Python Data Structures 101: Your Complete Introduction

The Python programming language can be used to make many kinds of programmes. Data structures and algorithms are the most important parts of programming languages, but they take time to learn. This is why many HR departments use Python data structure interview questions when hiring people for software engineering jobs.

The best way to get ready for questions about data structures in Python is to review basic theoretical ideas and practice solving problems. If you have a technical interview coming up, you should practice answering at least one or two Python data structure interview questions per day.

In this article, we’ll talk about a few Python data structure interview questions. If you know how to answer these questions, you will know what to expect during the interviews because they will help you develop a good plan for getting through the hard technical rounds.

We will talk about the following things in this article:

  • Data Structures in Python
  •  Algorithms in Python
  • User Defined Data Structures in Python
  • Data Structures in python interview questions
  • Data Structures in Python

What do you mean by “data structure”?

A data structure is a way of putting together data used in programming. With this kind of structure, it’s much easier to store, find, and change data. Data structures are the foundation of all modern software, so understanding them is essential. One of the first steps in learning any programming language is getting to know the different kinds of data structures and how to use them well in different situations.

Data Structures in Python

Python has two default data structures: those that can be changed and those that can’t. Data structures that can add, remove, or change their parts are called “mutable” (from Latin mutabilis, “changeable”). Three types of data structures in Python can be changed: 

  1. Lists
  2. Dictionaries and 
  3. Sets.

 On the other hand, immutable data structures can’t be changed once they’ve been made. Only tuples, Python’s most basic built-in data structure, can be considered immutable.

Stacks and queues are examples of more complex data structures that can be made with Python’s basic data structures. But these are more often used in software engineering and the implementation of complex algorithms, so we won’t talk about them in this lesson because they have little to do with data science.

Lists in Python are made up of dynamic, changeable arrays that store the items in a collection in a particular order.

To start, arrays are a common way of organizing data in many programming languages. Arrays store many of the same types of items together (for instance, all elements are integers). Python lists can hold different kinds of data and objects at the same time. 

Different kinds of data, such as numbers, texts, and even functions, can all be stored in the same list. Using integer indices, starting with 0 for the first item in the list, you can move through the different items in the collection. As a Python list is sorted, things stay in the same order they were in when they were first added.

Dictionary entries are often used to make it easy to find the information given a specific key. If we want to limit our access to detailed data without making things more confusing, we need to be able to tell things apart. Let’s say we opened a dictionary to find out what “data science” meant, but we were sent to two different pages. Which one has the correct information? 

We use dictionaries when we can link (or “map”) a specific key to a particular set of data, and we need to get to that data quickly (in constant time, no matter the dictionary size). Also, dictionary values can be hard to understand. The names of our customers could be our “keys,” and their characteristics (the “values”) could be in dictionaries with words like “age,” “hometown,” etc.

When you work with Python, you can make “sets,” groups of unique elements that can’t be changed but can be changed. The parts of a set must always be the same. At first glance, lists and groups may look alike, but they are very different.

First, they can’t have copies; everything must be unique. You can clean up a list by removing repeated items with sets. Next, they can be used in unique ways, like mathematical sets, such as set unions, set intersections, etc. Lastly, they make it easy to determine whether an item belongs to a group.

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To sum up, tuples are almost the same as lists in that they have an ordered list of items, but they are different in one important way: tuples can’t be changed. We would use tuples if we needed a data structure that could not be changed, like a list. Also, a tuple can be used as a key in a dictionary if all of its members can’t be changed.

Aside from that, tuples are the same as lists in every other way. To construct a tuple, use square brackets (()) or the tuple() function [native code]. L4 It takes little work to change a list into a tuple or a tuple into a list (recall that we created the list L4 from a tuple).

  •  Algorithms in Python

As you may know, an algorithm is a set of steps for solving a problem that is finite and in order.

Before an algorithm can be written, the details of the problem, where it starts and ends, and the steps in between must all be known.

You can solve algorithms in one of three main ways:

  • The “divide and conquer” or “divide and emasculate” strategy breaks up big problems into pieces that are easier to handle. As its name suggests, dynamic programming breaks a problem into smaller, easier-to-handle pieces. Keeps track of past results and uses them to guide actions in the future
  • Greedy algorithms put ease of use ahead of how well they solve a problem.
  • Tree traversals are not linear when it comes to Python data structures. The fact that they have both a root and a node is what makes them different. 

Tree traversal goes to each node in the tree exactly once to update or check it.

There are three different kinds of tree walking:


For example, if you are doing an in-order traversal, you would start at the left node, go to the root, and then move to the correct nodes.

Going from the root node to the left and right nodes in a tree is called “pre-order traversal.”

Post-order traversal means moving from left to the right and back to the root node.

Algorithm for Ordering

Sorting is the method you need to use if you want your data to be in a particular order. There are two different kinds of it: merge sort and bubble sort.

  • Merge Sort follows the rule of “divide and rule.” To get the desired order, the given list is first broken up into smaller lists, which are then compared to their neighbors. When a stream of random data comes in, what is needed is a stream of sorted data. Here is the code, along with notes that explain each part.
  • Bubble Sort compares the elements and sorts them based on their similarities if they are not in the correct order.
  • Insertion sort picks one item at a time from a given list and puts it in the position you want.

