chararray.encode(encoding=None, errors=None) [source] Calls str.encode element-wise. See also char.encode

numpy.real(val) [source] Return the real part of the elements of the array. Parameters: val : array_like Input array. Returns: out : ndarray Output array. If val is real, the type of val is used for the output. If val has complex elements, the returned type is float. See also real_if_close, imag, angle Examples >>> a = np.array([1+2j, 3+4j, 5+6j]) >>> a.real array([ 1., 3., 5.]) >>> a.real = 9 >>> a array([ 9.+2.j, 9.+4.j, 9.+6.j]) >>&

generic.choose() 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

chararray.replace(old, new, count=None) [source] For each element in self, return a copy of the string with all occurrences of substring old replaced by new. See also char.replace

numpy.all(a, axis=None, out=None, keepdims=False) [source] Test whether all array elements along a given axis evaluate to True. Parameters: a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical AND reduction is performed. The default (axis = None) is to perform a logical AND over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first

broadcast.next x.next() -> the next value, or raise StopIteration

record.byteswap() 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

ndarray.__setitem__ x.__setitem__(i, y) <==> x[i]=y

ndarray.__or__ x.__or__(y) <==> x|y

numpy.polynomial.polynomial.polyval2d(x, y, c) [source] Evaluate a 2-D polynomial at points (x, y). This function returns the value The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than two dimensions, ones are i