Have you added Python Data Structures to your learning bucket? To begin the learning process, it is important to understand the definition and types of DSAs in Pythons. Let’s understand it with a simple example.
Consider working as a programmer with a series of records for insurance ids such as TAP 111, TAP 112, TAP 121, and so on. Assume that there are approximately 100 records. For the implementation, you used a 200-element array. These records are subject to random updates at any time. Your boss requests that you insert TAP 114 between TAP 112 and TAP 121. Because you used an array to implement the sequence, you would need to copy all of the records from TAP 121 onwards, make room for the new record, and then paste your initial sequence. And it takes a long time!
Some programmer, from the other team, suggests using a linked list for the record which made inserting, deleting, or adding any new records even after the capacity of 200 very very easy. No need to now vacate the memory for the addition in between. Having said that, your manager is now impressed with this programmer and has started to think of you as some stupid wasting his time for no reason.
What made a difference then?
What made a difference then?
Linked List allows greater flexibility for modification(addition, deletion) of data. Whereas arrays allow restricted flexibility since additions and deletions have to be done elementwise.
This is the very reason why you as a programmer should have proper knowledge of data structures and algorithms. Nowadays data structures and algorithms are an integral part of the interview process and also help in landing a high-paying job. Having a fundamental understanding of these concepts will give you a leg up when it comes time to prepare for an interview or technical questions.
Python is an ideal language for learning about these concepts since it has built-in modules for implementing many basic data structures and algorithms. In this article, we’ll cover the most common data structures used in Python and how they’re implemented.
What is Data Structure?
Data Structure is a systematic process of organizing and storing the data in the main memory of the computer so that it can be managed properly and utilized efficiently. One common example of data structure is a LinkedIn List.
If you want to perform some operations like sorting on numbers or characters then there are 2 possibilities, either you can create multiple variables to store them or create an array to store them all at once. LinkedIn List sounds like a good option. Right? Data Structure helps in accessing the data in an appropriate way. That’s how data structure helps in organizing your programming life.
Now that we have covered what Data structures are. Let’s look into how they are classified in Python data structure. Data Structures in Python are of two types, namely:
Types of Data Structures in Python
In-built Data Structure
1. Lists
Lists are one of the most useful data structures to understand. Lists are often used to store sets of similar objects.
For example, if you’re building a website about books and want to keep track of each user’s favorite book genres and authors, then you’d store all this information in a list.
Lists are a type of sequence, which means that they can be
- indexed (accessed by their position),
- sliced (accessed by a range),
- concatenated (added together).
Lists are mutable, meaning you can change them after they’ve been created. In short: lists can hold any kind of data, from numbers to strings to other lists!
Creating a list
Output
Adding Elements to the list :
Output :
Deleting Lists:
Output :
Accessing elements of list :
Output :
2. Tuples
Tuples are immutable sequences. The elements in a tuple can be of any type and their order is fixed, just like lists. Unlike lists, tuples cannot be resized or have new elements appended to them (you can only create an entirely new tuple).
Tuple Syntax:
`tuple` = `(element1`, `element2`,…)
Examples:
Creating a Tuple :
Output :
Accessing Tuple :
Output :
Appending elements :
Output :
Other tuple functions :
Output :
3. Dictionaries
Dictionaries are one of the most powerful data structures in Python. A dictionary is a set of key-value pairs. Each key is associated with one and only one value, and vice versa. They allow you to associate keys with values and retrieve the values by their corresponding keys.
data structures in Python
You can think of a dictionary as an unordered list (or array) of items, where each item is a key-value pair. Dictionaries are useful when you want to store information like your favorite foods or drinks, the topography of a country, or even the characters in a movie.
Here are the three main rules to keep in mind while creating Dictionaries:
- The keys must be hashable. This means that each key must also be an immutable type, such as a string or number.
- The values can be mutable and can be of any type. In other words, you can store lists, strings, and numbers in a dictionary.
- Dictionaries are unordered collections of key-value pairs; their order is not guaranteed to be consistent across executions of your program (unlike lists).
Some common operations on dictionaries include:
- Lookups– Check whether an item is present in the dictionary by using the get() method (or [] operator).
- Searches– Find items by comparing their values to keys using the in operator or get() method.
- Updates– Update an existing key-value pair by assigning new values to existing keys (or adding new ones).
Lookups
Searches
Updates
The syntax for creating a dictionary is:
dictionary_name = {key1:value1, key2:value2, …}
The following example shows how to create a dictionary with two items.
Creating a Dictionary :
Output:
Changing or adding key, value pairs
Output:
Deleting key, value pairs :
Output:
Accessing Elements :
Output:
Operations on Dictionary :
Output:
4. Sets
Sets are unordered collections of unique items. You can think of them as a bag that contains elements, but without their positions or order. All sets have the same operations, so they can be treated as one data structure.
