by In Python, arrays and lists are both used to store collections of items, but they have different characteristics, use cases, and underlying implementations.
Lists
Lists are built-in data structures in Python that can store a collection of items of different data types.
- Usage:
Lists are versatile and can be used to store heterogeneous data types, meaning you can have a list containing integers, strings, floats, and other objects all at once.
- Example:
my_list = [1, “hello“, 3.14, True]
- Implementation:
Lists are implemented as dynamic arrays, meaning they can grow and shrink as needed. When the capacity of the list is exceeded, a new, larger underlying array is allocated, and the old elements are copied to it.
- Methods:
Lists come with a wide range of built-in methods for operations like adding, removing, and modifying elements (e.g., append(), extend(), insert(), remove(), pop(), sort(), etc.).
- Example:
my_list.append(42)
- Performance:
Lists are optimized for general-purpose use. Accessing elements by index is fast (O(1) time complexity), but operations like inserting or deleting elements can be slower (O(n) time complexity) depending on the position of the element.
Arrays
Arrays in Python are provided by the array module and are used to store collections of items of the same data type. They are more memory-efficient than lists for storing large amounts of data of the same type.
- Usage:
Arrays are best used when you need to store a large collection of items of the same type and perform numerical operations on them.
- Example:
import array
my_array = array.array(‘i‘, [1, 2, 3, 4])
- Implementation:
Arrays are implemented as tightly packed, homogeneous sequences of elements. Each element in an array occupies the same amount of space in memory.
- Methods:
Arrays support many of the same operations as lists, but they are more limited in scope. They support methods such as append(), extend(), insert(), remove(), and pop().
- Example:
my_array.append(5)
- Performance:
Arrays are more memory-efficient than lists because they store elements of the same type in contiguous memory locations. Arrays can be faster for numerical operations due to better memory locality and reduced overhead.
