record.fill()

record.fill() 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

record.max()

record.max() 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

Polynomial.copy()

Polynomial.copy() [source] Return a copy. Returns: new_series : series Copy of self.

numpy.load()

numpy.load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, encoding='ASCII') [source] Load arrays or pickled objects from .npy, .npz or pickled files. Parameters: file : file-like object or string The file to read. File-like objects must support the seek() and read() methods. Pickled files require that the file-like object support the readline() method as well. mmap_mode : {None, ?r+?, ?r?, ?w+?, ?c?}, optional If not None, then memory-map the file, using the given mode (see

ndarray.__copy__()

ndarray.__copy__([order]) Return a copy of the array. Parameters: order : {?C?, ?F?, ?A?}, optional If order is ?C? (False) then the result is contiguous (default). If order is ?Fortran? (True) then the result has fortran order. If order is ?Any? (None) then the result has fortran order only if the array already is in fortran order.

Chebyshev.__call__()

Chebyshev.__call__(arg) [source]

ndarray.ndim

ndarray.ndim Number of array dimensions. Examples >>> x = np.array([1, 2, 3]) >>> x.ndim 1 >>> y = np.zeros((2, 3, 4)) >>> y.ndim 3

dtype.isalignedstruct

dtype.isalignedstruct Boolean indicating whether the dtype is a struct which maintains field alignment. This flag is sticky, so when combining multiple structs together, it is preserved and produces new dtypes which are also aligned.

nditer.copy()

nditer.copy() Get a copy of the iterator in its current state. Examples >>> x = np.arange(10) >>> y = x + 1 >>> it = np.nditer([x, y]) >>> it.next() (array(0), array(1)) >>> it2 = it.copy() >>> it2.next() (array(1), array(2))

numpy.median()

numpy.median(a, axis=None, out=None, overwrite_input=False, keepdims=False) [source] Compute the median along the specified axis. Returns the median of the array elements. Parameters: a : array_like Input array or object that can be converted to an array. axis : {int, sequence of int, None}, optional Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : n