Performance Basics

11 examples to get you started with Performance - 8 basic and 3 intermediate.

Prerequisites

Python 3.14.0
Standard library modules: timeit, cProfile, tracemalloc
Optional: uv add numpy for vectorization examples

Basic Examples

1. Measure Before Optimizing

Never optimize without a baseline measurement.

import time
 
def slow_sum(n: int) -> int:
    return sum(range(n))
 
start = time.perf_counter()
result = slow_sum(1_000_000)
elapsed = time.perf_counter() - start
print(f"Result: {result}, Time: {elapsed:.4f}s")

time.perf_counter() is monotonic and high resolution
Record baseline before any optimization attempt
Compare after changes to verify improvement
If the measurement is noisy, run multiple iterations

Related: timeit & Microbenchmarks - precise timing

2. timeit for Microbenchmarks

The timeit module runs code in tight loops for stable timing.

import timeit
 
elapsed = timeit.timeit("sum(range(1000))", number=10000)
print(f"Average: {elapsed / 10000 * 1e6:.2f} μs")

number controls loop iterations
Isolates the statement from setup overhead
Use for comparing two approaches on small code paths
Not suitable for I/O-bound operations

Related: timeit & Microbenchmarks - methodology

3. cProfile for CPU Hotspots

Find which functions consume the most CPU time.

import cProfile
import pstats
 
def work():
    sum(i * i for i in range(100_000))
 
cProfile.run("work()", "profile.out")
stats = pstats.Stats("profile.out")
stats.sort_stats("cumulative").print_stats(10)

cumulative sorts by total time including subcalls
Top entries are optimization candidates
Profile real workloads, not toy examples
Save stats to file for comparison across runs

Related: cProfile & Profilers - flame graphs

4. tracemalloc for Memory

Track memory allocations to find leaks and bloat.

import tracemalloc
 
tracemalloc.start()
data = [bytearray(1024) for _ in range(10_000)]
snapshot = tracemalloc.take_snapshot()
top = snapshot.statistics("lineno")[:5]
for stat in top:
    print(stat)
tracemalloc.stop()

tracemalloc is stdlib - no install needed
Shows which line allocated the most memory
Compare snapshots before/after to find leaks
Lighter weight than memray for quick checks

Related: Memory Profiling - memray deep dive

5. List Comprehension vs Loop

Comprehensions are faster for simple transforms.

# Faster
squares = [x * x for x in range(100_000)]
 
# Slower
squares = []
for x in range(100_000):
    squares.append(x * x)

Comprehensions avoid repeated list.append lookups
For complex logic, readability may outweigh the speed gain
Profile before assuming comprehension wins
Generator expressions save memory for large sequences

6. functools.lru_cache

Cache pure function results to avoid redundant computation.

from functools import lru_cache
 
@lru_cache(maxsize=256)
def fibonacci(n: int) -> int:
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)
 
print(fibonacci(100))  # instant with cache

Only works for functions with hashable arguments
maxsize bounds memory usage
Thread-safe in CPython
Use .cache_info() to inspect hit rate

Related: Caching & Memoization - cache strategies

7. Generator vs List

Generators yield items lazily, saving memory on large datasets.

def read_lines(path: str):
    with open(path) as f:
        for line in f:
            yield line.strip()
 
# Processes one line at a time - constant memory
for line in read_lines("huge.log"):
    if "ERROR" in line:
        print(line)

Lists load everything into memory
Generators process one item at a time
Use generators for file I/O and pipeline stages
sum(1 for _ in gen) still consumes the generator once

8. Avoid Repeated Attribute Lookup

Bind frequently accessed attributes to local variables in hot loops.

