numpy.full_like(a, fill_value, dtype=None, order='K', subok=True) [source] Return a full array with the same shape and type as a given array. Parameters: a : array_like The shape and data-type of a define these same attributes of the returned array. fill_value : scalar Fill value. dtype : data-type, optional Overrides the data type of the result. order : {?C?, ?F?, ?A?, or ?K?}, optional Overrides the memory layout of the result. ?C? means C-order, ?F? means F-order, ?A? means ?F? i

recarray.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.])

record.ndim number of array dimensions

numpy.deg2rad(x[, out]) = Convert angles from degrees to radians. Parameters: x : array_like Angles in degrees. Returns: y : ndarray The corresponding angle in radians. See also rad2deg Convert angles from radians to degrees. unwrap Remove large jumps in angle by wrapping. Notes New in version 1.3.0. deg2rad(x) is x * pi / 180. Examples >>> np.deg2rad(180) 3.1415926535897931

numpy.ravel_multi_index(multi_index, dims, mode='raise', order='C') Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index. Parameters: multi_index : tuple of array_like A tuple of integer arrays, one array for each dimension. dims : tuple of ints The shape of array into which the indices from multi_index apply. mode : {?raise?, ?wrap?, ?clip?}, optional Specifies how out-of-bounds indices are handled. Can specify either one mode or

apply_along_axis(func1d, axis, arr, *args, ...) Apply a function to 1-D slices along the given axis. apply_over_axes(func, a, axes) Apply a function repeatedly over multiple axes. vectorize(pyfunc[, otypes, doc, excluded, cache]) Generalized function class. frompyfunc(func, nin, nout) Takes an arbitrary Python function and returns a Numpy ufunc. piecewise(x, condlist, funclist, *args, **kw) Evaluate a piecewise-defined function.

record.mean() 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

numpy.vstack(tup) [source] Stack arrays in sequence vertically (row wise). Take a sequence of arrays and stack them vertically to make a single array. Rebuild arrays divided by vsplit. Parameters: tup : sequence of ndarrays Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis. Returns: stacked : ndarray The array formed by stacking the given arrays. See also stack Join a sequence of arrays along a new axis. hstack Stack array

numpy.ma.soften_mask(self) = Force the mask to soft. Whether the mask of a masked array is hard or soft is determined by its hardmask property. soften_mask sets hardmask to False. See also hardmask

numpy.core.defchararray.istitle(a) [source] Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise. Call str.istitle element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like of str or unicode Returns: out : ndarray Output array of bools See also str.istitle