Late Binding Closures Another common source of confusion is the way Python binds its variables in closures (or in the surrounding global scope).

What You Wrote

def create_multipliers():
    return [lambda x : i * x for i in range(5)]

What You Might Have Expected to Happen

for multiplier in create_multipliers():
    print multiplier(2)

A list containing five functions that each have their own closed-over i variable that multiplies their argument, producing:

0
2
4
6
8

What Does Happen

8
8
8
8
8

Five functions are created; instead all of them just multiply x by 4.

Python’s closures are late binding. This means that the values of variables used in closures are looked up at the time the inner function is called.

Here, whenever any of the returned functions are called, the value of i is looked up in the surrounding scope at call time. By then, the loop has completed and i is left with its final value of 4.

What’s particularly nasty about this gotcha is the seemingly prevalent misinformation that this has something to do with lambdas in Python. Functions created with a lambda expression are in no way special, and in fact the same exact behavior is exhibited by just using an ordinary def:

def create_multipliers():
    multipliers = []

    for i in range(5):
        def multiplier(x):
            return i * x
        multipliers.append(multiplier)

    return multipliers

What You Should Do Instead The most general solution is arguably a bit of a hack. Due to Python’s aforementioned behavior concerning evaluating default arguments to functions (see Mutable Default Arguments), you can create a closure that binds immediately to its arguments by using a default arg like so:

def create_multipliers():
    return [lambda x, i=i : i * x for i in range(5)]

Alternatively, you can use the functools.partial function:

from functools import partial
from operator import mul

def create_multipliers():
    return [partial(mul, i) for i in range(5)]

reference

online book