Python中元组重新分配地址后旧地址对象是否被清理？元组与列表修改的空间效率对比

Let's walk through each of your questions clearly, using Python's core memory management rules as our guide.

1. Will the old tuple at address 140555133913704 be deleted, and will its address be released? #

Python relies on reference counting as its primary garbage collection tool, with a generational collector handling edge cases. Here's the play-by-play from your code:

• At first, the variable skills points to your original tuple at 140555133913704, giving that tuple a reference count of 1.
• When you run skills += ("ml","dl"), Python can't modify the original tuple (tuples are immutable by design). Instead, it creates a brand new tuple that combines the old elements and the new ones, then reassigns skills to point to this new object.
• The old tuple's reference count drops to 0 right away—assuming no other variables in your code are still pointing to it.

Once the reference count hits 0, Python's garbage collector will mark the old tuple for cleanup. Eventually, the memory occupied by that tuple (the address 140555133913704) will be released back to Python's memory pool, ready to be reused for future objects. Cleanup might not happen instantly, but it will run automatically without any manual intervention from you.

2. When a tuple is reassigned to a new address, is the value at the old address cleared? #

Until the garbage collector processes the old tuple, the data at the old address still lives in memory—but since no variables point to it anymore, your program has no way to access it. Once the collector cleans up the tuple, that memory space will either:

• Be overwritten with new data when the memory pool allocates it to a new object, or
• Be marked as free and returned to the operating system (depending on the object's size and Python's memory policies).

In short: You can't access the old value after reassignment, and it will be cleared or repurposed once garbage collection runs.

3. Is modifying a tuple more or less space-efficient than modifying a list? #

First, a key clarification: You can't actually modify a tuple. Any operation that looks like "modifying" a tuple (like +=) creates an entirely new tuple. Lists, by contrast, are mutable—you can add, remove, or change elements directly in-place.

Here's how their space efficiency stacks up:

• Tuples: Every "modification" requires allocating an entirely new block of memory to hold the new tuple, copying all existing elements plus the new ones. The old tuple lingers in memory until garbage collection cleans it up, meaning you temporarily use twice the memory (old + new tuple) during the operation. For frequent updates, this adds up to significant memory overhead.
• Lists: Lists pre-allocate extra space (a "capacity buffer") to handle future additions. When you use append() or extend(), Python can often add elements directly to this pre-allocated space without reallocating the entire list. Even when reallocation is needed, it typically doubles the list's capacity (reducing how often reallocations happen), making repeated modifications far more memory-efficient.

So, if you need to frequently update a collection of elements, lists are way more space-efficient than tuples. Tuples excel when you have a fixed set of unchanging elements—their immutability makes them safe for use as dictionary keys, and they have minor performance benefits in specific scenarios.

内容的提问来源于stack exchange，提问作者Jayant Kumar