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Python Performance Toolkit
Profiling tools, caching strategies, memory optimization, async patterns, and Cython/Numba acceleration guides.
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python-performance-toolkit/
├── LICENSE
├── README.md
├── configs/
│ └── benchmark_config.yaml
├── examples/
│ ├── optimize_api.py
│ └── optimize_data_processing.py
├── guides/
│ └── python-performance-guide.md
├── src/
│ ├── benchmarks/
│ │ ├── benchmark_runner.py
│ │ └── decorators.py
│ ├── optimizers/
│ │ ├── batch_processing.py
│ │ ├── caching.py
│ │ ├── data_structures.py
│ │ └── lazy_loading.py
│ └── profilers/
│ ├── cpu_profiler.py
│ ├── line_profiler.py
│ └── memory_profiler.py
└── tests/
├── test_benchmarks.py
└── test_profilers.py
📖 Documentation Preview README excerpt
Python Performance Toolkit
Profile, benchmark, and optimize your Python code with confidence.
Stop guessing where your bottlenecks are. Measure, compare, and ship faster code.
[](https://datanest.dev)
[](https://python.org)
[](LICENSE)
What You Get
- CPU Profiler — cProfile wrapper with flamegraph-compatible output
- Memory Profiler — tracemalloc-based leak detection and allocation tracking
- Line Profiler — Decorator-based line-by-line execution timing
- Benchmark Runner — Statistical benchmark suite with comparison reports
- Caching Strategies — LRU, TTL, memoize, and disk-backed cache
- Lazy Loading — Lazy properties, deferred imports, and computation
- Batch Processing — Chunked iteration, parallel map, async gather
- Optimized Data Structures — SortedList, typed containers, and more
- Real-World Examples — Before/after optimization of APIs and data pipelines
File Tree
python-performance-toolkit/
├── README.md
├── manifest.json
├── LICENSE
├── src/
│ ├── profilers/
│ │ ├── cpu_profiler.py
│ │ ├── memory_profiler.py
│ │ └── line_profiler.py
│ ├── benchmarks/
│ │ ├── benchmark_runner.py
│ │ └── decorators.py
│ └── optimizers/
│ ├── caching.py
│ ├── lazy_loading.py
│ ├── batch_processing.py
│ └── data_structures.py
├── examples/
│ ├── optimize_api.py
│ └── optimize_data_processing.py
├── configs/
│ └── benchmark_config.yaml
├── tests/
│ ├── test_profilers.py
│ └── test_benchmarks.py
└── guides/
└── python-performance-guide.md
Getting Started
Profile a function
... continues with setup instructions, usage examples, and more.
📄 Code Sample .py preview
src/profilers/cpu_profiler.py"""CPU profiler with cProfile wrapper and flamegraph-compatible output.
Provides a context-manager interface for profiling code blocks,
with export to sorted stats and folded flamegraph format.
"""
from __future__ import annotations
import cProfile
import io
import pstats
from contextlib import contextmanager
from dataclasses import dataclass, field
from pathlib import Path
from typing import Generator, Optional
@dataclass
class ProfileResult:
"""Container for profiling results."""
total_calls: int = 0
total_time: float = 0.0
top_functions: list[dict[str, float]] = field(default_factory=list)
class CPUProfiler:
"""cProfile-based CPU profiler with flamegraph export.
Usage:
profiler = CPUProfiler()
with profiler.profile():
expensive_function()
profiler.print_stats(top_n=20)
profiler.export_flamegraph("output.folded")
"""
def __init__(self) -> None:
self._profiler: Optional[cProfile.Profile] = None
self._stats: Optional[pstats.Stats] = None