numpy.resize#

numpy.resize(a, new_shape)[source]#

Return a new array with the specified shape.

If the new array is larger than the original array, then the new array is filled with repeated copies of a. Note that this behavior is different from a.resize(new_shape) which fills with zeros instead of repeated copies of a.

Parameters

aarray_like: Array to be resized.
new_shapeint or tuple of int: Shape of resized array.

Returns

reshaped_arrayndarray: The new array is formed from the data in the old array, repeated if necessary to fill out the required number of elements. The data are repeated iterating over the array in C-order.

See also

numpy.reshape: Reshape an array without changing the total size.
numpy.pad: Enlarge and pad an array.
numpy.repeat: Repeat elements of an array.
ndarray.resize: resize an array in-place.

Notes

When the total size of the array does not change reshape should be used. In most other cases either indexing (to reduce the size) or padding (to increase the size) may be a more appropriate solution.

Warning: This functionality does not consider axes separately, i.e. it does not apply interpolation/extrapolation. It fills the return array with the required number of elements, iterating over a in C-order, disregarding axes (and cycling back from the start if the new shape is larger). This functionality is therefore not suitable to resize images, or data where each axis represents a separate and distinct entity.

Examples

>>> a=np.array([[0,1],[2,3]])
>>> np.resize(a,(2,3))
array([[0, 1, 2],
       [3, 0, 1]])
>>> np.resize(a,(1,4))
array([[0, 1, 2, 3]])
>>> np.resize(a,(2,4))
array([[0, 1, 2, 3],
       [0, 1, 2, 3]])

numpy.append

numpy.trim_zeros