matrix.itemset(*args) Insert scalar into an array (scalar is cast to array?s dtype, if possible) There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a. Parameters: *args : Arguments If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a sc

numpy.ma.masked_invalid(a, copy=True) [source] Mask an array where invalid values occur (NaNs or infs). This function is a shortcut to masked_where, with condition = ~(np.isfinite(a)). Any pre-existing mask is conserved. Only applies to arrays with a dtype where NaNs or infs make sense (i.e. floating point types), but accepts any array_like object. See also masked_where Mask where a condition is met. Examples >>> import numpy.ma as ma >>> a = np.arange(5, dtype=np.flo

numpy.linalg.matrix_power(M, n) [source] Raise a square matrix to the (integer) power n. For positive integers n, the power is computed by repeated matrix squarings and matrix multiplications. If n == 0, the identity matrix of the same shape as M is returned. If n < 0, the inverse is computed and then raised to the abs(n). Parameters: M : ndarray or matrix object Matrix to be ?powered.? Must be square, i.e. M.shape == (m, m), with m a positive integer. n : int The exponent can be any

ndarray.setflags(write=None, align=None, uic=None) Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable

numpy.ma.hsplit(ary, indices_or_sections) = Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the split documentation. hsplit is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension. See also split Split an array into multiple sub-arrays of equal size. Notes The function is applied to both the _data and the _mask, if any. Examples >>> x = np.arange(16.0).reshape(4, 4) >>>

recarray.resize(new_shape, refcheck=True) Change shape and size of array in-place. Parameters: new_shape : tuple of ints, or n ints Shape of resized array. refcheck : bool, optional If False, reference count will not be checked. Default is True. Returns: None Raises: ValueError If a does not own its own data or references or views to it exist, and the data memory must be changed. SystemError If the order keyword argument is specified. This behaviour is a bug in NumPy. See als

numpy.logical_or(x1, x2[, out]) = Compute the truth value of x1 OR x2 element-wise. Parameters: x1, x2 : array_like Logical OR is applied to the elements of x1 and x2. They have to be of the same shape. Returns: y : ndarray or bool Boolean result with the same shape as x1 and x2 of the logical OR operation on elements of x1 and x2. See also logical_and, logical_not, logical_xor, bitwise_or Examples >>> np.logical_or(True, False) True >>> np.logical_or([True, Fal

numpy.putmask(a, mask, values) Changes elements of an array based on conditional and input values. Sets a.flat[n] = values[n] for each n where mask.flat[n]==True. If values is not the same size as a and mask then it will repeat. This gives behavior different from a[mask] = values. Parameters: a : array_like Target array. mask : array_like Boolean mask array. It has to be the same shape as a. values : array_like Values to put into a where mask is True. If values is smaller than a it wi

classmethod Chebyshev.fromroots(roots, domain=[], window=None) [source] Return series instance that has the specified roots. Returns a series representing the product (x - r[0])*(x - r[1])*...*(x - r[n-1]), where r is a list of roots. Parameters: roots : array_like List of roots. domain : {[], None, array_like}, optional Domain for the resulting series. If None the domain is the interval from the smallest root to the largest. If [] the domain is the class domain. The default is []. win

classmethod Polynomial.basis(deg, domain=None, window=None) [source] Series basis polynomial of degree deg. Returns the series representing the basis polynomial of degree deg. New in version 1.7.0. Parameters: deg : int Degree of the basis polynomial for the series. Must be >= 0. domain : {None, array_like}, optional If given, the array must be of the form [beg, end], where beg and end are the endpoints of the domain. If None is given then the class domain is used. The default is N