reshape¶

reshape(a, newshape, order='C', out=None)¶

Gives a new shape to an array without changing its data. This function always returns a copy of the input array if out is not provided.

Parameters:

a : ndarray: Array to be reshaped.
newshape : int or tuple of ints: The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.
order : {‘C’}, optional: Read the elements of a using this index order, and place the elements into the reshaped array using this index order. ‘C’ means to read / write the elements using C-like index order, with the last axis index changing fastest, back to the first axis index changing slowest. Other order types such as ‘F’/’A’ may be added in the future.

Returns:

reshaped_array : ndarray: It will be always a copy of the original array. This behavior is different from the official NumPy reshape operator where views of the original array may be generated.

See also

Examples

>>> a = np.arange(6).reshape((3, 2))
>>> a
array([[0., 1.],
       [2., 3.],
       [4., 5.]])

>>> np.reshape(a, (2, 3)) # C-like index ordering
array([[0., 1., 2.],
       [3., 4., 5.]])

>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([[0., 1., 2.],
       [3., 4., 5.]])

>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1., 2., 3., 4., 5., 6.])

>>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
array([[1., 2.],
       [3., 4.],
       [5., 6.]])