- ⚡ Accessing a list object via index is O(1), but accessing a local variable is even faster due to Python’s stack-based bytecode.
- 🔁 Reusing an object from a list avoids extra lookups and makes code easier to keep up, especially in nested or repeated access situations.
- 💡 Python doesn't duplicate objects when reused in a variable; it just creates a new reference, adding no memory overhead.
- 🔬 In large loops or code that runs very often, combining reuse and list object shorthand can make code run much faster.
- 🛠️ IDEs like PyCharm can spot repetitive indexing and suggest ways to make your code cleaner and work better with shorthand.
The Case of List Object Reuse
Imagine you're working with a list of objects and need to reference the same one multiple times in your function. Do you keep indexing into the list every time, or make your code cleaner by assigning the object to a variable first? It may seem like a matter of style, but as with many things in programming, there are things you gain and lose. Let's look at what it really means to reuse an object from a list, when list object shorthand is helpful, and how list object speed affects writing better code.
How List Indexing Works in Python
When working with lists in Python, you're used to using index lookups like my_list[i] to get an item. Python's list is built as a resizing array. As a result, finding items in a list by index is very fast — it’s what’s called an O(1), or constant-time, operation. No matter how large the list gets, accessing my_list[i] takes the same amount of time.
That sounds great — so why think about other ways?
But, even though it's O(1), looking up an item in a Python list by index still makes the Python interpreter do some work when the program runs. Every time you use my_list[i], Python gets the item again from memory. This isn't a problem once or twice. But if you're calling the same index again and again — especially in a tight loop or in code where speed really matters — those extra lookups can slow things down.
More importantly, Python’s compiler sees a difference between global, local, and extended scopes when it builds the code. And local variable access happens through fast stack-based bytecodes. That makes accessing a local variable — like one that holds a reference to your list object — faster than triggering another indexed lookup each time.
What List Object Shorthand Looks Like
Let’s break down what we mean by "list object shorthand." It usually means getting a list item once, giving it to a local variable, and then using that variable again.
Without Reuse:
if my_list[i].enabled:
print(my_list[i].name)
This line accesses my_list[i] twice — once to check .enabled, and again to print .name.
With Reuse (Shorthand):
item = my_list[i]
if item.enabled:
print(item.name)
Here, the list object is accessed only once, then used again through the short name item.
While this might look like a small change, it has a big effect on speed, clarity, and how easy your code is to keep up. It’s also the main idea behind the “reuse object from list” and “list object shorthand” ways of working used by experienced Python programmers.
Why Reuse: Clean Code with Less Repetition
Using reuse gets rid of repeated code. This is a rule known to programmers as DRY — Don’t Repeat Yourself. When you're using the same object in many places within a function or loop, giving it a temporary variable name makes code easier to read and makes it less likely to have bugs.
For instance, if you later change my_list[i] to use another data structure or method call like get_user_by_index(i), updating five hardcoded lines instead of one will take time and may cause mistakes or make things not match up.
Also, using a local variable — especially with a clear name — makes the meaning clearer. It's easier to understand user = users[i] followed by user.email, than to see users[i].email spread throughout the function and constantly having to figure out what users[i] means each time.
This makes your code better and easier for both you and other developers to understand and change in the future.
How List Objects Perform in the Real World
While Python is not usually for very low-level system speed, developers writing services that handle a lot of data quickly or processing large datasets often run into things that slow them down in surprising places — including getting to the same object many times.
As the Python Cookbook by Beazley and Jones (2013) says, local variable access is much faster than list index access. Let's look at why.
Python turns source code into bytecode, which the Python Virtual Machine (PVM) then runs. Getting to my_list[i] results in three or more bytecode steps:
- Load the list
- Load the index
- Do the lookup
But, getting to a local variable like item made from the list uses just one LOAD_FAST bytecode instruction. That difference becomes important in situations where you do something many times, such as:
for i in range(len(my_list)):
# Redundant index lookup
do_something(my_list[i].attribute)
if condition(my_list[i].value):
continue
Versus:
for i in range(len(my_list)):
item = my_list[i]
do_something(item.attribute)
if condition(item.value):
continue
Small tests often show hundreds or even thousands of nanoseconds saved in total in such cases. While these may not matter in simple uses, apps where speed is key, like machine learning preprocessing, real-time data pipelines, or API response formatting, get a clear boost.
Understanding Memory: No Extra Cost for Temp Variable
Some developers — especially those from languages like C++ or Java — worry that giving a list object to another variable might copy the object. That's not what happens in Python.
All variables in Python are references. They point to the data, but are not the data itself. Giving a list item to a new variable creates another reference to the exact same object in memory:
item = my_list[i]
No copying, no doubling up — just a faster and easier way to get to the same data.
💡 This way of saving memory with references makes it possible to reuse list object shorthands without affecting your app’s memory use or using too much memory.
Shorthand Reuse with Changeable Objects
Working with changeable objects like dictionaries, lists, or instances of classes you define brings up a small detail with reuse.
