The iterator object nditer, introduced in NumPy 1.6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. This page introduces some basic ways to use the object for computations on arrays in Python, then concludes with how one can accelerate the ...

class numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)[source]#

class numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)[source]#

import numpy as np arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) for x in np.nditer(arr): print(x) Try it Yourself »

class numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)[source]#

The iterator object nditer, introduced in NumPy 1.6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. This page introduces some basic ways to use the object for computations on arrays in Python, then concludes with how one can accelerate the ...

class numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)[source]#

July 23, 2025 - import numpy as np # Using nditer arr = np.array([[1, 2], [3, 4]]) for element in np.nditer(arr): print(element)

The iterator object nditer, introduced in NumPy 1.6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. This page introduces some basic ways to use the object for computations on arrays in Python, then concludes with how one can accelerate the ...

The nditer() function in NumPy provides an efficient multidimensional iterator object that can be used to iterate over elements of arrays.

def iter_add(x, y, out=None): addop = np.add it = np.nditer([x, y, out], [], [['readonly'], ['readonly'], ['writeonly','allocate']]) while not it.finished: addop(it[0], it[1], out=it[2]) it.iternext() return it.operands[2]

class numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)[source]#

The iterator object nditer, introduced in NumPy 1.6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. This page introduces some basic ways to use the object for computations on arrays in Python, then concludes with how one can accelerate the ...

March 26, 2014 - def iter_add(x, y, out=None): addop = np.add it = np.nditer([x, y, out], [], [['readonly'], ['readonly'], ['writeonly','allocate']]) while not it.finished: addop(it[0], it[1], out=it[2]) it.iternext() return it.operands[2]

The iterator object nditer, introduced in NumPy 1.6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. This page introduces some basic ways to use the object for computations on arrays in Python, then concludes with how one can accelerate the ...

def iter_add(x, y, out=None): addop = np.add it = np.nditer([x, y, out], [], [['readonly'], ['readonly'], ['writeonly','allocate']]) while not it.finished: addop(it[0], it[1], out=it[2]) it.iternext() return it.operands[2]

September 9, 2022 - Numpy.nditer is an iterator object in the NumPy package.

def iter_add(x, y, out=None): addop = np.add it = np.nditer([x, y, out], [], [['readonly'], ['readonly'], ['writeonly','allocate']]) while not it.finished: addop(it[0], it[1], out=it[2]) it.iternext() return it.operands[2]

May 15, 2011 - def iter_add(x, y, out=None): addop = np.add it = np.nditer([x, y, out], [], [['readonly'], ['readonly'], ['writeonly','allocate']]) while not it.finished: addop(it[0], it[1], out=it[2]) it.iternext() return it.operands[2]

By default, the nditer uses the flags ‘allocate’ and ‘writeonly’ for operands that are passed in as None. This means we were able to provide just the two operands to the iterator, and it handled the rest. However, “None” is full of junk in the beginning! import numpy as np a = np.arange(10, 19).reshape(3, 3) print(a) print() with np.nditer([a, None]) as iterator: for x, y in iterator: y[...] += x**2 out_1 = iterator.operands[0] out_2 = iterator.operands[1] print(out_1) print() print(out_2)