Optimizes Python library performance using profiling (cProfile, PyInstrument), memory analysis (memray, tracemalloc), benchmarking (pytest-benchmark), and strategies. Use for bottlenecks, leaks, regression testing.
npx claudepluginhub wdm0006/python-skills --plugin python-library-distributionThis skill uses the workspace's default tool permissions.
```bash
Applies Acme Corporation brand guidelines including colors, fonts, layouts, and messaging to generated PowerPoint, Excel, and PDF documents.
Builds DCF models with sensitivity analysis, Monte Carlo simulations, and scenario planning for investment valuation and risk assessment.
Calculates profitability (ROE, margins), liquidity (current ratio), leverage, efficiency, and valuation (P/E, EV/EBITDA) ratios from financial statements in CSV, JSON, text, or Excel for investment analysis.
# 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-*.bin
from pyinstrument import Profiler
profiler = Profiler()
profiler.start()
result = my_function()
profiler.stop()
print(profiler.output_text(unicode=True, color=True))
import tracemalloc
tracemalloc.start()
# ... code ...
snapshot = tracemalloc.take_snapshot()
for stat in snapshot.statistics('lineno')[:10]:
print(stat)
def test_encode_benchmark(benchmark):
result = benchmark(encode, 37.7749, -122.4194)
assert len(result) == 12
pytest tests/ --benchmark-only
pytest tests/ --benchmark-compare
# 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 loop
| 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:
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 needed
This skill is based on the Performance section of the Guide to Developing High-Quality Python Libraries by Will McGinnis. See these posts for deeper coverage: