B, i.e. uppercase characters may only follow uncased characters and lowercase characters only cased ones. Return False otherwise.
B.isupper() -> bool
Return True if all cased characters in B are uppercase and there is at least one cased character in B, False otherwise.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Concatenate any number of bytes objects.
The bytes whose method is called is inserted in between each pair.
The result is returned as a new bytes object.
Example: b'.'.join([b'ab', b'pq', b'rs']) -> b'ab.pq.rs'.
B.ljust(width[, fillchar]) -> copy of B
Return B left justified in a string of length width. Padding is done using the specified fill character (default is a space).
B.lower() -> copy of B
Return a copy of B with all ASCII characters converted to lowercase.
Strip leading bytes contained in the argument.
If the argument is omitted or None, strip leading ASCII whitespace.
Return a translation table useable for the bytes or bytearray translate method.
The returned table will be one where each byte in frm is mapped to the byte at the same position in to.
The bytes objects frm and to must be of the same length.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Partition the bytes into three parts using the given separator.
This will search for the separator sep in the bytes. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing the original bytes object and two empty bytes objects.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Return a copy with all occurrences of substring old replaced by new.
count Maximum number of occurrences to replace. -1 (the default value) means replace all occurrences.
If the optional argument count is given, only the first count occurrences are replaced.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.rfind(sub[, start[, end]]) -> int
Return the highest index in B where subsection sub is found, such that sub is contained within B[start,end]. Optional arguments start and end are interpreted as in slice notation.
Return -1 on failure.
B.rindex(sub[, start[, end]]) -> int
Return the highest index in B where subsection sub is found, such that sub is contained within B[start,end]. Optional arguments start and end are interpreted as in slice notation.
Raise ValueError when the subsection is not found.
B.rjust(width[, fillchar]) -> copy of B
Return B right justified in a string of length width. Padding is done using the specified fill character (default is a space)
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Partition the bytes into three parts using the given separator.
This will search for the separator sep in the bytes, starting at the end. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing two empty bytes objects and the original bytes object.
Return a list of the sections in the bytes, using sep as the delimiter.
sep The delimiter according which to split the bytes. None (the default value) means split on ASCII whitespace characters (space, tab, return, newline, formfeed, vertical tab). maxsplit Maximum number of splits to do. -1 (the default value) means no limit.
Splitting is done starting at the end of the bytes and working to the front.
Strip trailing bytes contained in the argument.
If the argument is omitted or None, strip trailing ASCII whitespace.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Return a list of the sections in the bytes, using sep as the delimiter.
sep The delimiter according which to split the bytes. None (the default value) means split on ASCII whitespace characters (space, tab, return, newline, formfeed, vertical tab). maxsplit Maximum number of splits to do. -1 (the default value) means no limit.
Return a list of the lines in the bytes, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends is given and true.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.startswith(prefix[, start[, end]]) -> bool
Return True if B starts with the specified prefix, False otherwise. With optional start, test B beginning at that position. With optional end, stop comparing B at that position. prefix can also be a tuple of bytes to try.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Strip leading and trailing bytes contained in the argument.
If the argument is omitted or None, strip leading and trailing ASCII whitespace.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.swapcase() -> copy of B
Return a copy of B with uppercase ASCII characters converted to lowercase ASCII and vice versa.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.title() -> copy of B
Return a titlecased version of B, i.e. ASCII words start with uppercase characters, all remaining cased characters have lowercase.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Return a copy with each character mapped by the given translation table.
table Translation table, which must be a bytes object of length 256.
All characters occurring in the optional argument delete are removed. The remaining characters are mapped through the given translation table.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.upper() -> copy of B
Return a copy of B with all ASCII characters converted to uppercase.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
B.zfill(width) -> copy of B
Pad a numeric string B with zeros on the left, to fill a field of the specified width. B is never truncated.
abs(self)
Return self+value.
Return self&value.
self != 0
Return key in self.
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Implement iter(self).
Return self<=value.
Return len(self).
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.


TensorFlow variant of NumPy's subtract.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.subtract.


TensorFlow variant of NumPy's sum.
Unsupported arguments: out, initial, where.
See the NumPy documentation for numpy.sum.


TensorFlow variant of NumPy's swapaxes.
See the NumPy documentation for numpy.swapaxes.


TensorFlow variant of NumPy's take.
out argument is not supported, and default mode is clip.
See the NumPy documentation for numpy.take.


TensorFlow variant of NumPy's take_along_axis.
See the NumPy documentation for numpy.take_along_axis.


TensorFlow variant of NumPy's tan.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.tan.


TensorFlow variant of NumPy's tanh.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.tanh.


TensorFlow variant of NumPy's tensordot.
See the NumPy documentation for numpy.tensordot.


TensorFlow variant of NumPy's tile.
See the NumPy documentation for numpy.tile.


TensorFlow variant of NumPy's trace.
Unsupported arguments: out.
See the NumPy documentation for numpy.trace.


TensorFlow variant of NumPy's transpose.
Main aliases
tf.experimental.numpy.ndarray.transpose
See the NumPy documentation for numpy.transpose.


TensorFlow variant of NumPy's tri.
See the NumPy documentation for numpy.tri.


TensorFlow variant of NumPy's tril.
See the NumPy documentation for numpy.tril.


TensorFlow variant of NumPy's triu.
See the NumPy documentation for numpy.triu.


TensorFlow variant of NumPy's true_divide.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.true_divide.


Unsigned integer type, compatible with C unsigned short.
Character code: 'H'. Canonical name: np.ushort. Alias on this platform: np.uint16: 16-bit unsigned integer (0 to 65535).
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.


Unsigned integer type, compatible with C unsigned int.
Character code: 'I'. Canonical name: np.uintc. Alias on this platform: np.uint32: 32-bit unsigned integer (0 to 4294967295).
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.