🐍2.3Mastering Python Lists: Essential Methods and Deep vs. Shallow Copy

 

Mastering Python Lists: Essential Methods and Deep vs. Shallow Copy

Lists in Python are one of the most versatile data structures, offering a variety of built-in methods to manipulate data effectively. In this blog, we'll explore some essential list methods using an example list and then delve into the difference between shallow and deep copies.

Essential List Methods with Examples

Let's start with a sample list:

# Define the list
my_list = [1, 2, 3, 3, 4, 5, ("A", "B", "B", "C", "D"), [11, 12, 13, 1, 2, 3], "DIVYA", "SANKET", "VENU"]

1. append() - Adding an element at the end

my_list.append(100)
print("After append:", my_list)

✅ Adds 100 at the end of the list.

2. extend() - Extending with another list

my_list.extend([200, 300])
print("After extend:", my_list)

✅ Adds elements [200, 300] to the list.

3. insert() - Inserting an element at a specific index

my_list.insert(2, "NEW_ELEMENT")
print("After insert:", my_list)

✅ Inserts "NEW_ELEMENT" at index 2.

4. remove() - Removing the first occurrence of an element

my_list.remove(3)
print("After remove:", my_list)

✅ Removes the first occurrence of 3.

5. pop() - Removing and returning the last element

popped_element = my_list.pop()
print("Popped Element:", popped_element)
print("After pop:", my_list)

✅ Removes and returns "VENU" (last element).

6. clear() - Clearing all elements

# my_list.clear()
# print("After clear:", my_list)

✅ Removes all elements from the list.

7. index() - Finding the index of an element

index_sanket = my_list.index("SANKET")
print("Index of SANKET:", index_sanket)

✅ Returns the index of "SANKET".

8. count() - Counting occurrences of an element

count_3 = my_list.count(3)
print("Count of 3:", count_3)

✅ Counts occurrences of 3 in the list.

9. sort() - Sorting a list

numeric_list = [5, 1, 3, 2, 4]
numeric_list.sort()
print("After sort:", numeric_list)

✅ Works only for lists with the same data type.

10. reverse() - Reversing the list

my_list.reverse()
print("After reverse:", my_list)

✅ Reverses the order of elements in the list.

11. copy() - Creating a shallow copy of the list

copied_list = my_list.copy()
print("Copied List:", copied_list)

✅ Creates a duplicate of my_list without modifying the original.

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Deep Copy vs. Shallow Copy in Python

When copying lists, there are two primary types:

1. Shallow Copy (copy()) → Creates a new list but keeps references to nested elements.
2. Deep Copy (copy.deepcopy()) → Creates a completely independent copy, including nested lists.

Shallow Copy (copy()) Example

import copy

# Original list with nested elements
original_list = [1, 2, [3, 4], ("A", "B")]

# Shallow copy
shallow_copy_list = original_list.copy()

# Modifying the nested list in the original list
original_list[2][0] = 100  

print("Original List:", original_list)      # Output: [1, 2, [100, 4], ("A", "B")]
print("Shallow Copy:", shallow_copy_list)   # Output: [1, 2, [100, 4], ("A", "B")]

✅ Changes in the nested list reflect in the copy!

Deep Copy (copy.deepcopy()) Example

import copy

# Original list with nested elements
original_list = [1, 2, [3, 4], ("A", "B")]

# Deep copy
deep_copy_list = copy.deepcopy(original_list)

# Modifying the nested list in the original list
original_list[2][0] = 100  

print("Original List:", original_list)    # Output: [1, 2, [100, 4], ("A", "B")]
print("Deep Copy:", deep_copy_list)       # Output: [1, 2, [3, 4], ("A", "B")]

✅ Deep copy ensures nested lists are independent!

Key Differences

Feature	Shallow Copy (copy())	Deep Copy (copy.deepcopy())
Copies list?	✅ Yes	✅ Yes
Copies nested elements?	❌ No (only references)	✅ Yes (creates new objects)
Modifications affect original?	✅ Yes	❌ No
Performance	🚀 Faster, uses less memory	🐢 Slower, uses more memory

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Conclusion

Mastering list methods is essential for Python programming, whether you're dealing with data manipulation or complex structures. Understanding shallow and deep copies helps prevent unintended modifications and optimizes performance. Choose the right method based on your use case!

🔥 Happy Coding! 🚀