In Python, a tuple and an array are both used to store collections of items, but they have some important differences in terms of mutability, functionality, and use cases:
- Immutable: Tuples are immutable, meaning their elements cannot be modified after creation. Once a tuple is defined, you cannot add, remove, or change elements.
- Heterogeneous: Tuples can contain elements of different data types.
- Ordered: Tuples are ordered collections, meaning the elements have a specific sequence, and you can access elements by their index.
- Syntax: Tuples are defined using parentheses
( ), although they can also be defined without any enclosing symbols.
- Use Cases: Tuples are typically used when you want to represent a fixed collection of related data that should remain constant throughout your program. They are also commonly used as keys in dictionaries due to their immutability.
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Example of a tuple:
my_tuple = (1, 2, 'apple', 3.14) # my_tuple = 10 # This will raise a TypeError because tuples are immutableCode language: Python (python)
- Mutable: Arrays in Python are typically implemented using the
arraymodule, and they are mutable, which means you can modify their elements after creation.
- Homogeneous: Arrays in Python are typically homogeneous, meaning they contain elements of the same data type.
- Sequential: Arrays are often used for numerical data and provide efficient sequential storage and manipulation of numeric values.
- Syntax: Arrays are not a built-in data type like tuples; they are implemented using the
arraymodule and need to be imported.
Example of an array:
from array import array my_array = array('i', [1, 2, 3, 4]) my_array = 10 # Modify an element in the arrayCode language: Python (python)
The primary difference between tuples and arrays in Python is their mutability and use cases. Tuples are immutable and are used for storing fixed collections of related data, while arrays (typically implemented using the
array module) are mutable and are used for efficiently storing and manipulating sequences of numeric values, especially when you need to perform numerical computations on the data.
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