# Faster in tight loops
append = results.append
for item in data:
    append(transform(item))
 
# Slower - repeated attribute lookup
for item in data:
    results.append(transform(item))

Local variable access is faster than attribute lookup
Matters only in innermost hot loops
Readability trade-off - apply only where profiled
Modern Python optimizes many cases automatically

Intermediate Examples

9. NumPy Vectorization

Replace Python loops with array operations for numeric work.

import numpy as np
 
data = np.arange(1_000_000, dtype=np.float64)
result = np.sqrt(data) * 2.0 + 1.0  # vectorized
# vs: [math.sqrt(x) * 2.0 + 1.0 for x in data]  # 10-100x slower

NumPy operations run in C/Fortran under the hood
Requires contiguous numeric data
pandas and Polars build on similar vectorization
Conversion overhead matters for small arrays

Related: Vectorization with NumPy - full guide

10. asyncio for I/O-Bound Work

Parallelize waiting on network/disk without threads.

import asyncio
import httpx
 
async def fetch_all(urls: list[str]) -> list[int]:
    async with httpx.AsyncClient() as client:
        tasks = [client.get(url) for url in urls]
        responses = await asyncio.gather(*tasks)
        return [r.status_code for r in responses]
 
asyncio.run(fetch_all(["https://example.com"] * 10))

asyncio helps when code waits on I/O, not CPU
Does not bypass the GIL for CPU-bound work
Use asyncio.gather for concurrent coroutines
For CPU parallelism, use multiprocessing or concurrent.futures

Related: Async & Concurrency for Throughput - patterns

11. Performance Audit Workflow

A systematic pass before claiming something is "optimized."

# 1. Measure baseline
# 2. Profile (cProfile or py-spy)
# 3. Fix the top hotspot only
# 4. Re-measure
# 5. Document the before/after numbers
 
baseline_ms = 450
optimized_ms = 120
improvement = (baseline_ms - optimized_ms) / baseline_ms * 100
print(f"Improvement: {improvement:.0f}%")  # 73%

Optimize the measured bottleneck, not a guess
One change at a time for clear attribution
Keep a log of optimization decisions
Stop when performance meets requirements

Related: Performance Audit Checklist - full checklist

Stack versions: This page was written for Python 3.14.0, FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Performance Basics

11 examples to get you started with Performance - 8 basic and 3 intermediate.

Prerequisites

Python 3.14.0
Standard library modules: timeit, cProfile, tracemalloc
Optional: uv add numpy for vectorization examples

Basic Examples

1. Measure Before Optimizing

Never optimize without a baseline measurement.

import time
 
def slow_sum(n: int) -> int:
    return sum(range(n))
 
start = time.perf_counter()
result = slow_sum(1_000_000)
elapsed = time.perf_counter() - start
print(f"Result: {result}, Time: {elapsed:.4f}s")

time.perf_counter() is monotonic and high resolution
Record baseline before any optimization attempt
Compare after changes to verify improvement
If the measurement is noisy, run multiple iterations

Related: timeit & Microbenchmarks - precise timing

2. timeit for Microbenchmarks

The timeit module runs code in tight loops for stable timing.

import timeit
 
elapsed = timeit.timeit("sum(range(1000))", number=10000)
print(f"Average: {elapsed / 10000 * 1e6:.2f} μs")

number controls loop iterations
Isolates the statement from setup overhead
Use for comparing two approaches on small code paths
Not suitable for I/O-bound operations

Related: timeit & Microbenchmarks - methodology

3. cProfile for CPU Hotspots

Find which functions consume the most CPU time.

import cProfile
import pstats
 
def work():
    sum(i * i for i in range(100_000))
 
cProfile.run("work()", "profile.out")
stats = pstats.Stats("profile.out")
stats.sort_stats("cumulative").print_stats(10)

cumulative sorts by total time including subcalls
Top entries are optimization candidates
Profile real workloads, not toy examples
Save stats to file for comparison across runs

Related: cProfile & Profilers - flame graphs

4. tracemalloc for Memory

Track memory allocations to find leaks and bloat.

import tracemalloc
 
tracemalloc.start()
data = [bytearray(1024) for _ in range(10_000)]
snapshot = tracemalloc.take_snapshot()
top = snapshot.statistics("lineno")[:5]
for stat in top:
    print(stat)
tracemalloc.stop()

tracemalloc is stdlib - no install needed
Shows which line allocated the most memory
Compare snapshots before/after to find leaks
Lighter weight than memray for quick checks

Related: Memory Profiling - memray deep dive

5. List Comprehension vs Loop

Comprehensions are faster for simple transforms.

