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)