RandomState.shuffle(x) Modify a sequence in-place by shuffling its contents. Parameters: x : array_like The array or list to be shuffled. Returns: None Examples >>> arr = np.arange(10) >>> np.random.shuffle(arr) >>> arr [1 7 5 2 9 4 3 6 0 8] This function only shuffles the array along the first index of a multi-dimensional array: >>> arr = np.arange(9).reshape((3, 3)) >>> np.random.shuffle(arr) >>> arr array([[3, 4, 5], [6,

ndarray.ravel([order]) Return a flattened array. Refer to numpy.ravel for full documentation. See also numpy.ravel equivalent function ndarray.flat a flat iterator on the array.

recarray.mean(axis=None, dtype=None, out=None, keepdims=False) Returns the average of the array elements along given axis. Refer to numpy.mean for full documentation. See also numpy.mean equivalent function

MaskedArray.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.ma.mask_or(m1, m2, copy=False, shrink=True) [source] Combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False). Parameters: m1, m2 : array_like Input masks. copy : bool, optional If copy is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False. shrink : bool, optional Whether to shrink the output to nomask if all its values are False. Defaults to True. Returns: mask : outp

record.getfield()

numpy.isrealobj(x) [source] Return True if x is a not complex type or an array of complex numbers. The type of the input is checked, not the value. So even if the input has an imaginary part equal to zero, isrealobj evaluates to False if the data type is complex. Parameters: x : any The input can be of any type and shape. Returns: y : bool The return value, False if x is of a complex type. See also iscomplexobj, isreal Examples >>> np.isrealobj(1) True >>> np.isr

numpy.remainder(x1, x2[, out]) = Return element-wise remainder of division. Computes the remainder complementary to the floor_divide function. It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor x2. It should not be confused with the Matlab(TM) rem function. 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 rig

numpy.setdiff1d(ar1, ar2, assume_unique=False) [source] Find the set difference of two arrays. Return the sorted, unique values in ar1 that are not in ar2. Parameters: ar1 : array_like Input array. ar2 : array_like Input comparison array. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns: setdiff1d : ndarray Sorted 1D array of values in ar1 that are not in ar2. See also numpy.lib.arrayset

numpy.polynomial.chebyshev.chebadd(c1, c2) [source] Add one Chebyshev series to another. Returns the sum of two Chebyshev series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series T_0 + 2*T_1 + 3*T_2. Parameters: c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns: out : ndarray Array representing the Chebyshev series of their sum. See also chebsub, chebmul