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Python is a versatile programming language with powerful built-in data types that simplify the way you store and manage data. In this quick read, you’ll learn about five essential data types: List, Tuple, Set, Dictionary and Array. These structures are the foundation of Python programming, each suited to specific use cases.

1. List

A list is an ordered, mutable collection that can store elements of different types.

Key Features:

• Allows duplicates.
• Elements can be added, removed, or modified.
• Indexed access, making it easy to retrieve elements.

Example:

# Creating a list
my_list = [1, 2, 3, "apple", 5.5]

# Accessing elements
print(my_list[0])  # Output: 1

# Adding an element
my_list.append("banana")

# Removing an element
my_list.remove(3)

print(my_list)  # Output: [1, 2, 'apple', 5.5, 'banana']

2. Tuple

A tuple is an ordered, immutable collection. It’s similar to a list but cannot be modified after creation.

Key Features:

• Faster than lists because they are immutable.
• Useful for fixed collections like coordinates or configurations.

Example:

# Creating a tuple
my_tuple = (1, 2, 3, "apple")

# Accessing elements
print(my_tuple[1])  # Output: 2

# Tuples are immutable
my_tuple[0] = 10  # This will raise an error

3. Set

A set is an unordered collection of unique elements, which provides built-in set operations for easy manipulation of data.

Key Features:

• Automatically removes duplicates.
• Useful for operations like union, intersection, and difference.

Example:

# Creating a set
my_set = {1, 2, 3, 4, 4}  # Duplicate '4' is removed automatically

# Adding an element
my_set.add(5)

# Removing an element
my_set.remove(3)

print(my_set)  # Output: {1, 2, 4, 5}

# Set operations
another_set = {4, 5, 6}
print(my_set.union(another_set))       # Output: {1, 2, 4, 5, 6}
print(my_set.intersection(another_set))  # Output: {4, 5}

4. Dictionary

A dictionary is a key-value pair mapping structure. It’s like a real-world dictionary where you look up a word (key) to get its meaning (value). Dictionaries support multi-level nesting and are useful in handling JSON objects.

Key Features:

• Keys must be unique and immutable.
• Values can be any data type.
• Optimized for fast lookups.

Example:

# Creating a dictionary
my_dict = {"name": "Alice", "age": 25, "city": "New York"}

# Accessing values
print(my_dict["name"])  # Output: Alice

# Adding a key-value pair
my_dict["job"] = "Developer"

# Updating a value
my_dict["age"] = 26

# Removing a key-value pair
del my_dict["city"]

print(my_dict)  # Output: {'name': 'Alice', 'age': 26, 'job': 'Developer'}

5. Array

An array (from the array module) is similar to a list but optimized for homogeneous data types (e.g., integers, floats). It’s more memory-efficient than a list. Note that array is not a built-in type and must be imported from the array module before using it.

Key Features:

• Only stores elements of the same type.
• Ideal for numeric operations.

Example:

import array

# Creating an array of integers
my_array = array.array('i', [1, 2, 3, 4])

# Adding an element
my_array.append(5)

# Accessing elements
print(my_array[0])  # Output: 1

# Removing an element
my_array.remove(2)

print(my_array)  # Output: array('i', [1, 3, 4, 5])

Comparison

Common Use Cases

Conclusion

Understanding these core sequence and collection types is essential for effective programming in Python, and it helps you choose the right type for the given use case. Mastering these data types will make your Python programs cleaner, efficient and maintainable.