Python Data Structures
Just as important as languages and frames are data structures, fundamental computer science concepts essential for writing code efficiently
When people think about programming, they often think in terms of languages and frames. Names like Javascript, Node, HTML, and others often come to mind when people are asked about the tools needed for high-level programming. But just as important as languages and frames are data structures, fundamental computer science concepts that are essential for writing code efficiently and creating well-tuned applications and programs no matter what language you choose.
Integrated data structures
These are data structures that are built into the Python language, designed to make programming easier and allow programmers to quickly find solutions to programming problems.
User-defined data structures
These are data structures that are more widely applicable.
In this article, we’ll dive into built-in and user-defined data structures for when to use Python, as well as some of the practical applications and impacts on your coding process that these various data structures can give you.
Built-in data structures in Python
There are four main categories of built-in data structures when using Python: lists, dictionaries, tuples, and arrays. Here’s what you need to know about each
Lists
A list is a data structure that contains an ordered collection of elements. This means that you can store any sequence of items in a given list. Lists in Python work exactly the same way as any real list (like a shopping list), except that you probably write different items on separate lines in your shopping list, unlike in Python where you insert commas between them.
Any list of items must be enclosed in square brackets, which allows Python to understand that you are specifying a list. Once you’ve created a list, you can simply add, remove, or search for items in the list whenever you need to find something specific. Since we can add and remove items, a list is known as a mutable data type, which is a fancy way of saying that a list can be modified if necessary.
Dictionaries
It may be easier to think of a Python dictionary as an address book. In the case of a Python dictionary, instead of finding someone by looking up their name, Python developers associate keys with values. This requires unique keys to work, allowing the list to be sorted by a single variable. This helps maintain the integrity of the list and prevents duplicates that would prevent access to the data.
It should be noted that you can only use immutable objects for the keys of a dictionary, but you can use mutable or immutable objects for dictionary values. This means that the keys themselves should be simple objects for clarity and efficiency.
Tuples
The main purpose of tuples is to hold together or “synchronize” multiple objects. You can think of them in a similar way to lists, but tuples don’t offer the extensive functionality that a list class can. An important and unique feature of tuples is that they are immutable as strings, which means that you cannot modify the tuples.
Tuples are defined by specifying comma-separated elements within an optional pair of parentheses. They are typically used in cases where a declaration or user-defined function can accurately assume that the collection of values will not change.
Sets
Sets refer to a collection of unordered items that are unique, which means that even if the data is repeated more than once, it would be entered into the set only once. Sets are used when the existence of an object in a collection is more essential than the order itself, or how many times the object appears. By using sets, you can test the membership of an object, find out if it is a subset of another set, find the intersection between two sets, and additional sort and search functions.
User Defined Data Structures in Python
There are a number of user-defined data structures that you should use and become familiar with programming with Python, including arrays, stacks, queues, trees, linked lists, graphs, and hash maps. Remember, user-defined structures are not unique to Python: they are data structures that are shared by many programming languages, but also help build Python-based applications.
Arrangements
Very similar to lists, the only difference between arrays and lists is that arrays only allow storing homogeneous elements within them, while lists allow the storage of heterogeneous data elements.
Stacks
Stacks are a linear type of data structure centered on the last-in, first-out principle, which means that data entered last will in turn be accessed first. Stacks are built using the array structure and include a variety of operations: push elements, pop elements, and access elements only from a point on the stack known as the TOP. This TOP is the indicator of the current position of the stack. Stacks are most often used for applications such as recursive programming, word reversal, undo mechanisms in word editors, and the like.
Queues
A queue is another form of linear data structure, although queues are based on the first-in, first-out principle, where data entered first will be accessed first. Queues are also built using the matrix structure and include operations that can be performed from either end of the queue. Some operations, such as adding and removing items, are called En-Queue and De-Queue, and you can also access items. Queues are most often used as a network buffer to help manage and control traffic congestion to an application or site. They are also often used to run operating systems and a variety of other functions.
Trees
Trees are nonlinear data structures that include roots and nodes. The root is the node where all the data originates from, and the other nodes are the other data points that are available to the programmers. The preceding node is known as the parent and the following node is called the child, for obvious reasons. Meanwhile, the end nodes are called leaves.
A tree also includes different levels, which can show the depth of information as someone views or accesses the data. Trees create a hierarchy that can be used in a variety of real-world applications. For example, HTML pages use trees to differentiate which tag comes under which block.
Linked lists
Linked lists are a type of linear data structure that, while not stored, are linked to each other using pointers. The node of a linked list consists of data and a pointer named next. Linked lists are most widely used for image display applications, music player applications, and similar media programs and applications.
Graphics
Graphs are used to collect and store data on a set of points called vertices (nodes) and edges (edges). Graphs are often used as data parallel to a map or graph of the real world, and can be used to find the relationship between cost and distance between various data points to help find the most efficient or direct route. . This makes charts essential for many apps like Google Maps, Uber, Grubhub, and other apps that involve some form of geographic distance measurement, to help calculate the most direct route.
