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Module « numpy.matlib »

Fonction reshape - module numpy.matlib

Signature de la fonction reshape

def reshape(a, /, shape=None, order='C', *, newshape=None, copy=None) 

Description

help(numpy.matlib.reshape)

Gives a new shape to an array without changing its data.

Parameters
----------
a : array_like
    Array to be reshaped.
shape : 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', 'F', 'A'}, 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. 'F' means to read / write the
    elements using Fortran-like index order, with the first index
    changing fastest, and the last index changing slowest. Note that
    the 'C' and 'F' options take no account of the memory layout of
    the underlying array, and only refer to the order of indexing.
    'A' means to read / write the elements in Fortran-like index
    order if ``a`` is Fortran *contiguous* in memory, C-like order
    otherwise.
newshape : int or tuple of ints
    .. deprecated:: 2.1
        Replaced by ``shape`` argument. Retained for backward
        compatibility.
copy : bool, optional
    If ``True``, then the array data is copied. If ``None``, a copy will
    only be made if it's required by ``order``. For ``False`` it raises
    a ``ValueError`` if a copy cannot be avoided. Default: ``None``.

Returns
-------
reshaped_array : ndarray
    This will be a new view object if possible; otherwise, it will
    be a copy.  Note there is no guarantee of the *memory layout* (C- or
    Fortran- contiguous) of the returned array.

See Also
--------
ndarray.reshape : Equivalent method.

Notes
-----
It is not always possible to change the shape of an array without copying
the data.

The ``order`` keyword gives the index ordering both for *fetching*
the values from ``a``, and then *placing* the values into the output
array. For example, let's say you have an array:

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

You can think of reshaping as first raveling the array (using the given
index order), then inserting the elements from the raveled array into the
new array using the same kind of index ordering as was used for the
raveling.

>>> 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]])
>>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
array([[0, 4, 3],
       [2, 1, 5]])
>>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
       [2, 1, 5]])

Examples
--------
>>> import numpy as np
>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])

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

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