Top Python Interview Questions and Answers (2024) Part 1

Python interview

Introduction to Python and talk about Python Interview

Python, conceived by Guido van Rossum, made its debut on February 20, 1991. Renowned for its widespread adoption and adoration, Python stands tall as one of the most cherished programming languages. Its interpretative nature endows it with the flexibility to incorporate dynamic semantics seamlessly. Furthermore, Python boasts a distinction as a free and open-source language, marked by its remarkably simple and clean syntax. This inherent simplicity renders Python a language easily embraced by developers of all levels.

Python’s support for object-oriented programming further enhances its appeal, rendering it a top choice for myriad general-purpose programming tasks. Its hallmark lies in its ability to achieve diverse functionalities with brevity, owing to its concise syntax. Consequently, Python’s popularity has surged exponentially, driven by its user-friendly nature and efficiency in achieving complex tasks with minimal code.

Beyond its foundational strengths, Python’s prowess extends into cutting-edge domains such as Machine Learning, Artificial Intelligence, Web Development, and Web Scraping. Empowered by potent libraries, Python excels in executing robust computations, making it indispensable across diverse industries. The surging demand for Python developers, both in India and globally, underscores its pivotal role in contemporary software development. Recognizing its significance, companies worldwide vie to attract Python developers, offering lucrative perks and benefits in their pursuit of top talent.

Most popular 10+ Python Interview Part 1

1. What is Python?

Python is a high-level, interpreted programming language known for its simplicity and readability. It was created by Guido van Rossum and first released in 1991. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming.

One of Python’s key features is its elegant syntax, which emphasizes code readability and allows developers to express concepts in fewer lines of code compared to other languages. Python uses indentation to define code blocks, which enhances code clarity.

Python has a vast standard library that provides support for a wide range of tasks, from web development and data analysis to artificial intelligence and scientific computing. Additionally, Python has a thriving ecosystem of third-party libraries and frameworks, such as NumPy, pandas, Django, Flask, TensorFlow, and PyTorch, which further extend its capabilities.

Python’s versatility, ease of learning, and community support have contributed to its popularity among developers for various applications, including web development, automation, data science, machine learning, and more.

Generally interviewer ask what is python? in Python Interview session.

2. How to Install Python?

Installing Python is relatively straightforward, and there are several methods available depending on your operating system. Here’s a general guide:


  1. Download Python Installer: Visit the official Python website at and download the latest Python installer for Windows.
  2. Run Installer: Once the download is complete, double-click the installer file to launch it.
  3. Install Python: Follow the prompts in the Python installer. Make sure to check the box that says “Add Python to PATH” during installation. This option allows you to run Python from the command line without specifying its full path.
  4. Verify Installation: After the installation is complete, open Command Prompt and type python --version to verify that Python is installed correctly. You should see the version number printed to the console.


Install Homebrew (Optional): If you prefer using Homebrew, a package manager for macOS, you can install Python via Homebrew. Open Terminal and run the following command to install Homebrew:

/bin/bash -c “$(curl -fsSL”

Install Python: Once Homebrew is installed (if you chose to use it), you can install Python by running the following command in Terminal:

brew install python

Alternatively, you can download the macOS installer from and follow steps similar to the Windows installation.

Verify Installation: After installation, open Terminal and type python3 --version to verify that Python is installed correctly.


Use Package Manager: Most Linux distributions come with Python pre-installed. You can use your package manager to install Python if it’s not already installed. For example, on Ubuntu or Debian-based systems, you can use apt:

sudo apt update
sudo apt install python3

Replace python3 with python if you want to install Python 2 (although Python 2 is no longer actively maintained).

Verify Installation: After installation, open Terminal and type python3 --version to verify that Python is installed correctly.

3. What are the key features of Python?

Certainly! Here are some key features of Python:

  1. Simple and Readable Syntax: Python has a straightforward and clean syntax, making it easy to read and write code. This readability reduces the cost of program maintenance and development.
  2. Interpreted and Interactive: Python is an interpreted language, meaning that code is executed line by line, making debugging and testing easier. It also supports an interactive mode, allowing users to experiment with code snippets in real-time.
  3. Dynamic Typing: Python uses dynamic typing, where variables are dynamically typed at runtime. This means you don’t need to declare the data type of a variable explicitly.
  4. High-Level Language: Python is a high-level language, which means it abstracts low-level details such as memory management and provides constructs that are closer to human language.
  5. Extensive Standard Library: Python comes with a comprehensive standard library that provides support for various tasks, such as file I/O, networking, data manipulation, and more, reducing the need for external libraries for many common tasks.
  6. Object-Oriented: Python supports object-oriented programming (OOP) paradigms, allowing for the creation and manipulation of objects, inheritance, encapsulation, and polymorphism.
  7. Platform Independent: Python is platform-independent, meaning you can write code on one platform (e.g., Windows) and run it on another (e.g., Linux or macOS) without modification.
  8. Large Ecosystem of Libraries and Frameworks: Python has a vast ecosystem of third-party libraries and frameworks for various domains, including web development (e.g., Django, Flask), data science (e.g., NumPy, pandas), machine learning (e.g., TensorFlow, PyTorch), and more.
  9. Community Support: Python has a large and active community of developers who contribute to its growth, provide support through forums, mailing lists, and online resources, and regularly release updates and enhancements.
  10. Versatility: Python is a versatile language that can be used for a wide range of applications, including web development, automation, scientific computing, artificial intelligence, data analysis, and more.

