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Module « numpy.matlib »

Fonction copy - module numpy.matlib

Signature de la fonction copy

def copy(a, order='K', subok=False) 

Description

help(numpy.matlib.copy)

Return an array copy of the given object.

Parameters
----------
a : array_like
    Input data.
order : {'C', 'F', 'A', 'K'}, optional
    Controls the memory layout of the copy. 'C' means C-order,
    'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
    'C' otherwise. 'K' means match the layout of `a` as closely
    as possible. (Note that this function and :meth:`ndarray.copy` are very
    similar, but have different default values for their order=
    arguments.)
subok : bool, optional
    If True, then sub-classes will be passed-through, otherwise the
    returned array will be forced to be a base-class array (defaults to False).

Returns
-------
arr : ndarray
    Array interpretation of `a`.

See Also
--------
ndarray.copy : Preferred method for creating an array copy

Notes
-----
This is equivalent to:

>>> np.array(a, copy=True)  #doctest: +SKIP

The copy made of the data is shallow, i.e., for arrays with object dtype,
the new array will point to the same objects.
See Examples from `ndarray.copy`.

Examples
--------
>>> import numpy as np

Create an array x, with a reference y and a copy z:

>>> x = np.array([1, 2, 3])
>>> y = x
>>> z = np.copy(x)

Note that, when we modify x, y changes, but not z:

>>> x[0] = 10
>>> x[0] == y[0]
True
>>> x[0] == z[0]
False

Note that, np.copy clears previously set WRITEABLE=False flag.

>>> a = np.array([1, 2, 3])
>>> a.flags["WRITEABLE"] = False
>>> b = np.copy(a)
>>> b.flags["WRITEABLE"]
True
>>> b[0] = 3
>>> b
array([3, 2, 3])

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