Python Data Collections

Python provides several data collection types that allow storing multiple elements within a single structure. These structures are designed for various tasks and make the code more convenient.

Types of Data Collections

Python includes the following primary data collection types:

1. list
2. tuple
3. set
4. dict

1. list

The list type is used to store ordered, mutable, and duplicate elements.

Features:

• Ordered.
• Elements can be modified, added, or removed.
• Supports various data types.

Examples:

fruits = ["apple", "banana", "cherry"]
colors = ["red", "green", "blue"]
numbers = [1, 2, 3, 4, 5]

Useful Methods:

fruits.append("pear")    # Add element
fruits.remove("banana")   # Remove element
fruits.sort()              # Sort list
print(len(fruits))         # Get length

2. tuple

The tuple type is used to store ordered, immutable elements.

Features:

• Ordered.
• Elements cannot be modified.
• Supports various data types.

Examples:

coordinates = (10.5, 25.4)
categories = ("book", "movie", "music")

Basic Operations:

print(coordinates[0])   # Access first element
print(len(categories))  # Get length

3. set

The set type is used to store unordered, unique elements.

Features:

• Elements must be unique.
• Unordered.
• Supports mathematical operations.

Examples:

numbers = {1, 2, 3, 4, 5}
names = {"Ali", "Vali", "Sami"}

Useful Operations:

numbers.add(6)            # Add element
numbers.remove(2)         # Remove element
union_set = numbers.union({7, 8})        # Union
intersection_set = numbers.intersection({3, 4, 5})  # Intersection

4. dict

The dict type stores key-value pairs.

Features:

• Ordered (Python 3.7+).
• Keys must be unique.
• Keys must be immutable types (int, str, tuple).

Examples:

student = {
    "name": "Ali",
    "age": 20,
    "year": 2
}

Useful Methods:

print(student["name"])        # Access value
student["year"] = 3          # Modify value
student["faculty"] = "IT"  # Add new key-value pair
print(student.keys())         # List of keys
print(student.values())       # List of values

Specialized Data Collections (Collections Module)

Python's collections module provides specialized data structures for managing collections. Some of them are:

1. namedtuple — Creates named structures.
2. deque — Optimized list for adding/removing items from both ends.
3. ChainMap — Combines multiple dictionaries into a single view.
4. Counter — Special dictionary for counting elements.
5. OrderedDict — Keeps insertion order.
6. defaultdict — Returns default values for missing keys.
7. UserDict, UserList, UserString — Extend dictionary, list, and string objects.

Conclusion

Data collections are essential in Python programming, each designed for specific tasks. Using them properly enhances program efficiency and code clarity.

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