record.put() 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

generic.cumsum() 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

MaskedArray.__rtruediv__(other) [source] Divide self into other, and return a new masked array.

numpy.round_(a, decimals=0, out=None) [source] Round an array to the given number of decimals. Refer to around for full documentation. See also around equivalent function

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

The ndarray in NumPy is a ?new-style? Python built-in-type. Therefore, it can be inherited from (in Python or in C) if desired. Therefore, it can form a foundation for many useful classes. Often whether to sub-class the array object or to simply use the core array component as an internal part of a new class is a difficult decision, and can be simply a matter of choice. NumPy has several tools for simplifying how your new object interacts with other array objects, and so the choice may not be

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

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

numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None) [source] Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. The endpoint of the interval can optionally be excluded. Parameters: start : scalar The starting value of the sequence. stop : scalar The end value of the sequence, unless endpoint is set to False. In that case, the sequence consists of all but the last of num + 1 even

ndarray.__and__ x.__and__(y) <==> x&y