Example:
user = users[0]
user.roles.append('admin')
Since both user and users[0] point to the same object, the append() changes the original list member where it is.
This is not a bug; it’s how Python works. But it can cause things to change in ways you didn't mean if you're not careful. For example, if later in the code you try to compare the original list with its changed reference, the results could be wrong.
Be careful when you:
- Need objects to stay the same (int, str, tuple are safe)
- Plan to rollback changes
- Use copied objects in list comprehensions
When objects must not change, copy the object directly using copy.copy() or copy.deepcopy().
Nested Access: Where Shorthand Really Helps
Getting to deeply nested data structures many times makes the benefits of reusing list objects even bigger. The less messy each line is, the easier it is to understand your code.
Long version:
if users[i].profile.settings.theme == 'dark' and users[i].profile.is_active:
notify(users[i].contact_details.email)
Changed with reuse:
user = users[i]
if user.profile.settings.theme == 'dark' and user.profile.is_active:
notify(user.contact_details.email)
This version:
- Is easier to read
- Shows important parts (attributes instead of the index path)
- Puts the logic in one spot for possible reuse and finding bugs
Times to Avoid List Object Shorthand
Even though shorthand is useful, it isn’t always the right choice. There are situations where being short or simple makes avoiding it the better way:
- One-time Access: If you're truly only getting to an index once, creating a temporary variable adds extra words you don't need.
- Short, Simple Expressions: In small, quick scripts or code you won't use again, it's more important to be brief than to make it easy to keep up.
- Multiple Indices or Conditions: Using too many shorthands can make the code harder to understand by adding too many local variables.
Example of over-doing it:
alpha = data[0]
beta = data[1]
gamma = data[2]
# Each used once
Better way:
result = data[0] + data[1] + data[2]
Only use shorthand when it makes things clearer or faster, rather than blindly simplifying every time you get something from a list.
Good Habits for Reusing List Objects
If you're going to use list object reuse and shorthand with care, follow these good habits:
- ✅ Use clear variable names that show what's inside (
product,user,entry) - ✅ Put your variable assignments as close as possible to where you use them
- ✅ Don't keep reusable variables around if you only use them once
- 🚫 Don't use a short name for overly nested attributes (e.g.,
x = y.z.a.b.c) unless you need to get to them many times - ⚠️ Add comments on reused references in places where things could go wrong (e.g., changing objects that other parts of the code also use)
Let Your IDE Help You
Modern coding tools see repeated indexing and will warn you: “Repeated subscript access can be replaced with temporary variable.” Tools like:
- PyCharm
- VS Code extensions (e.g., Python Linter)
- Pylint / Flake8
…all show these problems and suggest fixes, even auto-suggesting better ways to write code. Changing your code is often just one keyboard shortcut away.
Especially when working in teams, using shorthand and reuse patterns all the time helps people work together and makes it easier to find and fix bugs.
Looping Through List Objects Well
Perhaps the most common way to use reuse is inside loops.
Regular Loop with Index
for i in range(len(items)):
item = items[i]
process(item.value)
Here, reuse makes sense because you might get to many different attributes or methods.
A More Usual and Pythonic Way:
for item in items:
process(item.value)
Python encourages this direct way because it:
- Avoids dealing with indexes
- Is slightly faster inside
- Is easier to read and shorter
Only go back to indexed loops if you need the index (i) or are changing list items directly using indexes.
When List Comprehensions Win Over Reuse
List comprehensions offer Python’s most clever and neat shorthand in many situations:
ids = [user.id for user in users if user.is_active]
This way of writing implicitly uses the variable user, avoiding index calls entirely. It also works fast and is easier to read.
But, don't put too much complex code into comprehensions:
# Bad: Too much logic embedded
results = [f(x)+1 if x>0 else g(x)*2 for x in data if x%2==0 and x!=4]
Such complex code becomes hard to read. Instead, use list comprehensions for simple changes and move hard code parts to helper functions or loops.
Think About Speed, But Focus on Clarity
Writing fast code is good — writing clear code is better.
Before using list object shorthand:
- Ask if you get to the object many times.
- Check if reuse makes things clearer or less repetitive.
- Make sure it fits with how Python usually works.
A small speed gain isn’t worth confusing future readers (or your future self).
Final Say on Reusing List Objects
Reusing list objects with shorthand is more than a speed trick — it's a good habit that helps make Python code readable, easy to keep up, and fast. Bytecode savings are real, though small, but what matters most is how shorthand helps you avoid repetition, makes things clearer, and stops small bugs.
While list object speed may not always make it worth changing your code, how it combines with DRY principles, saving memory, and how well it fits with Python's design makes it a habit worth forming.
Use list object shorthand when it helps, skip it when it gets in the way, and use your tools and common sense to write good code.
Citations
- Van Rossum, G. (2024). Time Complexity Overview. Python Software Foundation. Retrieved from https://wiki.python.org/moin/TimeComplexity
- Beazley, D. M., & Jones, B. K. (2013). Python Cookbook. O'Reilly Media.
Want more clean-code tips and performance explanations? Check out our full Devsolus collection of Python best practices.
