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

numpy.amax(a, axis=None, out=None, keepdims=False) [source] Return the maximum of an array or maximum along an axis. Parameters: a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of

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

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

numpy.polynomial.chebyshev.chebx = array([0, 1])

matrix.itemsize Length of one array element in bytes. Examples >>> x = np.array([1,2,3], dtype=np.float64) >>> x.itemsize 8 >>> x = np.array([1,2,3], dtype=np.complex128) >>> x.itemsize 16

vectorize.__call__(*args, **kwargs) [source] Return arrays with the results of pyfunc broadcast (vectorized) over args and kwargs not in excluded.

numpy.log2(x[, out]) = Base-2 logarithm of x. Parameters: x : array_like Input values. Returns: y : ndarray Base-2 logarithm of x. See also log, log10, log1p, emath.log2 Notes New in version 1.3.0. Logarithm is a multivalued function: for each x there is an infinite number of z such that 2**z = x. The convention is to return the z whose imaginary part lies in [-pi, pi]. For real-valued input data types, log2 always returns real output. For each value that cannot be expressed as

MaskedArray.__idiv__(other) [source] Divide self by other in-place.

ndarray.__imul__ x.__imul__(y) <==> x*=y