Python Performance Optimization

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

When to Use This Skill

Identifying performance bottlenecks in Python applications
Reducing application latency and response times
Optimizing CPU-intensive operations
Reducing memory consumption and memory leaks
Improving database query performance
Optimizing I/O operations
Speeding up data processing pipelines
Implementing high-performance algorithms
Profiling production applications

Core Concepts

1. Profiling Types

CPU Profiling: Identify time-consuming functions
Memory Profiling: Track memory allocation and leaks
Line Profiling: Profile at line-by-line granularity
Call Graph: Visualize function call relationships

2. Performance Metrics

Execution Time: How long operations take
Memory Usage: Peak and average memory consumption
CPU Utilization: Processor usage patterns
I/O Wait: Time spent on I/O operations

3. Optimization Strategies

Algorithmic: Better algorithms and data structures
Implementation: More efficient code patterns
Parallelization: Multi-threading/processing
Caching: Avoid redundant computation
Native Extensions: C/Rust for critical paths

Quick Start

Basic Timing

import time

def measure_time():
    """Simple timing measurement."""
    start = time.time()

    # Your code here
    result = sum(range(1000000))

    elapsed = time.time() - start
    print(f"Execution time: {elapsed:.4f} seconds")
    return result

# Better: use timeit for accurate measurements
import timeit

execution_time = timeit.timeit(
    "sum(range(1000000))",
    number=100
)
print(f"Average time: {execution_time/100:.6f} seconds")

Profiling Tools

Pattern 1: cProfile - CPU Profiling

import cProfile
import pstats
from pstats import SortKey

def slow_function():
    """Function to profile."""
    total = 0
    for i in range(1000000):
        total += i
    return total

def another_function():
    """Another function."""
    return [i**2 for i in range(100000)]

def main():
    """Main function to profile."""
    result1 = slow_function()
    result2 = another_function()
    return result1, result2

# Profile the code
if __name__ == "__main__":
    profiler = cProfile.Profile()
    profiler.enable()

    main()

    profiler.disable()

    # Print stats
    stats = pstats.Stats(profiler)
    stats.sort_stats(SortKey.CUMULATIVE)
    stats.print_stats(10)  # Top 10 functions

    # Save to file for later analysis
    stats.dump_stats("profile_output.prof")

Command-line profiling:

# Profile a script
python -m cProfile -o output.prof script.py

# View results
python -m pstats output.prof
# In pstats:
# sort cumtime
# stats 10

Pattern 2: line_profiler - Line-by-Line Profiling

# Install: pip install line-profiler

# Add @profile decorator (line_profiler provides this)
@profile
def process_data(data):
    """Process data with line profiling."""
    result = []
    for item in data:
        processed = item * 2
        result.append(processed)
    return result

# Run with:
# kernprof -l -v script.py

Manual line profiling:

from line_profiler import LineProfiler

def process_data(data):
    """Function to profile."""
    result = []
    for item in data:
        processed = item * 2
        result.append(processed)
    return result

if __name__ == "__main__":
    lp = LineProfiler()
    lp.add_function(process_data)

    data = list(range(100000))

    lp_wrapper = lp(process_data)
    lp_wrapper(data)

    lp.print_stats()

Pattern 3: memory_profiler - Memory Usage

# Install: pip install memory-profiler

from memory_profiler import profile

@profile
def memory_intensive():
    """Function that uses lots of memory."""
    # Create large list
    big_list = [i for i in range(1000000)]

    # Create large dict
    big_dict = {i: i**2 for i in range(100000)}

    # Process data
    result = sum(big_list)

    return result

if __name__ == "__main__":
    memory_intensive()

# Run with:
# python -m memory_profiler script.py

Pattern 4: py-spy - Production Profiling

# Install: pip install py-spy

# Profile a running Python process
py-spy top --pid 12345

# Generate flamegraph
py-spy record -o profile.svg --pid 12345

# Profile a script
py-spy record -o profile.svg -- python script.py

# Dump current call stack
py-spy dump --pid 12345

Optimization Patterns

Pattern 5: List Comprehensions vs Loops

import timeit

# Slow: Traditional loop
def slow_squares(n):
    """Create list of squares using loop."""
    result = []
    for i in range(n):
        result.append(i**2)
    return result

# Fast: List comprehension
def fast_squares(n):
    """Create list of squares using comprehension."""
    return [i**2 for i in range(n)]

# Benchmark
n = 100000

slow_time = timeit.timeit(lambda: slow_squares(n), number=100)
fast_time = timeit.timeit(lambda: fast_squares(n), number=100)

print(f"Loop: {slow_time:.4f}s")
print(f"Comprehension: {fast_time:.4f}s")
print(f"Speedup: {slow_time/fast_time:.2f}x")

# Even faster for simple operations: map
def faster_squares(n):
    """Use map for even better performance."""
    return list(map(lambda x: x**2, range(n)))

Pattern 6: Generator Expressions for Memory

import sys

def list_approach():
    """Memory-intensive list."""
    data = [i**2 for i in range(1000000)]
    return sum(data)

def generator_approach():
    """Memory-efficient generator."""
    data = (i**2 for i in range(1000000))
    return sum(data)

# Memory comparison
list_data = [i for i in range(1000000)]
gen_data = (i for i in range(1000000))

print(f"List size: {sys.getsizeof(list_data)} bytes")
print(f"Generator size: {sys.getsizeof(gen_data)} bytes")

# Generators use constant memory regardless of size

Pattern 7: String Concatenation

import timeit

def slow_concat(items):
    """Slow string concatenation."""
    result = ""
    for item in items:
        result += str(item)
    return result

def fast_concat(items):
    """Fast string concatenation with join."""
    return "".join(str(item) for item in items)

def faster_concat(items):
    """Even faster with list."""
    parts = [str(item) for item in items]
    return "".join(parts)

items = list(range(10000))

# Benchmark
slow = timeit.timeit(lambda: slow_concat(items), number=100)
fast = timeit.timeit(lambda: fast_concat(items), number=100)
faster = timeit.timeit(lambda: faster_concat(items), number=100)

print(f"Concatenation (+): {slow:.4f}s")
print(f"Join (generator): {fast:.4f}s")
print(f"Join (list): {faster:.4f}s")

Pattern 8: Dictionary Lookups vs List Searches

import timeit

# Create test data
size = 10000
items = list(range(size))
lookup_dict = {i: i for i in range(size)}

def list_search(items, target):
    """O(n) search in list."""
    return target in items

def dict_search(lookup_dict, target):
    """O(1) search in dict."""
    return target in lookup_dict

target = size - 1  # Worst case for list

# Benchmark
list_time = timeit.timeit(
    lambda: list_search(items, target),
    number=1000
)
dict_time = timeit.timeit(
    lambda: dict_search(lookup_dict, target),
    number=1000
)

print(f"List search: {list_time:.6f}s")
print(f"Dict search: {dict_time:.6f}s")
print(f"Speedup: {list_time/dict_time:.0f}x")

Pattern 9: Local Variable Access

import timeit

# Global variable (slow)
GLOBAL_VALUE = 100

def use_global():
    """Access global variable."""
    total = 0
    for i in range(10000):
        total += GLOBAL_VALUE
    return total

def use_local():
    """Use local variable."""
    local_value = 100
    total = 0
    for i in range(10000):
        t

python-performance-optimization

Como adicionar

Cole no README do seu repo

Skills relacionadas

security-research

top-web-vulnerabilities

antigravity-workflows

vulnerability-scanner

Receba novas skills de Segurança toda segunda