Document

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

Notes

This is equivalent to:

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

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 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).

versionadded

Returns

arr : ndarray: Array interpretation of a.

Return an array copy of the given object.

Examples

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])

Note that np.copy is a shallow copy and will not copy object elements within arrays. This is mainly important for arrays containing Python objects. The new array will contain the same object which may lead to surprises if that object can be modified (is mutable):

>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
... b = np.copy(a)
... b[2][0] = 10
... a
array([1, 'm', list([10, 3, 4])], dtype=object)

To ensure all elements within an object array are copied, use copy.deepcopy :

>>> import copy
... a = np.array([1, 'm', [2, 3, 4]], dtype=object)
... c = copy.deepcopy(a)
... c[2][0] = 10
... c
array([1, 'm', list([10, 3, 4])], dtype=object)

>>> a
array([1, 'm', list([2, 3, 4])], dtype=object)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.nan_to_num astropy.io.fits.fitsrec.FITS_rec.copy numpy.ma.core.copy scipy.signal._signaltools.correlate2d numpy.ma.core.masked_where

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