python-tooling

Master Python package management with UV, performance profiling, optimization techniques, and CI/CD integration. Use this skill for dependency management, Docker integration, code profiling, and build optimization.

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Install skill "python-tooling" with this command: npx skills add amrahman90/python-expert-agent/amrahman90-python-expert-agent-python-tooling

Python Tooling

Master Python package management with UV, performance profiling, optimization techniques, and CI/CD integration. Use this skill for dependency management, Docker integration, code profiling, and build optimization.

When to Use This Skill

  • Setting up Python projects with UV

  • Managing dependencies and virtual environments

  • Profiling Python code for performance

  • Optimizing slow code

  • Setting up CI/CD pipelines

  • Docker integration for Python apps

UV Package Manager

Installation

macOS/Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell)

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Using pip (if you already have Python)

pip install uv

Using Homebrew (macOS)

brew install uv

Quick Start

Create new project with virtual environment

uv init my-project cd my-project

Or create in current directory

uv init .

Initialize creates:

- .python-version (Python version)

- pyproject.toml (project config)

- README.md

- .gitignore

Install Dependencies

Install packages (creates venv if needed)

uv add requests pandas

Install dev dependencies

uv add --dev pytest black ruff

Install from requirements.txt

uv pip install -r requirements.txt

Install from pyproject.toml

uv sync

Virtual Environment Management

Create virtual environment with uv

uv venv

Create with specific Python version

uv venv --python 3.13

Create with custom name

uv venv my-env

Or use uv run (no activation needed)

uv run python script.py uv run pytest

Locking Dependencies

Generate uv.lock file

uv lock

Update lock file

uv lock --upgrade

Install from lockfile (exact versions)

uv sync --frozen

Check if lockfile is up to date

uv lock --check

Python Version Management

Install Python version

uv python install 3.13

Install multiple versions

uv python install 3.11 3.12 3.13

Set Python version for project

uv python pin 3.13

Project Configuration

pyproject.toml

[project] name = "my-project" version = "0.1.0" description = "My awesome project" requires-python = ">=3.13" dependencies = [ "requests>=2.31.0", "pydantic>=2.0.0", "click>=8.1.0", ]

[project.optional-dependencies] dev = [ "pytest>=7.4.0", "pytest-cov>=4.1.0", "black>=23.0.0", "ruff>=0.1.0", "mypy>=1.5.0", ]

[build-system] requires = ["hatchling"] build-backend = "hatchling.build"

[tool.uv] dev-dependencies = []

Performance Profiling

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")

cProfile - CPU Profiling

import cProfile import pstats from pstats import SortKey

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

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

line_profiler - Line-by-Line Profiling

Install: pip install line-profiler

Add @profile decorator

@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

memory_profiler - Memory Usage

Install: pip install memory-profiler

from memory_profiler import profile

@profile def memory_intensive(): """Function that uses lots of memory.""" big_list = [i for i in range(1000000)] big_dict = {i: i**2 for i in range(100000)} result = sum(big_list) return result

Run with:

python -m memory_profiler script.py

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

List Comprehensions vs Loops

Slow: Traditional loop

def slow_squares(n): result = [] for i in range(n): result.append(i**2) return result

Fast: List comprehension

def fast_squares(n): return [i**2 for i in range(n)]

Generator Expressions for Memory

Memory-intensive list

def list_approach(): data = [i**2 for i in range(1000000)] return sum(data)

Memory-efficient generator

def generator_approach(): data = (i**2 for i in range(1000000)) return sum(data)

String Concatenation

Slow string concatenation

def slow_concat(items): result = "" for item in items: result += str(item) return result

Fast string concatenation with join

def fast_concat(items): return "".join(str(item) for item in items)

Dictionary Lookups vs List Searches

O(n) search in list

def list_search(items, target): return target in items

O(1) search in dict

def dict_search(lookup_dict, target): return target in lookup_dict

Caching with functools.lru_cache

from functools import lru_cache

@lru_cache(maxsize=None) def fibonacci_fast(n): """Recursive fibonacci with caching.""" if n < 2: return n return fibonacci_fast(n-1) + fibonacci_fast(n-2)

Multiprocessing for CPU-Bound Tasks

import multiprocessing as mp

def cpu_intensive_task(n): return sum(i**2 for i in range(n))

def parallel_processing(): with mp.Pool(processes=4) as pool: results = pool.map(cpu_intensive_task, [1000000] * 4) return results

Async I/O for I/O-Bound Tasks

import asyncio import aiohttp

async def asynchronous_requests(urls: list[str]): async with aiohttp.ClientSession() as session: tasks = [async_fetch(session, url) for url in urls] results = await asyncio.gather(*tasks) return results

Database Optimization

Batch Database Operations

Slow: Individual inserts

def slow_inserts(conn, count): cursor = conn.cursor() for i in range(count): cursor.execute("INSERT INTO users (name) VALUES (?)", (f"User {i}",)) conn.commit() # Commit each insert

