Python 3.11 Best Practices

Comprehensive performance optimization guide for Python 3.11+ applications. Contains 42 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation.

When to Apply

Reference these guidelines when:

• Writing new Python async I/O code

• Choosing data structures for collections

• Optimizing memory usage in data-intensive applications

• Implementing concurrent or parallel processing

• Reviewing Python code for performance issues

Rule Categories by Priority

Priority Category Impact Prefix

1 I/O & Async Patterns CRITICAL io-

2 Data Structure Selection CRITICAL ds-

3 Memory Optimization HIGH mem-

4 Concurrency & Parallelism HIGH conc-

5 Loop & Iteration MEDIUM loop-

6 String Operations MEDIUM str-

7 Function & Call Overhead LOW-MEDIUM func-

8 Python Idioms & Micro LOW py-

Table of Contents

I/O & Async Patterns — CRITICAL

• 1.1 Defer await Until Value Needed — CRITICAL (2-5× faster for dependent operations)

• 1.2 Use aiofiles for Async File Operations — CRITICAL (prevents event loop blocking)

• 1.3 Use asyncio.gather() for Concurrent I/O — CRITICAL (2-10× throughput improvement)

• 1.4 Use Connection Pooling for Database Access — CRITICAL (100-200ms saved per connection)

• 1.5 Use Semaphores to Limit Concurrent Operations — CRITICAL (prevents resource exhaustion)

• 1.6 Use uvloop for Faster Event Loop — CRITICAL (2-4× faster async I/O)

Data Structure Selection — CRITICAL

• 2.1 Use bisect for O(log n) Sorted List Operations — CRITICAL (O(n) to O(log n) search)

• 2.2 Use defaultdict to Avoid Key Existence Checks — CRITICAL (eliminates redundant lookups)

• 2.3 Use deque for O(1) Queue Operations — CRITICAL (O(n) to O(1) for popleft)

• 2.4 Use Dict for O(1) Key-Value Lookup — CRITICAL (O(n) to O(1) lookup)

• 2.5 Use frozenset for Hashable Set Keys — CRITICAL (enables set-of-sets patterns)

• 2.6 Use Set for O(1) Membership Testing — CRITICAL (O(n) to O(1) lookup)

Memory Optimization — HIGH

• 3.1 Intern Repeated Strings to Save Memory — HIGH (reduces duplicate string storage)

• 3.2 Use slots for Memory-Efficient Classes — HIGH (20-50% memory reduction per instance)

• 3.3 Use array.array for Homogeneous Numeric Data — HIGH (4-8× memory reduction for numbers)

• 3.4 Use Generators for Large Sequences — HIGH (100-1000× memory reduction)

• 3.5 Use weakref for Caches to Prevent Memory Leaks — HIGH (prevents unbounded cache growth)

Concurrency & Parallelism — HIGH

• 4.1 Use asyncio for I/O-Bound Concurrency — HIGH (300% throughput improvement for I/O)

• 4.2 Use multiprocessing for CPU-Bound Parallelism — HIGH (4-8× speedup on multi-core systems)

• 4.3 Use Queue for Thread-Safe Communication — HIGH (prevents race conditions)

• 4.4 Use TaskGroup for Structured Concurrency — HIGH (prevents resource leaks on failure)

• 4.5 Use ThreadPoolExecutor for Blocking Calls in Async — HIGH (prevents event loop blocking)

Loop & Iteration — MEDIUM

• 5.1 Hoist Loop-Invariant Computations — MEDIUM (avoids N× redundant work)

• 5.2 Use any() and all() for Boolean Aggregation — MEDIUM (O(n) to O(1) best case)

• 5.3 Use dict.items() for Key-Value Iteration — MEDIUM (single lookup vs double lookup)

• 5.4 Use enumerate() for Index-Value Iteration — MEDIUM (cleaner code, avoids index errors)

• 5.5 Use itertools for Efficient Iteration Patterns — MEDIUM (2-3× faster iteration patterns)

• 5.6 Use List Comprehensions Over Explicit Loops — MEDIUM (2-3× faster iteration)

String Operations — MEDIUM

• 6.1 Use f-strings for Simple String Formatting — MEDIUM (20-30% faster than .format())

• 6.2 Use join() for Multiple String Concatenation — MEDIUM (4× faster for 5+ strings)

• 6.3 Use str.startswith() with Tuple for Multiple Prefixes — MEDIUM (single call vs multiple comparisons)

• 6.4 Use str.translate() for Character-Level Replacements — MEDIUM (10× faster than chained replace())

Function & Call Overhead — LOW-MEDIUM

• 7.1 Reduce Function Calls in Tight Loops — LOW-MEDIUM (100ms savings per 1M iterations)

• 7.2 Use functools.partial for Pre-Filled Arguments — LOW-MEDIUM (50% faster debugging via introspection)

• 7.3 Use Keyword-Only Arguments for API Clarity — LOW-MEDIUM (prevents positional argument errors)

• 7.4 Use lru_cache for Expensive Function Memoization — LOW-MEDIUM (avoids repeated computation)

Python Idioms & Micro — LOW

• 8.1 Leverage Zero-Cost Exception Handling — LOW (zero overhead in happy path (Python 3.11+))

• 8.2 Prefer Local Variables Over Global Lookups — LOW (faster name resolution)

• 8.3 Use dataclass for Data-Holding Classes — LOW (reduces boilerplate by 80%)

• 8.4 Use Lazy Imports for Faster Startup — LOW (10-15% faster startup)

• 8.5 Use match Statement for Structural Pattern Matching — LOW (reduces branch complexity)

• 8.6 Use Walrus Operator for Assignment in Expressions — LOW (eliminates redundant computations)

References

• Python 3.11 Release Notes

• PEP 8 Style Guide

• Python Wiki - Performance Tips

• Real Python - Async IO

• Real Python - LEGB Rule

• Real Python - String Concatenation

• Python Tutorial - Data Structures

• CPython Exception Handling

• DataCamp - Python Generators

• JetBrains - Performance Hacks