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Python Performance Toolkit
Profiling tools, caching strategies, memory optimization, async patterns, and Cython/Numba acceleration guides.
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📁 File Structure 18 files
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/benchmarks/benchmark_runner.py
"""Benchmark runner with statistical analysis and comparison reporting.
Run benchmark suites, collect timing data, compute statistics,
and generate comparison reports across multiple implementations.
"""
from __future__ import annotations
import gc
import statistics
import time
from dataclasses import dataclass, field
from typing import Any, Callable, Optional
@dataclass
class BenchmarkResult:
"""Statistical results for a single benchmark."""
name: str
iterations: int
times_ns: list[float] = field(default_factory=list, repr=False)
@property
def mean_ms(self) -> float:
return statistics.mean(self.times_ns) / 1_000_000
@property
def median_ms(self) -> float:
return statistics.median(self.times_ns) / 1_000_000
@property
def stdev_ms(self) -> float:
return statistics.stdev(self.times_ns) / 1_000_000 if len(self.times_ns) > 1 else 0.0
@property
def min_ms(self) -> float:
return min(self.times_ns) / 1_000_000
@property
def max_ms(self) -> float:
return max(self.times_ns) / 1_000_000
@property
def p95_ms(self) -> float:
sorted_times = sorted(self.times_ns)
idx = int(len(sorted_times) * 0.95)
return sorted_times[min(idx, len(sorted_times) - 1)] / 1_000_000
@property
# ... 145 more lines ...