numpy.reshape#
- numpy.reshape(a, /, shape=None, *, newshape=None, order='C', copy=None)[source]#
Gives a new shape to an array without changing its data.
- Parameters:
- aarray_like
Array to be reshaped.
- shapeint 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.
- newshapeint or tuple of ints
Deprecated since version 2.1: Replaced by
shape
argument. Retained for backward compatibility.- 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 ifa
is Fortran contiguous in memory, C-like order otherwise.- copybool, optional
If
True
, then the array data is copied. IfNone
, a copy will only be made if it’s required byorder
. ForFalse
it raises aValueError
if a copy cannot be avoided. Default:None
.
- Returns:
- reshaped_arrayndarray
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 froma
, 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]])