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Select appropriate built-in data structures and algorithmic patterns that eliminate manual checks and reduce code complexity. This improves both performance and maintainability by leveraging Python’s optimized implementations.

Key principles:

1. Use defaultdict instead of manual key existence checks - Replace patterns like if key not in dict: dict[key] = [] with defaultdict(list)
2. Try direct operations before fallback logic - Attempt the primary algorithm first, then handle edge cases, rather than pre-checking conditions
3. Leverage built-in data structure behaviors - Use data structures that naturally handle your use case rather than implementing the logic manually

Example from the codebase:

# Instead of manual key checking:
if location not in key_mapping:
    key_mapping[location] = [key]
    start_mapping[location] = [start] 
    end_mapping[location] = [end]

# Use defaultdict to eliminate the check:
from collections import defaultdict
block_mapping: defaultdict = defaultdict(list)
block_mapping[location].append((key, start, end))

For memory management algorithms, try allocation first before implementing eviction logic:

# Try direct allocation first
memory_obj = self.memory_allocator.allocate(shape, dtype)
if memory_obj is None:
    # Then implement eviction logic
    self._evict_and_retry_allocation(shape, dtype)

This approach reduces branching, leverages optimized implementations, and makes code more readable and less error-prone.