What is the need of ellipsis[…] while modifying array values in numpy?
What is the need of ellipsis[…] while modifying array values in numpy?
import numpy as np
a = np.arange(0,60,5)
a = a.reshape(3,4)
for x in np.nditer(a, op_flags = ['readwrite']):
x[...] = 2*x
print 'Modified array is:'
print a
In the above code, why can't we simply write x=2*x instead of x[...]=2*x?
x = 2*x
x[...] = 2*x
@user2285236:
x = 2*x doesn't create a new variable. It's just that the variable isn't the thing we need to change. Assigning to the variable doesn't help us.– user2357112
Sep 2 at 10:09
x = 2*x
What was wrong with this question that somebody downvoted it?
– Wojciech Kaczmarek
Sep 2 at 10:11
If you are a numpy beginner, don't spend much time on
nditer. Focus on whole array operations, like b=2*a. Even when you must use iteration, it isn't the fastest or simplest.– hpaulj
Sep 2 at 13:01
nditer
b=2*a
The main
nditer tutorial, docs.scipy.org/doc/numpy/reference/arrays.nditer.html, is most useful if you read all the way to the end, and experiment with the cython port. Otherwise you'll get a false sense of its usefulness.– hpaulj
Sep 2 at 17:14
nditer
cython
1 Answer
1
No matter what kind of object we were iterating over or how that object was implemented, it would be almost impossible for x = 2*x to do anything useful to that object. x = 2*x is an assignment to the variable x; even if the previous contents of the x variable were obtained by iterating over some object, a new assignment to x would not affect the object we're iterating over.
x = 2*x
x = 2*x
x
x
x
In this specific case, iterating over a NumPy array with np.nditer(a, op_flags = ['readwrite']), each iteration of the loop sets x to a zero-dimensional array that's a writeable view of a cell of a. x[...] = 2*x writes to the contents of the zero-dimensional array, rather than rebinding the x variable. Since the array is a view of a cell of a, this assignment writes to the corresponding cell of a.
np.nditer(a, op_flags = ['readwrite'])
x
a
x[...] = 2*x
x
a
a
This is very similar to the difference between l = and l[:] = with ordinary lists, where l[:] = will clear an existing list and l = will replace the list with a new, empty list without modifying the original. Lists don't support views or zero-dimensional lists, though.
l =
l[:] =
l[:] =
l =
The fact that
x is 0d, and requires x[..,] instead of x[:] is part of why nditer is unsuitable for numpy beginners.– hpaulj
Sep 2 at 13:07
x
x[..,]
x[:]
nditer
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If you write
x = 2*xinside that loop, you just create a new variable x (different from the one that comes from the iteration) and assign the result to it. Withx[...] = 2*x, you are modifying x directly. You might want to check this post.– ayhan
Sep 2 at 10:08