any #python reading recommendations to optimize memory use of a dict containing lists which in turn contain lots of small dicts? once build up they can be read-only.
Reduce nesting: Avoid unnecessary layers of nesting. Flatten the structure or use simpler data types like tuples when inner dictionaries have few keys.
Example: Instead of List[Dict[Any, List[Dict[Any, Any]]]], consider List[Tuple[Key, Value]].
Custom classes: Create specific classes for inner dictionaries with fixed keys to improve efficiency, readability, and maintainability.
Example: Instead of {'name': 'John', 'age': 30}, use a Person class with attributes name and age.
Use NumPy or Pandas: For numerical computations or tabular data, utilize NumPy arrays or Pandas DataFrames for faster operations.
Example: Convert a nested list into a NumPy array for mathematical operations.
Dictionaries with fixed keys: If inner dictionaries have constant keys, consider using named tuples or custom classes for better performance.
Example: Replace {'x': 1, 'y': 2} with a named tuple or class Point(x=1, y=2).
Appropriate data structures: Choose data structures based on access patterns. Use dictionaries when accessing elements by specific keys.
Example: Use a dictionary to store items with unique identifiers.
Generators or lazy evaluation: Use generators or lazy evaluation techniques to process data incrementally, saving memory and improving performance.
Example: Use a generator to read large files line-by-line.
Profiling: Before optimizing, profile your code to identify resource-intensive areas and focus on the most impactful improvements.
Example: Use Python's cProfile module to analyze code performance.