Python Data Types & 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

Data Structure

Python provides several built-in data structures, which are fundamental for organizing and storing data efficiently. Here are some commonly used data structures in Python:

1. Lists:

   • A mutable, ordered sequence.

   • Created using square brackets [].

   • Example:

       pythonCopy codemy_list = [1, 2, 3, 'hello', 4.5]
     

2. Tuples:

   • An immutable, ordered sequence.

   • Created using parentheses ().

   • Example:

       pythonCopy codemy_tuple = (1, 2, 3, 'world', 4.5)
     

3. Sets:

   • An unordered collection of unique elements.

   • Created using curly braces {} or the set() constructor.

   • Example:

       pythonCopy codemy_set = {1, 2, 3, 4, 4, 5}
     

4. Dictionaries:

   • A collection of key-value pairs.

   • Created using curly braces {} or the dict() constructor.

   • Example:

       pythonCopy codemy_dict = {'name': 'John', 'age': 25, 'city': 'New York'}
     

5. Strings:

   • A sequence of characters.

   • Immutable.

   • Created using single (') or double (") quotes.

   • Example:

       pythonCopy codemy_string = "Hello, Python!"
     

6. Arrays (from the array module):

   • A more compact way to represent arrays of numeric values.

   • Example:

       pythonCopy codefrom array import array
       my_array = array('i', [1, 2, 3, 4, 5])
     

7. Deque (from the collections module):

   • Double-ended queue.

   • Supports fast appends and pops from both ends.

   • Example:

       pythonCopy codefrom collections import deque
       my_deque = deque([1, 2, 3, 4, 5])
     

8. Stacks (using lists):

   • LIFO (Last-In, First-Out) data structure.

   • Implemented using lists.

   • Example:

       pythonCopy codemy_stack = [1, 2, 3]
       my_stack.append(4)  # push
       top_item = my_stack.pop()  # pop
     

9. Queues (using lists or queue module):

   • FIFO (First-In, First-Out) data structure.

   • Example (using lists):

       pythonCopy codemy_queue = [1, 2, 3]
       my_queue.append(4)  # enqueue
       front_item = my_queue.pop(0)  # dequeue
     

   • Example (using queue module):

       pythonCopy codefrom queue import Queue
       my_queue = Queue()
       my_queue.put(1)  # enqueue
       front_item = my_queue.get()  # dequeue