numpy.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) [source] Compute the variance along the specified axis. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. Parameters: a : array_like Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted. axis : None or int or tuple of ints, optional Axis or

numpy.polynomial.legendre.legvander2d(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 Legendre polynomials. If V = legvander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a

chararray.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) Copy of the array, cast to a specified type. Parameters: dtype : str or dtype Typecode or data-type to which the array is cast. order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout order of the result. ?C? means C order, ?F? means Fortran order, ?A? means ?F? order if all the arrays are Fortran contiguous, ?C? order otherwise, and ?K? means as close to the order the array elements appear in memory a

numpy.ma.hstack(tup) = Stack arrays in sequence horizontally (column wise). Take a sequence of arrays and stack them horizontally to make a single array. Rebuild arrays divided by hsplit. Parameters: tup : sequence of ndarrays All arrays must have the same shape along all but the second axis. Returns: stacked : ndarray The array formed by stacking the given arrays. See also stack Join a sequence of arrays along a new axis. vstack Stack arrays in sequence vertically (row wise

MaskedArray.__rmod__ x.__rmod__(y) <==> y%x

generic.itemsize length of one element in bytes

record.diagonal() 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.__getstate__() [source] Return the internal state of the masked array, for pickling purposes.

numpy.arange([start, ]stop, [step, ]dtype=None) Return evenly spaced values within a given interval. Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns an ndarray rather than a list. When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases.

numpy.random.lognormal(mean=0.0, sigma=1.0, size=None) Draw samples from a log-normal distribution. Draw samples from a log-normal distribution with specified mean, standard deviation, and array shape. Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from. Parameters: mean : float Mean value of the underlying normal distribution sigma : float, > 0. Standard deviation of the underlying nor