Sets in Python are implemented as dictionary objects. They support three main operations:
● Membership Test:
Membership Test:
“x in A” returns True if x is an element of A, else False. The result is not affected by whether A contains x or not. This is called a fast membership test since it doesn’t require any sort of search or scan through all elements of a set.
● Union:
Union:
“A union B” creates a new set that contains elements from both A and B without overlap.
● Intersection:
Intersection:
“A intersect B” creates a new set that contains elements common between both A and B
Creating Set :
Output:
Add elements to set :
Output:
Now, that we looked into Built-in data structures, let’s look into User-defined data structures in Python and their types.
Now, that we looked into Built-in data structures, let’s look into User-defined data structures in Python and their types.
User-defined Data Structure
1. Linked Lists
Suppose you are given a list of your friends’ names and you must sort them by the first letter of their names. Once you sort the list, it’s time to store the data in permanent memory.
There are 2 ways to do so.
- Arrays
- LinkedIn Lists
Because arrays have some limitations and disadvantages, LinkedIn List is an efficient and preferred method. A linked list is a linear data structure in which the elements aren’t stored in a contiguous memory location which simply means that data could be scattered anywhere in the memory.
It’s called “linked” because the first node contains the memory address second element, the second node contains the elements of the third element, and henceforth and one node is linked to each other. The node doesn’t contain any of the addresses and hence it’ NULL, end of the list. The size of the Linked List is variable, therefore, we cna create unlimited nodes in the main memory.
2. Stack
A stack is a data structure that allows you to add and remove elements from the top of the stack, in order. In other words, it’s like a plate or pancake stack: you can only put things on top and take them off in a top to bottom manner i.e LIFO (last in first out). If you try to take something out of the middle, all hell breaks loose (and sometimes there’s hell on top!).
“Stacks are used when we want to do everything from one side of an object,” says Stacky McStackerson, who has been working with stacks since before computers existed.
3. Queue
A queue is a linear data structure that implements the FIFO principle — First In First Out. The first element added to the queue will be removed from its last, and no element can be removed until all elements behind it are removed as well.
Think about a queue at the grocery store or in an airport — when you arrive, you go to the back of the line. The person who arrives first is served first. When it’s your turn to be served at the grocery store or at the airport, you go to the front of the line because someone else has left so now you are at the front.
This is exactly what happens in our queues as well! You may add items to a queue but they won’t be removed until they reach the front of the line.
4. Heaps
Heaps are a useful data structure that can be used to store elements in order of priority. They’re a special type of binary tree. Heaps have many applications in computer science.
Example: Let’s say you have three tasks you need to complete: A, B, and C. You have a limited amount of time each day to do them (represented by the numbers 1–5), so you want them done quickly but not necessarily in order.
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Example:
Heaps provide an efficient way to keep track of this information; they let you know which task is most important at any given time so that when time runs out instead of having incomplete work from two tasks with low priorities, only one finished task gets dropped off the heap.
5. Hash maps
Hash maps are one of the most common data structures in Python, and they are often used to store sets of key-value pairs. Hash maps are very efficient ways to access data, because they don’t have to search through an entire list when looking up a particular item. Instead, they use an indexing system that looks up items quickly by using their keys.
Hash maps can be very useful in web applications, since they allow you to quickly find information about a certain user or piece of content. For example, if you wanted to display all users who logged into your app at 5:30pm yesterday then you could use a hash map for this purpose!
6. Trie
A trie (pronounced “try”, but sometimes called a radix tree) is a special kind of tree data structure that is used to store a collection of strings. It’s an alternative to the more common approach of using an array or list to store string values, and it has some advantages.
Traditionally, when you want to find the characters in one string that match those in another string (for example, when searching for words in text or finding all permutations of a word), you do so by comparing each character in one string with each character in another one until there are no more matches. This process can require a lot of computation time if there are many characters on either side.
Tries offer faster comparison speeds because they store strings as nodes within an “encoded” tree structure rather than as flat arrays—so instead of trying every possibility for matching letters against each other sequentially, tries can jump directly from one node on one branch toward another node whose corresponding letter will have already been checked off by previous iterations through both branches at once!
Being comfortable with data structures and algorithms will help you write better python code.
The first step toward writing better Python code is understanding how data structures are used to store and organize data. Data structures are nothing more than the way we represent the information we want to work with. For example, a person’s name can be represented by different types of data:
- String (e.g., “John Smith”)
- Integer (e.g., 13)
- List (e.g., [“John”, “Smith”])
The second step toward writing better Python code involves understanding how algorithms are used to process data. Algorithms are like recipes for solving problems in computer science—they’re sets of instructions for performing tasks such as sorting, searching, or traversing through lists of items stored in a data structure (like linked lists). In addition to knowing which algorithm(s) will best solve your problem, it’s important that you know when they’re applicable so that you don’t waste time reinventing the wheel! Mastering these data structures is important for interview preparation.
