optimizing-python-performance
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Optimizes Python library performance through profiling (cProfile, PyInstrument), memory analysis (memray, tracemalloc), benchmarking (pytest-benchmark), and optimization strategies. Use when analyzing performance bottlenecks, finding memory leaks, or setting up performance regression testing.
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View Translation Comparison →Python Performance Optimization
Profiling Quick Start
bash
# PyInstrument (statistical, readable output)
python -m pyinstrument script.py
# cProfile (detailed, built-in)
python -m cProfile -s cumulative script.py
# Memory profiling
pip install memray
memray run script.py
memray flamegraph memray-*.binPyInstrument Usage
python
from pyinstrument import Profiler
profiler = Profiler()
profiler.start()
result = my_function()
profiler.stop()
print(profiler.output_text(unicode=True, color=True))Memory Analysis
python
import tracemalloc
tracemalloc.start()
# ... code ...
snapshot = tracemalloc.take_snapshot()
for stat in snapshot.statistics('lineno')[:10]:
print(stat)Benchmarking (pytest-benchmark)
python
def test_encode_benchmark(benchmark):
result = benchmark(encode, 37.7749, -122.4194)
assert len(result) == 12bash
pytest tests/ --benchmark-only
pytest tests/ --benchmark-compareCommon Optimizations
python
# Use set for membership (O(1) vs O(n))
valid = set(items)
if item in valid: ...
# Use deque for queue operations
from collections import deque
queue = deque()
queue.popleft() # O(1) vs list.pop(0) O(n)
# Use generators for large data
def process(items):
for item in items:
yield transform(item)
# Cache expensive computations
from functools import lru_cache
@lru_cache(maxsize=1000)
def expensive(x):
return compute(x)
# String building
result = "".join(str(x) for x in items) # Not += in loopAlgorithm Complexity
| Operation | list | set | dict |
|---|---|---|---|
| Lookup | O(n) | O(1) | O(1) |
| Insert | O(1) | O(1) | O(1) |
| Delete | O(n) | O(1) | O(1) |
For detailed strategies, see:
- PROFILING.md - Advanced profiling techniques
- BENCHMARKS.md - CI benchmark regression testing
Optimization Checklist
Before Optimizing:
- [ ] Confirm there's a real problem
- [ ] Profile to find actual bottleneck
- [ ] Establish baseline measurements
Process:
- [ ] Algorithm improvements first
- [ ] Then data structures
- [ ] Then implementation details
- [ ] Measure after each change
After:
- [ ] Add benchmarks to prevent regression
- [ ] Verify correctness unchanged
- [ ] Document why optimization neededLearn More
This skill is based on the Performance section of the Guide to Developing High-Quality Python Libraries by Will McGinnis.