How to copy a dictionary and only edit the copy

Cloning Dictionaries: it's a deep(shallow) matter!

The art of copying in Python is full of surprises. While there is more than one way to copy a dictionary, choosing the correct one can be the difference between a correctly running program and one that keeps barfing errors 🤮.

Shallow and deep copying under the microscope

When Python shallow copies a dictionary, it only clones the contents of the top-level dictionary, leaving references to nested structures intact:

• Convenient for dictionaries with simple, immutable types like numbers, strings, and tuples.
• Could cause troubles with mutable types hidden in the dictionary's layers.

At the other end of the spectrum, Python deep copying is like an archaeologist, unearthing every layer:

• The excavation reaches every mutable object, achieving complete independence from the original dictionary.
• It's the Swiss army knife 🗡️ against unintended side-effects when mutating any part of the copied dictionary.

Meet the Unpackers: {**dict1}

Python provides a stylish {**dict1} notation for unpacking an existing dictionary into a new one:

dict1 = {'a': 1, 'b': 2}
dict2 = {**dict1}
dict2['b'] = 100  # dict1 has no idea about this mutation! 
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It's a shallow copy, quick and effective, as long as you're not dealing with the nested devils.

The memory compass: id()

The id() function is your compass 🧭 in Python: it points to where variables live. It helps you confirm memory independence between objects :

print(id(dict1) == id(dict2))  # If False, they live in different memory continents.
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This understanding of shared references and memory addresses is invaluable for avoiding unintended entanglements.

The hidden pitfalls of references

Surprises are great for birthdays 🎂, not so much for code debugging. Understanding how Python uses mutable objects can save you from unnecessary surprise parties thrown by your code.

The Trojan horse of assignments

Assignment operations, such as dict2 = dict1, could be pesky Trojan horses! They don't deliver a copy. Instead, deliveries are shared references:

• Changes to dict2 add unwanted presents to dict1.
• Think of it as clone wars where every side bears the scars!

Be the master of mutations

The challenge hidden behind Python's flexibility with dictionaries is the risk of unintended mutations. The strong weapon against it? Independent copies!

Learning how to safely handle mutations can keep your dictionaries unscathed and your code bug-free. For this, you need an arsenal of effective copying strategies.

The winning strategies

Winning in dictionary copying is all about getting the right strategies in place:

1. Identify the battlefield: Get to know your data. Are we facing immutables or dealing with mutables?
2. Choose the right weapon: Pick wisely between dict.copy() and copy.deepcopy().
3. Test your conquest: Ensure copying independence with id().

Taking a deeper dive

Delving deeper into the rabbit hole, let us explore some complex aspects that will elevate your understanding of dictionary handling techniques in Python.

The power of PEP 448

Python's PEP 448 is an important document to reference for understanding dictionary copying. It offers in-depth insights on the impactful {**dict1} copying method.

The trade-off of deepcopying

While copy.deepcopy() is helpful, it comes with costs. It requires more memory and computational time⏲️. So, before you get a deep copy, ask yourself — can I afford it?

Defensive techniques to the rescue

Navigating Python's mutable objects is safer when you adopt defensive programming techniques:

• Immutable Defaults: Create functions with immutable objects as default values.
• Copy on Write: Clone objects when you intend to modify them.