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

numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None) [source] Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. The endpoint of the interval can optionally be excluded. Parameters: start : scalar The starting value of the sequence. stop : scalar The end value of the sequence, unless endpoint is set to False. In that case, the sequence consists of all but the last of num + 1 even

chararray.isnumeric() [source] For each element in self, return True if there are only numeric characters in the element. See also char.isnumeric

numpy.polynomial.polynomial.polyval2d(x, y, c) [source] Evaluate a 2-D polynomial at points (x, y). This function returns the value The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than two dimensions, ones are i

numpy.random.standard_cauchy(size=None) Draw samples from a standard Cauchy distribution with mode = 0. Also known as the Lorentz distribution. Parameters: 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 scalar The drawn samples. Notes The probability density function for the full Cauchy distribution is and the Stan

MaskedArray.__getitem__(indx) [source] x.__getitem__(y) <==> x[y] Return the item described by i, as a masked array.

static MaskedArray.__new__(data=None, mask=False, dtype=None, copy=False, subok=True, ndmin=0, fill_value=None, keep_mask=True, hard_mask=None, shrink=True, order=None, **options) [source] Create a new masked array from scratch. Notes A masked array can also be created by taking a .view(MaskedArray).

recarray.base Base object if memory is from some other object. Examples The base of an array that owns its memory is None: >>> x = np.array([1,2,3,4]) >>> x.base is None True Slicing creates a view, whose memory is shared with x: >>> y = x[2:] >>> y.base is x True

chararray.zfill(width) [source] Return the numeric string left-filled with zeros in a string of length width. See also char.zfill

class numpy.lib.Arrayterator(var, buf_size=None) [source] Buffered iterator for big arrays. Arrayterator creates a buffered iterator for reading big arrays in small contiguous blocks. The class is useful for objects stored in the file system. It allows iteration over the object without reading everything in memory; instead, small blocks are read and iterated over. Arrayterator can be used with any object that supports multidimensional slices. This includes NumPy arrays, but also variables f