Why do elements in Python for ... in ...: statements use byValue behavior?

Question

I can't seem to find an explanation anywhere for this...

Suppose I have a vector y initialized to all zeroes:

from numpy import *
y = zeros(5)

It could also be a plain python array, I don't think it really matters.

I noticed that the behavior of the for x in y: statement is that it makes a copy of each element in y, thus when you modify x, it does not modify y.

for x in y:
    x = 1
print y

output : array([ 0.,  0.,  0.,  0.,  0.])

My question is the following : Why is that so? I thought in Python everything was all byReference and that there is very little byValue passing around?

How could I do the following, using referenced variables? Doing the following seems to work :

for i in range(len(y)):
    y[i] = 2*rand()-1

But from what I know about Python, this is wrong and I believe it will be slow when I start using vectors of thousands or millions of values.

What else could I do?

Answer 1

You can replace this:

y = np.zeros(5)
for i in range(len(y)):
    y[i] = 2*rand()-1

with:

y=2*np.random.rand(5)-1

---

If y is a Python sequence (tuple, list, array, etc) or Numpy array,

for x in y:

iterates through y, setting x to each element in y. If the elements are immutable objects like str, int, float or a Numpy numeric type, then changing the value of x does not change the value in y.

If the elements are mutable objects, such as a list, then mutating the list will affect y.

Think of it this way: x is "pointing" to an object:

y---> [o----o----o----o]
            ^
            |
            x

When you write an assignment statement:

x += 1

Now you are reassigning x to "point" to a different object:

y---> [o----o----o----o]

x---> o

On the other hand, if you mutate a list with, say, x.append(1), then x still points to the same object (e.g. a list), but you've mutated the contents of that list.