matrix.choose(choices, out=None, mode='raise') Use an index array to construct a new array from a set of choices. Refer to numpy.choose for full documentation. See also numpy.choose equivalent function

recarray.newbyteorder(new_order='S') Return the array with the same data viewed with a different byte order. Equivalent to: arr.view(arr.dtype.newbytorder(new_order)) Changes are also made in all fields and sub-arrays of the array data type. Parameters: new_order : string, optional Byte order to force; a value from the byte order specifications below. new_order codes can be any of: ?S? - swap dtype from current to opposite endian {?<?, ?L?} - little endian {?>?, ?B?} - big endian {

numpy.core.defchararray.translate(a, table, deletechars=None) [source] For each element in a, return a copy of the string where all characters occurring in the optional argument deletechars are removed, and the remaining characters have been mapped through the given translation table. Calls str.translate element-wise. Parameters: a : array-like of str or unicode table : str of length 256 deletechars : str Returns: out : ndarray Output array of str or unicode, depending on input type

dtype.fields Dictionary of named fields defined for this data type, or None. The dictionary is indexed by keys that are the names of the fields. Each entry in the dictionary is a tuple fully describing the field: (dtype, offset[, title]) If present, the optional title can be any object (if it is a string or unicode then it will also be a key in the fields dictionary, otherwise it?s meta-data). Notice also that the first two elements of the tuple can be passed directly as arguments to the n

numpy.union1d(ar1, ar2) [source] Find the union of two arrays. Return the unique, sorted array of values that are in either of the two input arrays. Parameters: ar1, ar2 : array_like Input arrays. They are flattened if they are not already 1D. Returns: union1d : ndarray Unique, sorted union of the input arrays. See also numpy.lib.arraysetops Module with a number of other functions for performing set operations on arrays. Examples >>> np.union1d([-1, 0, 1], [-2, 0, 2]) a

Chebyshev.convert(domain=None, kind=None, window=None) [source] Convert series to a different kind and/or domain and/or window. Parameters: domain : array_like, optional The domain of the converted series. If the value is None, the default domain of kind is used. kind : class, optional The polynomial series type class to which the current instance should be converted. If kind is None, then the class of the current instance is used. window : array_like, optional The window of the conve

matrix.sort(axis=-1, kind='quicksort', order=None) Sort an array, in-place. Parameters: axis : int, optional Axis along which to sort. Default is -1, which means sort along the last axis. kind : {?quicksort?, ?mergesort?, ?heapsort?}, optional Sorting algorithm. Default is ?quicksort?. order : str or list of str, optional When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all

numpy.square(x[, out]) = Return the element-wise square of the input. Parameters: x : array_like Input data. Returns: out : ndarray Element-wise x*x, of the same shape and dtype as x. Returns scalar if x is a scalar. See also numpy.linalg.matrix_power, sqrt, power Examples >>> np.square([-1j, 1]) array([-1.-0.j, 1.+0.j])

HermiteE.cutdeg(deg) [source] Truncate series to the given degree. Reduce the degree of the series to deg by discarding the high order terms. If deg is greater than the current degree a copy of the current series is returned. This can be useful in least squares where the coefficients of the high degree terms may be very small. New in version 1.5.0. Parameters: deg : non-negative int The series is reduced to degree deg by discarding the high order terms. The value of deg must be a non-ne

numpy.ma.is_masked(x) [source] Determine whether input has masked values. Accepts any object as input, but always returns False unless the input is a MaskedArray containing masked values. Parameters: x : array_like Array to check for masked values. Returns: result : bool True if x is a MaskedArray with masked values, False otherwise. Examples >>> import numpy.ma as ma >>> x = ma.masked_equal([0, 1, 0, 2, 3], 0) >>> x masked_array(data = [-- 1 -- 2 3],