numpy.reshape¶

numpy.reshape(a,newshape,order='C')[source]¶

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

Parameters:

a : array_like Array to be reshaped. newshape : int or tuple of ints The new shape should be compatible with the original shape. Ifan integer, then the result will be a 1-D array of that length.One shape dimension can be -1. In this case, the value isinferred from the length of the array and remaining dimensions. order : {‘C’, ‘F’, ‘A’}, optional Read the elements ofa using this index order, and place theelements 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 firstaxis index changing slowest. ‘F’ means to read / write theelements using Fortran-like index order, with the first indexchanging fastest, and the last index changing slowest. Note thatthe ‘C’ and ‘F’ options take no account of the memory layout ofthe underlying array, and only refer to the order of indexing.‘A’ means to read / write the elements in Fortran-like indexorder ifa is Fortrancontiguous in memory, C-like orderotherwise.

Returns:

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

See also

ndarray.reshape

Equivalent method.

Notes

It is not always possible to change the shape of an array withoutcopying the data. If you want an error to be raise if the data is copied,you should assign the new shape to the shape attribute of the array:

>>>a=np.zeros((10,2))# A transpose make the array non-contiguous>>>b=a.T# Taking a view makes it possible to modify the shape without modifying# the initial object.>>>c=b.view()>>>c.shape=(20)AttributeError: incompatible shape for a non-contiguous array

Theorder keyword gives the index ordering both forfetching the valuesfroma, and thenplacing the values into the output array.For example, let’s say you have an array:

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

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

>>>np.reshape(a,(2,3))# C-like index orderingarray([[0, 1, 2],       [3, 4, 5]])>>>np.reshape(np.ravel(a),(2,3))# equivalent to C ravel then C reshapearray([[0, 1, 2],       [3, 4, 5]])>>>np.reshape(a,(2,3),order='F')# Fortran-like index orderingarray([[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

>>>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 2array([[1, 2],       [3, 4],       [5, 6]])