dtype.byteorder A character indicating the byte-order of this data-type object. One of: ?=? native ?<? little-endian ?>? big-endian ?|? not applicable All built-in data-type objects have byteorder either ?=? or ?|?. Examples >>> dt = np.dtype('i2') >>> dt.byteorder '=' >>> # endian is not relevant for 8 bit numbers >>> np.dtype('i1').byteorder '|' >>> # or ASCII strings >>> np.dtype('S2').byteorder '|' >>> # Even if specif

numpy.add(x1, x2[, out]) = Add arguments element-wise. Parameters: x1, x2 : array_like The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: add : ndarray or scalar The sum of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars. Notes Equivalent to x1 + x2 in terms of array broadcasting. Examples >>> np.add(1.0, 4.0) 5.0 >>> x1 = np.arange(9.0).reshap

recarray.transpose(*axes) Returns a view of the array with axes transposed. For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). Par

numpy.seterr(all=None, divide=None, over=None, under=None, invalid=None) [source] Set how floating-point errors are handled. Note that operations on integer scalar types (such as int16) are handled like floating point, and are affected by these settings. Parameters: all : {?ignore?, ?warn?, ?raise?, ?call?, ?print?, ?log?}, optional Set treatment for all types of floating-point errors at once: ignore: Take no action when the exception occurs. warn: Print a RuntimeWarning (via the Python w

numpy.polynomial.hermite.hermdiv(c1, c2) [source] Divide one Hermite series by another. Returns the quotient-with-remainder of two Hermite series c1 / c2. The arguments are sequences of coefficients from lowest order ?term? to highest, e.g., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: [quo, rem] : ndarrays Of Hermite series coefficients representing the quotient and rema

numpy.flipud(m) [source] Flip array in the up/down direction. Flip the entries in each column in the up/down direction. Rows are preserved, but appear in a different order than before. Parameters: m : array_like Input array. Returns: out : array_like A view of m with the rows reversed. Since a view is returned, this operation is . See also fliplr Flip array in the left/right direction. rot90 Rotate array counterclockwise. Notes Equivalent to A[::-1,...]. Does not require the

MaskedArray.copy(order='C') [source] Return a copy of the array. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout of the copy. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous, ?C? otherwise. ?K? means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.) See also numpy.copy, numpy.copyto Examples >>>

numpy.issubsctype(arg1, arg2) [source] Determine if the first argument is a subclass of the second argument. Parameters: arg1, arg2 : dtype or dtype specifier Data-types. Returns: out : bool The result. See also issctype, issubdtype, obj2sctype Examples >>> np.issubsctype('S8', str) True >>> np.issubsctype(np.array([1]), np.int) True >>> np.issubsctype(np.array([1]), np.float) False

numpy.compress(condition, a, axis=None, out=None) [source] Return selected slices of an array along given axis. When working along a given axis, a slice along that axis is returned in output for each index where condition evaluates to True. When working on a 1-D array, compress is equivalent to extract. Parameters: condition : 1-D array of bools Array that selects which entries to return. If len(condition) is less than the size of a along the given axis, then output is truncated to the le

numpy.divide(x1, x2[, out]) = Divide arguments element-wise. Parameters: x1 : array_like Dividend array. x2 : array_like Divisor array. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs. Returns: y : ndarray or scalar The quotient x1/x2, element-wise. Returns a scalar if both x1 and x2 are scalars. See also seterr Set whether to raise or warn on overflow, underflow and di