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certainly! the phrase "i can't believe python would allow this" often refers to unexpected behavior or features in python that can lead to surprising results. one such feature is the dynamic typing and the way python handles mutable and immutable objects. in this tutorial, we will explore a few noteworthy examples that highlight python's flexibility, sometimes leading to confusion or unexpected outcomes.
1. mutable vs. immutable types
python has both mutable and immutable types. mutable types can be changed after they are created (like lists or dictionaries), whereas immutable types cannot be changed (like strings and tuples).
#### example: mutable list behavior
*output:*
*explanation:*
the list `my_list` is passed to the function `modify_list`, and since lists are mutable, the original list is modified. this behavior can sometimes lead to unintended side effects.
2. function default arguments
default arguments in functions are evaluated once when the function is defined, not each time the function is called.
#### example: default argument behavior
*output:*
*explanation:*
the default list is shared across function calls. when you append to the list in a subsequent call, it retains the values from previous calls unless a new list is explicitly provided.
3. variable scope and closures
in python, nested functions can access variables from their enclosing scopes, which can lead to surprising behaviors.
#### example: closure behavior
*output:*
*explanation:*
the `incrementer` function retains access to the `increment` variable from `make_incrementer`, which can lead to confusion if the variable is modified between calls.
4. list comprehensions and scope
list comprehensions can introduce some quirks with variable scope.
#### example: variable scope in comprehensions
*output:*
*explanation:*
the variable `x` inside the list comprehension shadows the outer `x`, which can lead to unexpected results. aft ...
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