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numpy.copy(a, order='K', subok=False)[source]#

Return an array copy of the given object.

Parameters: aarray_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.)

subokbool, 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: arrndarray

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)

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

Try it in your browser! >>> importnumpyasnp

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]) Go BackOpen In Tab

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