Python 3.11 Best Practices

When to Apply

• 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

io-

ds-

mem-

conc-

loop-

str-

func-

py-

Table of Contents

1. 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)
2. 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)
3. 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)
4. 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)
5. 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)
6. 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())
7. 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)
8. 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

1. Python 3.11 Release Notes
2. PEP 8 Style Guide
3. Python Wiki - Performance Tips
4. Real Python - Async IO
5. Real Python - LEGB Rule
6. Real Python - String Concatenation
7. Python Tutorial - Data Structures
8. CPython Exception Handling
9. DataCamp - Python Generators
10. JetBrains - Performance Hacks