chararray.fill(value) Fill the array with a scalar value. Parameters: value : scalar All elements of a will be assigned this value. Examples >>> a = np.array([1, 2]) >>> a.fill(0) >>> a array([0, 0]) >>> a = np.empty(2) >>> a.fill(1) >>> a array([ 1., 1.])

generic.sort() 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.__mul__(other) [source] Multiply self by other, and return a new masked array.

numpy.irr(values) [source] Return the Internal Rate of Return (IRR). This is the ?average? periodically compounded rate of return that gives a net present value of 0.0; for a more complete explanation, see Notes below. Parameters: values : array_like, shape(N,) Input cash flows per time period. By convention, net ?deposits? are negative and net ?withdrawals? are positive. Thus, for example, at least the first element of values, which represents the initial investment, will typically be ne

MaskedArray.__pow__(other) [source] Raise self to the power other, masking the potential NaNs/Infs

ndindex.ndincr() [source] Increment the multi-dimensional index by one. This method is for backward compatibility only: do not use.

numpy.reshape(a, newshape, order='C') [source] Gives a new shape to an array without changing its data. Parameters: a : array_like Array to be reshaped. newshape : int or tuple of ints The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions. order : {?C?, ?F?, ?A?}, optional Read the elements of

recarray.conjugate() Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None) [source] Return numbers spaced evenly on a log scale. In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below). Parameters: start : float base ** start is the starting value of the sequence. stop : float base ** stop is the final value of the sequence, unless endpoint is False. In that case, num + 1 values are spaced over the interval in log

RandomState.logistic(loc=0.0, scale=1.0, size=None) Draw samples from a logistic distribution. Samples are drawn from a logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0). Parameters: loc : float scale : float > 0. 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. Default is None, in which case a single value is returned. Returns: samples : ndarray or scal