There are also algorithms like selection sort and shell sort that sort data.

The search algorithms

We can use searching techniques to find certain things in a dataset. There are many different kinds of search algorithms, such as linear search, binary search, exponential search, interpolation search, and so on. In this section, we’ll look at the linear and binary search methods.

  • In a linear search, we need to find a specific item in an array with only one dimension. Both the wanted element and the set of possible features are given as input. So, it’s essential to compare the central part to all the other parts of the set. 
  • To use the algorithm for binary search, we assume that the list is sorted from highest to lowest. So, we only look at the bottom half of the range if the value of the search key is less than the value of the middle item in the list. If this isn’t the case, we focus on the top part. We keep looking until either we find the value, or we’ve tried all of the possible options.
  • User Defined Data Structures in Python

Because Python uses classes, programmers can make their own unique data structures. In Python, the following is an example of a user-defined data structure:

Class Person:

    def __init__(self, name, age): = name

        self.age = age

    def __str__(self):

        return f”{} ({self.age})”

Here, we’re making a class called “Person” that has two fields: “name” and “age.” Also, a method called str that returns a string is set up. We set this method up. This function is called when the object is going to be printed or turned into a string.

Once the class has been defined, the following things can be done to make instances of the Person class:

p1 = Person(“Alice”, 25)

p2 = Person(“Bob”, 30)

The names and ages of these items are accessible in the following ways:



Result : 

Alice 30

  • Data Structures in python interview questions

Why is Python a dynamic programming language?

With dynamic type checking, data types can be checked as they are used. This is because Python is a language that is read out loud. It executes each command one line at a time. Python is a dynamically typed language, so checking for the right type happens at runtime.

How well do you understand inheritance in Python?

Inheritance makes it easier to reuse code and cuts down on development time. It makes two different levels of society:

The class from which we inherit is called the “superclass.” It is the class that a system is built on.

The class that has been passed down is called the derived class. It is sometimes called “kid class” for short.

Some types of inheritance in Python are:

  • Single inheritance is when a class gets its properties and methods from a single superclass.
  • When two different derived classes inherit the same base class, this is an example of multilevel inheritance.
  • Hierarchical inheritance is when many child classes get properties and methods from a single base class.
  • Multiple inheritances are when a subclass gets properties from more than one parent class.

What do the terms “pickling” and “unpickling” mean in Python?

Pickling is the process of turning an object tree into a string of bytes in Python. On the other hand, unpicking is like doing something backward. Unpicking a byte stream can be done by turning it back into an object hierarchy. Pickling is a way to store and retrieve Python objects in a form similar to a file.

Why does Python have a lambda? List some of the things you can do with it.

Lambda is a type of anonymous function in Python that is different. It takes in more than one value but only gives back one. Lambda functions are used by programmers when they only need a short-term, anonymous process. You can use lambda functions in the following ways:

  • They help us in simple, one-time ways every day.
  • They make code easier to read.

Slices in Python: Explain the term

By slicing, you can choose parts of sequence types like lists, tuples, and strings. When you “slice” a list, you pick out a domain of it to work on while leaving the rest of the list unchanged. The only change to the contents is that one item has been taken away.

Here is the syntax for list slicing: The name of the list is [start:stop: steps]

Why do we need Python decorators?

Decorators are an essential part of Python. They add more features and capabilities to a function without changing how it works at its core. On the other hand, decorators take a function as an argument and return a different function. Another typical question about Python data structures

Distinguish between the ways that Python’s xrange and range work.

Even though both xrange() and range() return a list of integers, they are different. Python’s Range function gives you a list of numbers, while xrange makes an xrange object. So, xrange() doesn’t make a set of already-set items. Instead, it makes up the value as it goes.

What is the method of the join function in Python?

Strings are what the Python join function works on. This method uses a string connector value to connect the items in an iterable data structure (like an array or a list).

Explain how Python’s statement controls how code flows.

The order in which a program’s instructions are carried out is shown by its “control flow.” Python uses if-else statements, loops, and function calls to manage control flow.

In this system, there are three main types of regulations:

1. Sequential is the default mode.

2. Selection is how decisions and paths are made.

3. Looping works well with repetition because:

Setting up how Python handles allocating and releasing memory

Python’s private heap space manages a private heap where all objects and data structures are stored. The programmer can’t use this heap. Instead, the Python interpreter runs it. A memory manager provides heap space, where Python objects are stored. Python has its own trash collector, which is a nice bonus. When memory isn’t being used, it is recycled.

Can you tell me about Python generators?

Knowing that a generator is a function that returns a set of things can help you answer this question about a Python data structure in an interview. One step at a time, this is done. In this way, a generator is a group of functions that don’t give a single result. As a replacement, they provide an iterator object with a list of values.


Python’s built-in data structures and the fact that users can make their own data structures cover a lot of needs when it comes to data structures. Please choose the proper data structure for the task at hand and use it well to improve the performance of a programme. Thank you for spending the time to read this article on data structures and algorithms in Python.

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