Fast: Batch insert with single commit

def fast_inserts(conn, count): cursor = conn.cursor() data = [(f"User {i}",) for i in range(count)] cursor.executemany("INSERT INTO users (name) VALUES (?)", data) conn.commit() # Single commit

Query Optimization

Use indexes for frequently queried columns

""" -- Slow: No index SELECT * FROM users WHERE email = 'user@example.com';

-- Fast: With index CREATE INDEX idx_users_email ON users(email); SELECT * FROM users WHERE email = 'user@example.com'; """

Use EXPLAIN QUERY PLAN

cursor.execute("EXPLAIN QUERY PLAN SELECT * FROM users WHERE email = ?", ("test@example.com",))

Memory Optimization

Detecting Memory Leaks

import tracemalloc import gc

def track_memory_usage(): tracemalloc.start()

# Take snapshot before
snapshot1 = tracemalloc.take_snapshot()

# Run code
memory_leak_example()

# Take snapshot after
snapshot2 = tracemalloc.take_snapshot()

# Compare
top_stats = snapshot2.compare_to(snapshot1, 'lineno')

print("Top 10 memory allocations:")
for stat in top_stats[:10]:
    print(stat)

tracemalloc.stop()

Force garbage collection

gc.collect()

Using slots for Memory

class RegularClass: def init(self, x, y, z): self.x = x self.y = y self.z = z

class SlottedClass: slots = ['x', 'y', 'z']

def __init__(self, x, y, z):
    self.x = x
    self.y = y
    self.z = z

Weakref for Caches

import weakref

weak_cache = weakref.WeakValueDictionary()

def get_resource_weak(key): resource = weak_cache.get(key) if resource is None: resource = CachedResource(f"Data for {key}") weak_cache[key] = resource return resource

Docker Integration

Basic Dockerfile

FROM python:3.13-slim

Install uv

COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv

WORKDIR /app

Copy dependency files

COPY pyproject.toml uv.lock ./

Install dependencies

RUN uv sync --frozen --no-dev

Copy application code

COPY . .

Run application

CMD ["uv", "run", "python", "app.py"]

Multi-stage Dockerfile

FROM python:3.13-slim AS builder

Install uv

COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv

WORKDIR /app

Install dependencies to venv

COPY pyproject.toml uv.lock ./ RUN uv sync --frozen --no-dev --no-editable

Runtime stage

FROM python:3.13-slim

WORKDIR /app

Copy venv from builder

COPY --from=builder /app/.venv .venv COPY . .

Use venv

ENV PATH="/app/.venv/bin:$PATH"

CMD ["python", "app.py"]

CI/CD Integration

GitHub Actions

name: Tests

on: [push, pull_request]

jobs: test: runs-on: ubuntu-latest

steps:
  - uses: actions/checkout@v4

  - name: Install uv
    uses: astral-sh/setup-uv@v2
    with:
      enable-cache: true

  - name: Set up Python
    run: uv python install 3.13

  - name: Install dependencies
    run: uv sync --all-extras --dev

  - name: Run tests
    run: uv run pytest

  - name: Run linting
    run: |
      uv run ruff check .
      uv run ruff format --check .

Pre-commit Hooks

.pre-commit-config.yaml

repos:

Benchmarking Tools

pytest-benchmark

Install: pip install pytest-benchmark

def test_list_comprehension(benchmark): result = benchmark(lambda: [i**2 for i in range(10000)]) assert len(result) == 10000

def test_map_function(benchmark): result = benchmark(lambda: list(map(lambda x: x**2, range(10000)))) assert len(result) == 10000

Run with: pytest test_performance.py --benchmark-compare

Custom Benchmark Decorator

import time from functools import wraps

def benchmark(func): @wraps(func) def wrapper(*args, **kwargs): start = time.perf_counter() result = func(*args, **kwargs) elapsed = time.perf_counter() - start print(f"{func.name} took {elapsed:.6f} seconds") return result return wrapper

@benchmark def slow_function(): time.sleep(0.5) return sum(range(1000000))

Best Practices

  • Profile before optimizing - Measure to find real bottlenecks

  • Focus on hot paths - Optimize code that runs most frequently

  • Use appropriate data structures - Dict for lookups, set for membership

  • Avoid premature optimization - Clarity first, then optimize

  • Use built-in functions - They're implemented in C

  • Cache expensive computations - Use lru_cache

  • Batch I/O operations - Reduce system calls

  • Use generators for large datasets

  • Consider NumPy for numerical operations

  • Profile production code - Use py-spy for live systems

References

  • UV Documentation

  • py-spy Documentation

  • memory_profiler Documentation

  • line_profiler Documentation

  • pytest-benchmark Documentation

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