generic.nbytes length of item in bytes

Constants ma.MaskType alias of bool_ Creation From existing data ma.masked_array alias of MaskedArray ma.array(data[, dtype, copy, order, mask, ...]) An array class with possibly masked values. ma.copy(self, *args, **params) a.copy(order=) Return a copy of the array. ma.frombuffer(buffer[, dtype, count, offset]) Interpret a buffer as a 1-dimensional array. ma.fromfunction(function, shape, **kwargs) Construct an array by executing a function over each coordinate. ma.MaskedArray.copy(

MaskedArray.__iadd__(other) [source] Add other to self in-place.

RandomState.chisquare(df, size=None) Draw samples from a chi-square distribution. When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing. Parameters: df : int Number of degrees of freedom. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.

numpy.polynomial.hermite_e.hermevander2d(x, y, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the degrees of the HermiteE polynomials. If V = hermevander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements

numpy.polynomial.hermite.hermvander3d(x, y, z, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y, z). If l, m, n are the given degrees in x, y, z, then The pseudo-Vandermonde matrix is defined by where 0 <= i <= l, 0 <= j <= m, and 0 <= j <= n. The leading indices of V index the points (x, y, z) and the last index encodes the degrees of the Hermite polynomials. If V = hermvander3d(x, y,

numpy.choose(a, choices, out=None, mode='raise') [source] Construct an array from an index array and a set of arrays to choose from. First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = numpy.lib.index_tricks): np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)]). But this omits some subtleties. Here is a fully general summary: Given

Numpy binary files (NPY, NPZ) load(file[, mmap_mode, allow_pickle, ...]) Load arrays or pickled objects from .npy, .npz or pickled files. save(file, arr[, allow_pickle, fix_imports]) Save an array to a binary file in NumPy .npy format. savez(file, *args, **kwds) Save several arrays into a single file in uncompressed .npz format. savez_compressed(file, *args, **kwds) Save several arrays into a single file in compressed .npz format. The format of these binary file types is documented in h

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'

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