19 Data Structures in Python

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Data science involves manipulating and analyzing data efficiently, and various data structures serve different purposes in this field. Here’s a list of common data structures used in data science:

1. Lists:

• A collection of elements where each element has an assigned index.
• Lists in Python are mutable, meaning you can modify them in-place, and they can contain elements of different data types.
• They are commonly used for storing and manipulating sequences of items.

# Creating a list
my_list = [1, 2, 3, 'four', 5]

# Accessing elements
first_element = my_list[0]   # Output: 1
last_element = my_list[-1]   # Output: 5

# Slicing
subset = my_list[1:4]   # Output: [2, 3, 'four']

# Appending elements
my_list.append(6)

# Removing elements
my_list.remove('four')

# Iterating through a list
for item in my_list:
    print(item)

# List comprehension
squared_numbers = [x**2 for x in my_list]

• my_list is a Python list containing a mix of integers and strings.
• Various operations such as indexing, slicing, appending, and removing elements are demonstrated.
• The list comprehension technique is also used to create a new list of squared numbers based on the original list.

2. Arrays: