As beginners, we’ve been introduced to a lot of data structures in Python. In this article, we’ll be discussing two popular data structures – list and deque, and their differences.
We’ll look into what they are, how they work, and what their time complexities are.
Comparing List and Deque in Python
In Python, a list is a mutable data structure that can contain a sequence of elements with different datatypes. It is an ordered collection of items, and you can perform various operations on it, including addition, deletion, and modification of elements.
A list in Python is implemented using an array under the hood, which means accessing an element from a list is an O(1) operation. On the other hand, deque stands for “double-ended queue,” and it’s a special type of queue, where insertion and deletion are allowed from both ends.
A deque is implemented using a doubly-linked list data structure, which allows fast insertion and deletion operations from both sides. A deque is often used in situations that require faster append and pop operations than list constructions.
Understanding List in Python
A list in Python is one of the most commonly used data structures, and it can hold any mutable or immutable object. Lists are ordered collections of elements where each element is indexed by an integer.
You can access the elements of a list using an index value, which starts from 0.
One of the significant advantages of using a list is that you can change its size, unlike tuples and strings.
This property makes it dynamic and flexible enough to hold any number of items. Any changes made in a list affect its memory representation, so it can either get smaller or larger depending on the number of elements in it.
For instance, creating a list in Python is pretty simple. You can define a list by listing the values separated by commas inside square brackets:
fruits = [‘apple’, ‘banana’, ‘cherry’]
You can access the list’s elements using indexing like this:
print(fruits) # Output: apple
Understanding Deque in Python
As already mentioned, a deque is a doubly-linked list data structure that allows fast insertion and deletion operations from both sides. The collections module in Python contains a deque class that you can use to create deque objects.
Here’s how you can define a deque:
d = collections.deque()
The deque() constructor can optionally take an iterable object as an argument. You can also provide the maximum length of a deque object when initializing it.
The difference between a deque and other sequence data types in Python is that it supports two methods: appendleft() and popleft(). These two operations are essential and are used in implementing various algorithms that require deleting and inserting elements from the beginning of a list or the end of a list.
Other operations that can be performed on a deque object include append(), pop(), clear(), count(), and extend(), among others.
A critical factor when choosing a data structure to use is its time complexity. Time complexity determines the amount of time required to perform operations on the data structure.
In Python, a list and a deque have different time complexities for different operations. For instance, accessing an element in a list using index takes constant time, O(1), because lists are implemented as arrays.
Insertion or deletion of an element at the end of a list is also constant time, O(1), but insertion or deletion of an element at the beginning of a list has a time complexity of O(n), which is linear time. For deque, inserting and deleting items from the ends takes constant time, O(1), but doing so from the beginning takes linear time O(n).
This is because, in a deque, the first item and the last item are fully loaded.
In conclusion, both a list and a deque in Python are data structures that hold a collection of items in an ordered sequence. However, the time complexity of each data structure is different for different operations.
Lists are better for operations where indexing is required, while deque is better for operations that require faster append and pop operations than lists.
Big O Notation
Big O notation is a mathematical tool used to describe the complexity of algorithms and data structures. It is a way to measure how time and memory requirements scale as the input size increases.
Big O notation is vital in computer science because it helps developers to compare algorithms and choose the most efficient one.
The time complexity of an algorithm represents the number of steps it takes to execute as a function of the input size.
Big O notation explains the worst-case scenario and characterizes algorithms as they scale to larger inputs. It is commonly used with the step-counting method to predict the running time of an algorithm.
The importance of using Big O notation cannot be overstated, especially when dealing with large datasets. A small difference in time complexity can make a significant difference in processing efficiency, which can affect the overall performance of a program.
By using Big O notation, developers can optimize algorithms to deliver faster run times, which are essential for applications that handle big data.
Comparing Append Time
In Python, the append() method is used to add elements to the end of a list or deque data structure. In this section, we will compare the append() operation of lists and deque and how their time complexities differ.
