🐍Day 14 - Python Data Types and Data Structures for DevOps.

✨Python Data Types

Data types are the classification or categorization of data items. It represents the kind of value that tells what operations can be performed on a particular data. Since everything is an object in Python programming, data types are actually classes and variables are instance (object) of these classes. Python has the following data types built-in by default: Numeric(Integer, complex, float), Sequential(string, lists, tuples), Boolean, Set, Dictionaries, etc.

To check what is the data type of the variable used, we can simply write:

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 # Define some variables
integer_var = 42
float_var = 3.14
string_var = "Hello, world!"
list_var = [1, 2, 3]
tuple_var = (1, 2, 3)
dict_var = {"key": "value"}
boolean_var = True
none_var = None

# Check the data type of each variable
print(type(integer_var))  # Output: <class 'int'>
print(type(float_var))    # Output: <class 'float'>
print(type(string_var))   # Output: <class 'str'>
print(type(list_var))     # Output: <class 'list'>
print(type(tuple_var))    # Output: <class 'tuple'>
print(type(dict_var))     # Output: <class 'dict'>
print(type(boolean_var))  # Output: <class 'bool'>
print(type(none_var))     # Output: <class 'NoneType'>

✨Data Structures

In Python, a data structure is a way of organizing and storing data in a particular format so that it can be accessed and manipulated efficiently. Python offers several built-in data structures, each with its own characteristics and use cases. Here's an overview of some commonly used data structures in Python:

1. Lists:

   • Ordered collection of items.

   • Mutable (can be modified after creation).

   • Allows duplicate elements.

   • Accessed by index.

   • Syntax: [item1, item2, ...].

2. Tuples:

   • Ordered collection of items.

   • Immutable (cannot be modified after creation).

   • Allows duplicate elements.

   • Accessed by index.

   • Syntax: (item1, item2, ...).

3. Sets:

   • Unordered collection of unique items.

   • Mutable (can be modified after creation).

   • Does not allow duplicate elements.

   • Does not support indexing.

   • Syntax: {item1, item2, ...}.

4. Dictionaries:

   • Collection of key-value pairs.

   • Mutable (can be modified after creation).

   • Keys are unique and immutable (e.g., strings, numbers, tuples).

   • Values can be of any data type.

   • Accessed by key.

   • Syntax: {key1: value1, key2: value2, ...}.

5. Strings:

   • Sequence of characters.

   • Immutable (cannot be modified after creation).

   • Accessed by index.

   • Syntax: 'string' or "string".

6. Arrays (from array module):

   • Similar to lists but homogeneous (all elements must be of the same type).

   • More memory efficient compared to lists for certain use cases.

7. Stacks and Queues (can be implemented using lists or deque):

   • Stacks follow Last-In-First-Out (LIFO) principle.

   • Queues follow First-In-First-Out (FIFO) principle.

8. Heaps (from heapq module):

   • Binary tree-based data structure.

   • Efficiently maintains a partially ordered tree.

   • Useful for implementing priority queues.

9. Linked Lists:

   • Consists of nodes where each node points to the next node in the sequence.

   • Different from lists in Python; often implemented manually or using third-party libraries.

✨Task-1: Give the Difference between List, Tuple and set. Do Hands-on and put screenshots as per your understanding.

Examples of List, Set & Tuple:

✨Task-2: Create below Dictionary and use Dictionary methods to print your favorite tool just by using the keys of the Dictionary.

✨Task-3: 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.

✨Conclusion

Python's versatile data types and data structures empower DevOps practitioners with efficient tools for managing and manipulating data, enhancing productivity and facilitating streamlined development processes in diverse operational contexts.

I believe this blog will be really helpful, giving you fresh perspectives and teaching you something new and interesting. 🙏

😊 Enjoy learning!