dtype.isnative Boolean indicating whether the byte order of this dtype is native to the platform.

class numpy.polynomial.polynomial.Polynomial(coef, domain=None, window=None) [source] A power series class. The Polynomial class provides the standard Python numerical methods ?+?, ?-?, ?*?, ?//?, ?%?, ?divmod?, ?**?, and ?()? as well as the attributes and methods listed in the ABCPolyBase documentation. Parameters: coef : array_like Polynomial coefficients in order of increasing degree, i.e., (1, 2, 3) give 1 + 2*x + 3*x**2. domain : (2,) array_like, optional Domain to use. The interva

matrix.dump(file) Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load. Parameters: file : str A string naming the dump file.

generic.nbytes length of item in bytes

New in version 1.7.0. Starting in NumPy 1.7, there are core array data types which natively support datetime functionality. The data type is called ?datetime64?, so named because ?datetime? is already taken by the datetime library included in Python. Note The datetime API is experimental in 1.7.0, and may undergo changes in future versions of NumPy. Basic Datetimes The most basic way to create datetimes is from strings in ISO 8601 date or datetime format. The unit for internal storage is

dtype.__reduce__()

numpy.core.defchararray.title(a) [source] Return element-wise title cased version of string or unicode. Title case words start with uppercase characters, all remaining cased characters are lowercase. Calls str.title element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like, {str, unicode} Input array. Returns: out : ndarray Output array of str or unicode, depending on input type See also str.title Examples >>> c=np.array(['a1b c','1b ca'

There is a general need for looping over not only functions on scalars but also over functions on vectors (or arrays). This concept is realized in Numpy by generalizing the universal functions (ufuncs). In regular ufuncs, the elementary function is limited to element-by-element operations, whereas the generalized version (gufuncs) supports ?sub-array? by ?sub-array? operations. The Perl vector library PDL provides a similar functionality and its terms are re-used in the following. Each general

record.resize() 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.__mul__ x.__mul__(y) <==> x*y