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- numpy array subclass unexpedly shares attributes across instances 1 answer
Here's a function. My intent is to use keyword argument defaults to make the dictionary an empty dictionary if it is not supplied.
>>> def f( i, d={}, x=3 ) :
... d[i] = i*i
... x += i
... return x, d
...
>>> f( 2 )
(5, {2: 4})
But when I next call f, I get:
>>> f(3)
(6, {2: 4, 3: 9})
It looks like the keyword argument d at the second call does not point to an empty dictionary, but rather to the dictionary as it was left at the end of the preceding call. The number x is reset to three on each call.
Now I can work around this, but I would like your help understanding this. I believed that keyword arguments are in the local scope of the function, and would be deleted once the function returned. (Excuse and correct my terminology if I am being imprecise.)
So the local value pointed to by the name d should be deleted, and on the next call, if I don't supply the keyword argument d, then d should be set to the default {}
. But as you can see, d is being set to the dictionary that d pointed to in the preceding call.
What is going on?
Is the literal {}
in the def line in the enclosing scope?
This behavior is seen in 2.5, 2.6 and 3.1.