Is creating deep copy takes more money and cause performance issues?

Creating a deep copy can be slower and consume more memory, which may impact performance, especially for large or complex data structures. However, the exact impact depends on the size and depth of the dictionary.

Performance and Cost Considerations of deepcopy()

1. More Memory Usage

deepcopy() creates new independent objects for everything, increasing memory consumption.

If the dictionary is large, this can be expensive in terms of RAM.

2. Slower Execution

Since deepcopy() recursively copies every element, it takes more CPU time compared to a shallow copy (copy()).

The deeper and larger the structure, the more time it takes.

3. Can Cause Performance Issues in Large-Scale Applications

If you're working with big data, high-frequency operations, or real-time systems, deep copying might slow things down.

Example: Copying a dictionary with millions of nested objects can significantly affect performance.

Alternatives to Improve Performance

Use Shallow Copy (dict.copy()) if you don’t need to modify nested structures.

Manually Copy Only Needed Parts to avoid unnecessary duplication.

Use Immutable Data Structures (like frozenset or dataclasses) to prevent unwanted changes instead of deep copying.

Optimize Data Storage by using references wisely instead of making full copies.

When to Use Deep Copy?

-- If you need a fully independent copy of a dictionary with nested structures.

-- Avoid it for large datasets unless necessary—try shallow copy or restructuring data instead.