numpy.core.defchararray.rpartition(a, sep) [source] Partition (split) each element around the right-most separator. Calls str.rpartition element-wise. For each element in a, split the element as the last occurrence of sep, and return 3 strings containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 strings containing the string itself, followed by two empty strings. Parameters: a : array_like of str or unicod

numpy.core.defchararray.multiply(a, i) [source] Return (a * i), that is string multiple concatenation, element-wise. Values in i of less than 0 are treated as 0 (which yields an empty string). Parameters: a : array_like of str or unicode i : array_like of ints Returns: out : ndarray Output array of str or unicode, depending on input types

recarray.dtype Data-type of the array?s elements. Parameters: None Returns: d : numpy dtype object See also numpy.dtype Examples >>> x array([[0, 1], [2, 3]]) >>> x.dtype dtype('int32') >>> type(x.dtype) <type 'numpy.dtype'>

numpy.polynomial.hermite_e.hermeval2d(x, y, c) [source] Evaluate a 2-D HermiteE series at points (x, y). This function returns the values: 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 is a 1-D array a one is implicitly

numpy.core.records.fromstring(datastring, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None) [source] create a (read-only) record array from binary data contained in a string

numpy.ma.clump_masked(a) [source] Returns a list of slices corresponding to the masked clumps of a 1-D array. (A ?clump? is defined as a contiguous region of the array). Parameters: a : ndarray A one-dimensional masked array. Returns: slices : list of slice The list of slices, one for each continuous region of masked elements in a. See also flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, notmasked_contiguous, clump_unmasked Notes New in version 1.4.0. Examples &g

numpy.column_stack(tup) [source] Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first. Parameters: tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension. Returns: stacked : 2-D array The array formed by stacking the given arrays. See also hstack, vstack, concatena

MaskedArray.filled(fill_value=None) [source] Return a copy of self, with masked values filled with a given value. However, if there are no masked values to fill, self will be returned instead as an ndarray. Parameters: fill_value : scalar, optional The value to use for invalid entries (None by default). If None, the fill_value attribute of the array is used instead. Returns: filled_array : ndarray A copy of self with invalid entries replaced by fill_value (be it the function argument

RandomState.weibull(a, size=None) Draw samples from a Weibull distribution. Draw samples from a 1-parameter Weibull distribution with the given shape parameter a. Here, U is drawn from the uniform distribution over (0,1]. The more common 2-parameter Weibull, including a scale parameter is just . Parameters: a : float Shape of the distribution. 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

Legendre.degree() [source] The degree of the series. New in version 1.5.0. Returns: degree : int Degree of the series, one less than the number of coefficients.