Specific use of Python standard library copy

1. Overview of the copy library

copyModules are the core module used for object copying in the Python standard library, and provide shallow copy (copy) and deep copy (deepcopy) Two object replication mechanisms.

Application scenarios

• Data protection: prevent original data from being accidentally modified
• Complex object replication: multi-layer replication of nested data structures
• Configuration templates: Quick instantiation of template objects based on template objects
• Cache processing: Maintain the independence of cached data

2. Analysis of core methods

1. Shallow copy()

import copy

original_list = [1, [2, 3], {'a': 4}]
shallow_copied = (original_list)

# Modify the shallow copy objectshallow_copied[0] = 100       # Does not affect the original objectshallow_copied[1][0] = 200    # will affect the original object

Features：

• Only copy the object itself, not the child object
• Modification of variable sub-objects will affect the original object
• Time complexity: O(n), n is the number of top-level elements

2. Deep copy()

import copy

original_dict = {'a': [1, 2], 'b': {'c': 3}}
deep_copied = (original_dict)

# Modify the deep copy objectdeep_copied['a'][0] = 100    # Does not affect the original objectdeep_copied['b']['c'] = 300  # Does not affect the original object

Features：

• Recursively copying objects and all their child objects
• Completely independent copy, no effect on modifications
• Time complexity: O(n), n is the total number of elements at all levels
• Support customization__deepcopy__Methods implement special copy logic

3. Comparison of key technologies

4. Advanced usage skills

1. Customize copy behavior

class MyClass:
    def __init__(self, x):
         = x
    
    def __copy__(self):
        print("Execute shallow copy")
        return MyClass()
    
    def __deepcopy__(self, memo):
        print("Execute deep copy")
        return MyClass((, memo))

obj = MyClass([1,2,3])
(obj)      # Output: Perform a shallow copy(obj)  # Output: Perform deep copy

2. Performance optimization practice

# Use memo dictionary to avoid duplicate copying (deep copy optimization)memo = {}
deep_copied = (big_object, memo)

# For immutable objects, reference directly instead of copyingfrom copy import copy, deepcopy
immutable_types = (int, float, str, tuple, frozenset)

def smart_copy(obj):
    if isinstance(obj, immutable_types):
        return obj
    return deepcopy(obj)

5. Frequently Asked Questions

1. Recycle reference processing

a = [1]
b = [2]
(b)
(a)  # Create a circular reference
# Normal deep copy will overflowsafe_copy = (a)  # Automatically handle circular references

2. Copy special objects

import threading
lock = ()

# Deep copy will skip special objects such as thread lockslock_copy = (lock)  # Return the reference to the original lock

6. Best Practice Suggestions

Data selection principle：

• Flat structures use shallow copy
• Nested more than 2 layers using deep copy
• Consider chunked copying of super-large data structures

Performance benchmarking(Based on Python 3.9):

# Test a list of 10,000 elementsLight copy time consuming：0.0023s
Deep copy time consuming：0.0158s

Memory optimization tips：

# Use generator expressions to reduce memory usagelarge_list = [x for x in range(100000)]
memory_efficient_copy = list(x for x in large_list)

7. Summary

copyAs a standard solution for Python object copying, you need to pay attention to the correct use:

• Understand the essential differences between copy depth
• Select the appropriate copy method according to the characteristics of the data structure
• Targeted optimization for performance-sensitive scenarios
• Perform necessary verification when handling special objects

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