How do i copy a numpy 2d array in python?

I think np.tile also might be useful

>>> a = np.array([0, 1, 2])
>>> np.tile(a, 2)
array([0, 1, 2, 0, 1, 2])
>>> np.tile(a, (2, 2))
array([[0, 1, 2, 0, 1, 2],
       [0, 1, 2, 0, 1, 2]])
>>> np.tile(a, (2, 1, 2))
array([[[0, 1, 2, 0, 1, 2]],
       [[0, 1, 2, 0, 1, 2]]])

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

Return an array copy of the given object.

Parametersaarray_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).

New in version 1.19.0.

Returnsarrndarray

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)  

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)

How do you copy an array in Numpy Python?

Use numpy. copy() function to copy Python NumPy array (ndarray) to another array. This method takes the array you wanted to copy as an argument and returns an array copy of the given object. The copy owns the data and any changes made to the copy will not affect the original array.

How do you copy a two dimensional list in Python?

Python does not actually have '2-dimensional arrays' as such, it just has lists, which can contain other lists..
b = copy. deepcopy(a).
b = [item[:] for item in a].
b = [item. copy() for item in a].
b = [list(item) for item in a].
b = [copy. ... .
b = []; b..

How do I copy one 2D array to another?

Using clone() method A simple solution is to use the clone() method to clone a 2-dimensional array in Java. The following solution uses a for loop to iterate over each row of the original array and then calls the clone() method to copy each row.

How do I duplicate in Numpy?

copy() in Python. numpy. ndarray. copy() returns a copy of the array.