Appending to the end of a list is an O(1) operation because of its contiguous memory allocation method. The append() method adds an element to the end of the list, and this operation takes constant time, irrespective of the list’s size.
Therefore, the run time for appending an element at the end of a list is predictable and remains constant as the input size increases. For a deque data structure, appending to the end requires the addition of a node at the tail of the list.
This operation takes O(1) time because deque is implemented as a doubly-linked list, which has access to both the tail and the head nodes. Therefore, deque append() operation at the end of the list is also a constant time operation.
Appending an element at the beginning of a list requires re-ordering elements and updating their indices. This operation takes O(n) time because all the elements have to be shifted right by one index position to accommodate the new element.
In contrast, appending an element at the beginning of a deque requires adding a new node and setting its previous node pointer to null. This operation takes constant time, O(1), because deque has access to both head and tail nodes.
Appending an element at the middle of a list requires inserting the element at the required index and moving elements with higher indices to the right. This operation takes O(n) time on average because half the list elements have to be moved.
Deque allows appending elements at the middle of the list, but this method is less efficient compared to the append() operation at the end or the beginning. Since deque is implemented as a linked list, inserting an element at the middle involves iterating through the linked list to the required index, which is an O(n) operation.
In conclusion, appending elements to lists and deques take different amounts of time, depending on the position of the element – beginning, middle, or end. Appending an element at the end or the beginning of a list or deque is a constant-time operation, O(1), whereas appending an element at the middle of a list is an O(n) operation.
Big O notation is useful in measuring these operations’ time complexities and helps in choosing the most efficient data structure for a given application.
Comparing Pop Time
In Python, the pop() method is used to remove elements from a list or deque data structure. The pop() method allows us to remove an element from the end, the beginning, or a specific position in the data structure.
In this section, we will compare the pop() operation of lists and deque and how their time complexities differ. Popping an element from the end of a list, using the pop() method, is an O(1) operation.
This is because deleting the last element of a list is a simple step, and the list’s size does not impact the time complexity of the operation. Similarly, popping an element from the end of a deque is also an O(1) operation.
Pop() operations from the beginning of a list, on the other hand, are expensive because of the time required to shift every other element down by one index. This operation has a time complexity of O(n) because all the elements have to be shifted down by one index position to replace the first element’s index.
Alternatively, popping the element at the beginning of a deque is an O(1) operation because it does not require shifting every other element down by one index. Instead, the node at the beginning of the list is removed, and the head pointer is updated to the next node.
For specific positions in a list, the pop() operation is also a costly operation. Removing an element from a specific index requires shifting all elements to the right of the index to the left by one index position.
This operation takes O(n) time in lists, as all the elements have to be moved down by one position. Deleting a specific node in a deque is an O(n) operation since the deque requires iterating through the list to the specified element index.
Which is Better – Deque or List? Both Lists and Deques are useful data structures, but their time complexities and functionalities vary.
Lists are useful if you need to store and access elements within an ordered sequence, while deques are useful if you need to perform operations at both ends of the list. Lists have the advantage of being faster in specific operations, such as slice operations, while deque is faster in implementing stack-like operations such as pushing and popping elements from either end of the list.
Besides, most operations in a deque, including append, pop, and rotate, are O(1) operations, while in a list, insert and remove, among others, have a time complexity of O(n). In conclusion, both lists and deques are useful data structures in Python.
Lists are useful for storing and accessing elements within an ordered sequence, but they may not be very efficient in operations that require adding or removing elements from either end of the list. Deques are useful in implementing stack-like operations such as push and pop operations and are faster than lists in most of these operations.
Ultimately, the choice of the data structure to use depends on the intended application and its functional requirements. In summary, lists and deques are both popular data structures in Python, and their time complexities and functionalities differ.
Lists are better suited for operations that require indexing and slicing, while deques are suitable for operations that require faster append and pop operations. Big O notation helps in understanding the time complexity of each data structure in different operations.
Ultimately, the choice of the data structure to use depends on the intended application’s functional requirements, and developers should select the most efficient data structure to optimize performance.