numpy.ravel¶

numpy.
ravel
(a, order='C')[source]¶ Return a contiguous flattened array.
A 1D array, containing the elements of the input, is returned. A copy is made only if needed.
As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array input)
Parameters: a : array_like
Input array. The elements in a are read in the order specified by order, and packed as a 1D array.
order : {‘C’,’F’, ‘A’, ‘K’}, optional
The elements of a are read using this index order. ‘C’ means to index the elements in rowmajor, Cstyle order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in columnmajor, Fortranstyle 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 axis indexing. ‘A’ means to read the elements in Fortranlike index order if a is Fortran contiguous in memory, Clike order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.
Returns: y : array_like
If a is a matrix, y is a 1D ndarray, otherwise y is an array of the same subtype as a. The shape of the returned array is
(a.size,)
. Matrices are special cased for backward compatibility.See also
ndarray.flat
 1D iterator over an array.
ndarray.flatten
 1D array copy of the elements of an array in rowmajor order.
ndarray.reshape
 Change the shape of an array without changing its data.
Notes
In rowmajor, Cstyle order, in two dimensions, the row index varies the slowest, and the column index the quickest. This can be generalized to multiple dimensions, where rowmajor order implies that the index along the first axis varies slowest, and the index along the last quickest. The opposite holds for columnmajor, Fortranstyle index ordering.
When a view is desired in as many cases as possible,
arr.reshape(1)
may be preferable.Examples
It is equivalent to
reshape(1, order=order)
.>>> x = np.array([[1, 2, 3], [4, 5, 6]]) >>> print(np.ravel(x)) [1 2 3 4 5 6]
>>> print(x.reshape(1)) [1 2 3 4 5 6]
>>> print(np.ravel(x, order='F')) [1 4 2 5 3 6]
When
order
is ‘A’, it will preserve the array’s ‘C’ or ‘F’ ordering:>>> print(np.ravel(x.T)) [1 4 2 5 3 6] >>> print(np.ravel(x.T, order='A')) [1 2 3 4 5 6]
When
order
is ‘K’, it will preserve orderings that are neither ‘C’ nor ‘F’, but won’t reverse axes:>>> a = np.arange(3)[::1]; a array([2, 1, 0]) >>> a.ravel(order='C') array([2, 1, 0]) >>> a.ravel(order='K') array([2, 1, 0])
>>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a array([[[ 0, 2, 4], [ 1, 3, 5]], [[ 6, 8, 10], [ 7, 9, 11]]]) >>> a.ravel(order='C') array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11]) >>> a.ravel(order='K') array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])