# Faster
squares = [x * x for x in range(100_000)]
 
# Slower
squares = []
for x in range(100_000):
    squares.append(x * x)

Comprehensions avoid repeated list.append lookups
For complex logic, readability may outweigh the speed gain
Profile before assuming comprehension wins
Generator expressions save memory for large sequences

6. functools.lru_cache

Cache pure function results to avoid redundant computation.

from functools import lru_cache
 
@lru_cache(maxsize=256)
def fibonacci(n: int) -> int:
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)
 
print(fibonacci(100))  # instant with cache

Only works for functions with hashable arguments
maxsize bounds memory usage
Thread-safe in CPython
Use .cache_info() to inspect hit rate

Related: Caching & Memoization - cache strategies

7. Generator vs List

Generators yield items lazily, saving memory on large datasets.

def read_lines(path: str):
    with open(path) as f:
        for line in f:
            yield line.strip()
 
# Processes one line at a time - constant memory
for line in read_lines("huge.log"):
    if "ERROR" in line:
        print(line)

Lists load everything into memory
Generators process one item at a time
Use generators for file I/O and pipeline stages
sum(1 for _ in gen) still consumes the generator once

8. Avoid Repeated Attribute Lookup

Bind frequently accessed attributes to local variables in hot loops.

# Faster in tight loops
append = results.append
for item in data:
    append(transform(item))
 
# Slower - repeated attribute lookup
for item in data:
    results.append(transform(item))

Local variable access is faster than attribute lookup
Matters only in innermost hot loops
Readability trade-off - apply only where profiled
Modern Python optimizes many cases automatically

Intermediate Examples

9. NumPy Vectorization

Replace Python loops with array operations for numeric work.

import numpy as np
 
data = np.arange(1_000_000, dtype=np.float64)
result = np.sqrt(data) * 2.0 + 1.0  # vectorized
# vs: [math.sqrt(x) * 2.0 + 1.0 for x in data]  # 10-100x slower

NumPy operations run in C/Fortran under the hood
Requires contiguous numeric data
pandas and Polars build on similar vectorization
Conversion overhead matters for small arrays

Related: Vectorization with NumPy - full guide

10. asyncio for I/O-Bound Work

Parallelize waiting on network/disk without threads.

import asyncio
import httpx
 
async def fetch_all(urls: list[str]) -> list[int]:
    async with httpx.AsyncClient() as client:
        tasks = [client.get(url) for url in urls]
        responses = await asyncio.gather(*tasks)
        return [r.status_code for r in responses]
 
asyncio.run(fetch_all(["https://example.com"] * 10))

asyncio helps when code waits on I/O, not CPU
Does not bypass the GIL for CPU-bound work
Use asyncio.gather for concurrent coroutines
For CPU parallelism, use multiprocessing or concurrent.futures

Related: Async & Concurrency for Throughput - patterns

11. Performance Audit Workflow

A systematic pass before claiming something is "optimized."

# 1. Measure baseline
# 2. Profile (cProfile or py-spy)
# 3. Fix the top hotspot only
# 4. Re-measure
# 5. Document the before/after numbers
 
baseline_ms = 450
optimized_ms = 120
improvement = (baseline_ms - optimized_ms) / baseline_ms * 100
print(f"Improvement: {improvement:.0f}%")  # 73%

Optimize the measured bottleneck, not a guess
One change at a time for clear attribution
Keep a log of optimization decisions
Stop when performance meets requirements

Related: Performance Audit Checklist - full checklist

Stack versions: This page was written for Python 3.14.0, FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Performance Basics

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Performance Basics