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Module « numpy »

Fonction resize - module numpy

Signature de la fonction resize

def resize(a, new_shape) 

Description

help(numpy.resize)

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
----------
a : array_like
    Array to be resized.

new_shape : int or tuple of int
    Shape of resized array.

Returns
-------
reshaped_array : ndarray
    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 `~numpy.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
--------
>>> import numpy as np
>>> 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]])

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