numpy.tile¶

numpy.tile(A,reps)[source]¶

Construct an array by repeating A the number of times given by reps.

Ifreps has lengthd, the result will have dimension ofmax(d,A.ndim).

IfA.ndim<d,A is promoted to be d-dimensional by prepending newaxes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,or shape (1, 1, 3) for 3-D replication. If this is not the desiredbehavior, promoteA to d-dimensions manually before calling thisfunction.

IfA.ndim>d,reps is promoted toA.ndim by pre-pending 1’s to it.Thus for anA of shape (2, 3, 4, 5), areps of (2, 2) is treated as(1, 1, 2, 2).

Note : Although tile may be used for broadcasting, it is stronglyrecommended to use numpy’s broadcasting operations and functions.

Parameters:	A:array_like The input array. reps:array_like The number of repetitions ofA along each axis.
Returns:	c:ndarray The tiled output array.

See also

repeat

Repeat elements of an array.

broadcast_to

Broadcast an array to a new shape

Examples

>>>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]]])

>>>b=np.array([[1,2],[3,4]])>>>np.tile(b,2)array([[1, 2, 1, 2],       [3, 4, 3, 4]])>>>np.tile(b,(2,1))array([[1, 2],       [3, 4],       [1, 2],       [3, 4]])

>>>c=np.array([1,2,3,4])>>>np.tile(c,(4,1))array([[1, 2, 3, 4],       [1, 2, 3, 4],       [1, 2, 3, 4],       [1, 2, 3, 4]])