One of the first things you learn about Python list comprehensions is that they are "Pythonic". But are they actually faster than traditional for loops?
If you ask a senior Python developer, they will likely say "Yes." If you ask why, they might mutter something about "C-level optimizations."
In this deep dive, we are going to look beyond the common wisdom. we will run rigorous benchmarks and analyze the bytecode.
[!NOTE] Benchmark Summary
- Simple Lists: Comprehensions are ~23% Faster.
- Filtering: Comprehensions are ~25% Faster.
- Complex Math: Speed is Identical (CPU bound).
- Verdict: Use comprehensions for speed in tight loops, but prioritize readability elsewhere.
Table of Contents
- The Short Answer
- Why Are Comprehensions Faster? (Visualized)
- Benchmark 1: Simple List Creation
- Benchmark 2: Filtering Items
- Benchmark 3: Complex Function Calls (The Equalizer)
- Memory Usage: The Hidden Cost
- When Speed Doesn't Matter
- FAQ
- Test Your Knowledge (Quiz)
The Short Answer
Yes, List Comprehensions are generally faster.
In most standard scenarios (creating a list by iterating over another), a list comprehension is 10% to 25% faster than an equivalent for loop that uses .append().
However, this speed advantage diminishes if:
- The logic inside the loop is extremely complex (the computation time outweighs the loop overhead).
- You are using Python 3.11+, which has significantly optimized standard loops.
Why Are Comprehensions Faster? (Visualized)
To understand the speed difference, we need to compare the "instructions" Python executes.
Standard For Loop List Comprehension
----------------- ------------------
1. START LOOP 1. START LOOP
2. Load 'res' variable 2. C-Optimized Append <---+
3. Load 'append' Method (FAST) 3. Next Item |
4. Call Method (SLOW) 4. Repeat ----------------+
5. Repeat
The "Append" Bottleneck
In the for loop, Python has to look up the .append attribute and call it as a function on every single iteration. This adds overhead.
The list comprehension creates a specific bytecode instruction (LIST_APPEND) that bypasses the attribute lookup. The iteration logic runs in C speed inside the interpreter.
Bytecode Analysis
We can verify this using the dis module.
For Loop Instructions:
FOR_ITER
STORE_FAST (i)
LOAD_FAST (res)
LOAD_ATTR (append) <-- Expensive lookup each time
LOAD_FAST (i)
CALL_METHOD <-- Expensive call each time
Comprehension Instructions:
LIST_APPEND <-- Optimized opcode
Benchmark 1: Simple List Creation
Let's verify this with hard data. We will create a list of 1 million integers, multiplying each by 2.
Test Bed:
- Python 3.12
- CPU: Apple M2 Pro
- Iterations: 50 loops
import timeit
# 1. Standard For Loop
def loop():
res = []
for i in range(1_000_000):
res.append(i * 2)
# 2. List Comprehension
def comp():
return [i * 2 for i in range(1_000_000)]
Results:
| Method | Time (Avg) | Comparison | | :--- | :--- | :--- | | For Loop | 145ms | Baseline (100%) | | List Comprehension | 112ms | ~23% Faster |
Benchmark 2: Filtering Items
Now let's add an if condition. Does the extra logic narrow the gap?
Task: keep only even numbers from 1 million items.
# Loop
if i % 2 == 0: res.append(i)
# Comp
[i for i in range(N) if i % 2 == 0]
Results:
| Method | Time (Avg) | Comparison | | :--- | :--- | :--- | | For Loop | 180ms | Baseline | | List Comprehension | 135ms | ~25% Faster |
Verdict: The layout is consistent. The comprehension handles the filtering logic purely in C-optimized constructs, maintaining its lead.
Benchmark 3: Complex Function Calls
This is where benchmarking gets tricky. If the "work" you are doing inside the loop is heavy (like parsing a JSON string, doing complex math, or a DB call), the loop overhead becomes irrelevant.
def heavy_work(x):
# Simulate expensive math
return math.sqrt(x) ** 2.5 + 5
Results:
| Method | Time (Avg) | | :--- | :--- | | For Loop | 2.54s | | List Comprehension | 2.51s |
Difference: < 1%.
Why? The CPU spent 99% of its time inside heavy_work(). Saving 30ms on append lookups is essentially a rounding error compared to the 2.5 seconds of math.
Memory Usage: The Hidden Cost
Speed isn't the only metric. Comprehensions have a fatal flaw: they are Eager.
When you run [x for x in range(10_000_000)], Python must allocate memory for 10 million pointers immediately. This can crash your server.
Let's compare it to a Generator Expression (x for x in ...), which is Lazy.
import sys
N = 1_000_000
list_comp = [x for x in range(N)]
gen_exp = (x for x in range(N))
print(f"List Size: {sys.getsizeof(list_comp) / 1024 / 1024:.2f} MB")
print(f"Gen Size: {sys.getsizeof(gen_exp)} Bytes")
Results:
| Method | Memory Usage | | :--- | :--- | | List Comprehension | 8.5 MB | | Generator Expression | 104 Bytes |
The Trade-off: Generators are infinitely smaller in memory, but they are slightly slower to iterate over than a pre-built list.
When Speed Doesn't Matter
It is easy to get obsessed with these numbers. "I must use comprehensions everywhere because they are 20% faster!"
Stop.
In web development (Django/Flask/FastAPI), your bottleneck is almost always:
- Database Queries (SQL)
- Network I/O
Changing a loop from 0.1ms to 0.08ms is usually "Premature Optimization".
The Real Winner: Readability. Use comprehensions because they are easier to read, not just because they are potentially faster.
FAQ: Performance Questions
Q1: Is map() ever faster?
A: Yes. If you are using a built-in C function like map(str, numbers), it can be faster than [str(x) for x in numbers] because it avoids the Python-layer loop entirely.
Q2: Did Python 3.11 make loops faster?
A: Yes. Python 3.11 introduced "Specializing Adaptive Interpreter" which optimizes repeated bytecode access. This reduced the gap between loops and comprehensions.
Test Your Knowledge
Summary
- Simple Loops: List Comprehension is ~20% faster.
- Complex Logic: Speed is basically equal.
- Memory Constraints: Generator Expressions are superior.
- Readability: Wins every time.
Next Steps: Now that you know the performance, learn how to use them effectively: