Choose appropriate built-in data structures and collection types to improve code efficiency and readability. Python’s collections module and built-in data structures often provide more elegant and performant solutions than manual implementations.
Key guidelines:
collections.UserDict instead of inheriting from dict when you need to override dictionary behaviorcollections.defaultdict to eliminate manual key existence checks and initializationExamples:
# Instead of manual dictionary building:
target_nodes: dict[str, list[GraphEdge]] = {}
for edge in edge_mappings:
if edge.target_node_id not in target_nodes:
target_nodes[edge.target_node_id] = []
target_nodes[edge.target_node_id].append(edge)
# Use defaultdict:
from collections import defaultdict
target_nodes = defaultdict(list)
for edge in edge_mappings:
target_nodes[edge.target_node_id].append(edge)
# For deduplication, instead of manual loop:
unique_documents = []
doc_ids = set()
for document in documents:
if document.metadata["doc_id"] not in doc_ids:
doc_ids.add(document.metadata["doc_id"])
unique_documents.append(document)
# Use dictionary comprehension:
unique_documents = list({doc.metadata["doc_id"]: doc for doc in documents}.values())
These optimizations reduce code complexity, improve performance, and make intent clearer to other developers.
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