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__(arg) [source]

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 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() 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(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

numpy.random.negative_binomial(n, p, size=None) Draw samples from a negative binomial distribution. Samples are drawn from a negative binomial distribution with specified parameters, n trials and p probability of success where n is an integer > 0 and p is in the interval [0, 1]. Parameters: n : int Parameter, > 0. p : float Parameter, >= 0 and <=1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.

matrix.imag The imaginary part of the array. Examples >>> x = np.sqrt([1+0j, 0+1j]) >>> x.imag array([ 0. , 0.70710678]) >>> x.imag.dtype dtype('float64')

class numpy.lib.user_array.container(data, dtype=None, copy=True) [source] Standard container-class for easy multiple-inheritance. Methods copy tostring byteswap astype

MaskedArray.diagonal(offset=0, axis1=0, axis2=1) [source] Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed. Refer to numpy.diagonal for full documentation. See also numpy.diagonal equivalent function