Python Data Types and Data Structures 📚🐍

Python, with its simplicity and versatility, has become a popular programming language for various applications. Understanding data types and data structures in Python is crucial for efficient and effective programming. Let's dive into the world of Python data and make it fun! 🎉

1️⃣ Data Types in Python:

In Python, data types define the nature of the variables and the operations that can be performed on them. Some of the common data types include:

• 🔹Numeric Types: Integers (int), Floating-Point Numbers (float), and Complex Numbers (complex).

• 🔹 Text Type: Strings (str).

• 🔹 Boolean Type: Boolean (bool) representing True or False.

• 🔹 Sequence Types: Lists, Tuples, and Strings.

• 🔹 Mapping Type: Dictionaries (dict).

• 🔹 Set Types: Sets (set) and Frozen sets (frozenset).

• 🔹 None Type: Represents the absence of a value (None).

2️⃣ Data Structures in Python:

Python offers various data structures to organize and manage data efficiently. Here are some popular ones:

• 📜List :

  Lists are ordered and mutable collections of elements enclosed in square brackets []. They can hold various data types and allow duplicate elements.

  For example:

fruits = ['apple', 'banana', 'orange']

• 🍐Tuples:

  Tuples are ordered and immutable collections of elements enclosed in parentheses (). They are similar to lists but cannot be modified after creation.

  For example:

dimensions = (10, 20, 30)

• 🔠Sets :

  Sets are unordered collections of unique elements enclosed in curly braces {}. They eliminate duplicates automatically.

  For example:

colors = {'red', 'green', 'blue'}

• 🗂️Dictionaries :

  Dictionaries are another essential data structure in Python. They are collections of key-value pairs enclosed in curly braces {}. Each key is unique, and it maps to a corresponding value. Dictionaries are also known as associative arrays or hash maps.

  For example:

# Example of a dictionary representing a person's details
person = {
    'name': 'John Doe',
    'age': 30,
    'occupation': 'Software Engineer',
    'location': 'New York'
}

3️⃣ Differences between List, Tuple, and Set with Examples:

Lists 📜 vs. Tuples 🧵

🔹 Mutability: Lists are mutable, meaning you can change, add, or remove elements after creation. Tuples, on the other hand, are immutable, and their elements cannot be modified once defined.

🔹 Syntax: Lists are defined using square brackets [], while tuples use parentheses ().

# List
numbers = [1, 2, 3]

# Tuple
coordinates = (4, 5)

Tuples 🧵 vs. Sets 🛠️

🔹 Mutability: Tuples are immutable, but sets are mutable, allowing you to add or remove elements.

🔹 Duplicate Elements: Tuples can contain duplicate elements, whereas sets automatically remove duplicates.

# Tuple
dimensions = (10, 20, 30)

# Set
unique_dimensions = {10, 20, 30}

📝 Task 1: Creating and using Dictionaries

Create the below Dictionary and use Dictionary methods to print your favorite tool just by using the keys of the Dictionary.

fav_tools = 
{ 
  1:"Linux", 
  2:"Git", 
  3:"Docker", 
  4:"Kubernetes", 
  5:"Terraform", 
  6:"Ansible", 
  7:"Chef"
}

# Printing your favorite tool (e.g., key=6)
print(fav_tools[6]) # Output: Ansible

📝 Task 2: Creating and Sorting Lists

Create a List of cloud service providers eg.

cloud_providers = ["AWS","GCP","Azure"]

Write a program to add Digital Ocean to the list of cloud_providers and sort the list in alphabetical order.

# Adding "Digital Ocean" to the list
cloud_providers.append("Digital Ocean")

# Sorting the list alphabetically
cloud_providers.sort()

print(cloud_providers) # Output: ['AWS', 'Azure', 'Digital Ocean', 'GCP']

Conclusion 🎯

Understanding Python data types and data structures is crucial for efficient coding and problem-solving. Lists, tuples, and sets serve different purposes and are useful in various scenarios. With this knowledge, you can optimize your code and build robust applications with Python! 🚀

Happy coding! 😊🐍

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