4. What are the applications of Python?

Python is an incredibly versatile programming language with a wide range of applications across various domains. Some of the common applications of Python include:

  1. Web Development: Python is widely used for building web applications and websites. Frameworks like Django and Flask make web development efficient and scalable.
  2. Data Science and Machine Learning: Python has become the de facto language for data science and machine learning projects. Libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch provide powerful tools for data manipulation, analysis, visualization, and building machine learning models.
  3. Scientific Computing: Python is extensively used in scientific computing and research due to its simplicity and the availability of libraries like SciPy, matplotlib, and SymPy, which facilitate numerical computations, plotting, and symbolic mathematics.
  4. Artificial Intelligence: Python is a popular choice for developing artificial intelligence (AI) applications, including natural language processing (NLP), computer vision, and robotics, thanks to libraries like NLTK, spaCy, OpenCV, and TensorFlow.
  5. Automation and Scripting: Python is excellent for automating repetitive tasks and writing scripts for system administration, data processing, and software testing, thanks to its simplicity and extensive standard library.
  6. Game Development: Python is used in game development, both for creating game engines and scripting game logic. Libraries like Pygame provide frameworks for building 2D games, while Panda3D and Unity support 3D game development with Python scripting.
  7. Desktop GUI Applications: Python can be used to develop desktop graphical user interface (GUI) applications using libraries like Tkinter, PyQt, and wxPython, enabling developers to create cross-platform desktop applications with ease.
  8. Backend Development: Python is commonly used for backend development, powering the server-side logic of web applications and APIs. Frameworks like Django and Flask make it straightforward to build robust and scalable backend systems.
  9. DevOps and System Administration: Python is widely used in DevOps and system administration tasks, including configuration management, infrastructure automation, and monitoring, with tools like Ansible, SaltStack, and Fabric.
  10. Education and Teaching: Python’s simplicity and readability make it an excellent choice for teaching programming to beginners and for educational purposes in schools, universities, and coding bootcamps.

5. What is a dynamically typed language?

A dynamically typed language is a programming language where variable types are determined at runtime rather than at compile time. In dynamically typed languages, variables can hold values of any type, and their types can change over the course of the program’s execution.

Here are some key characteristics of dynamically typed languages:

  1. No Explicit Type Declarations: In dynamically typed languages, you typically do not need to declare the type of a variable explicitly. Instead, variables are assigned values, and their types are inferred based on the assigned values.
  2. Type Checking at Runtime: In dynamically typed languages, type checking is performed at runtime rather than at compile time. This means that type errors, such as attempting to perform an operation on incompatible types, may only be discovered when the code is executed.
  3. Variable Types Can Change: Since variable types are not fixed, they can change during the execution of the program. For example, a variable that initially holds an integer value may later be assigned a string value.
  4. Flexibility and Expressiveness: Dynamically typed languages offer greater flexibility and expressiveness, as they allow you to write code without worrying about the specifics of variable types. This can lead to more concise and readable code.
  5. Dynamic Binding: Dynamic typing often goes hand in hand with dynamic binding, where method calls and variable references are resolved at runtime based on the actual type of the object. This allows for polymorphism and late binding, where the specific implementation of a method is determined dynamically.

6. What is the difference between list and tuples in Python?

In Python, both lists and tuples are used to store collections of items, but there are some key differences between them:


  • Lists are mutable, meaning that you can modify their contents after they have been created. You can add, remove, or modify elements in a list.
  • Tuples, on the other hand, are immutable, meaning that once they are created, their contents cannot be changed. You cannot add, remove, or modify elements in a tuple.


  • Lists are enclosed in square brackets [ ], and elements are separated by commas.
  • Tuples are enclosed in parentheses ( ), and elements are also separated by commas.


  • Since lists are mutable, they require more memory and are generally slower than tuples.
  • Tuples are more memory-efficient and have better performance, especially when used as keys in dictionaries or as elements in sets.


  • Lists are commonly used for collections of items where the order or the elements may change, and when you need to modify the contents of the collection.
  • Tuples are often used for collections of items where the order of the elements is important and should not change, and when you want to ensure the integrity of the data.

Here’s a simple example to illustrate the differences:

# List example
my_list = [1, 2, 3, 4, 5]
my_list[0] = 10  # Modify the first element
print(my_list)   # Output: [10, 2, 3, 4, 5]

# Tuple example
my_tuple = (1, 2, 3, 4, 5)
# my_tuple[0] = 10  # This would raise an error since tuples are immutable
print(my_tuple)     # Output: (1, 2, 3, 4, 5)

7. What are Pickling and Unpickling?

Pickling and unpickling are processes in Python for serializing and deserializing objects, respectively. These processes are commonly used for saving and loading Python objects to and from files or transmitting them over a network.

  • Pickling: Pickling is the process of converting a Python object into a byte stream. This byte stream can then be written to a file or sent over a network. Pickling is achieved using the pickle module in Python. The pickle.dump() function is used to serialize an object into a byte stream.
  • Unpickling: Unpickling is the process of converting a byte stream back into a Python object. This allows you to reconstruct the original object from the serialized data. Unpickling is achieved using the pickle module in Python. The pickle.load() function is used to deserialize a byte stream into a Python object.

Here’s a simple example demonstrating pickling and unpickling:

import pickle

# Define a Python object (in this case, a dictionary)
data = {'name': 'John', 'age': 30, 'city': 'New York'}

# Pickle the object and write it to a file
with open('data.pkl', 'wb') as file:
    pickle.dump(data, file)

# Unpickle the object from the file
with open('data.pkl', 'rb') as file:
    loaded_data = pickle.load(file)

# Print the unpickled object
print(loaded_data)  # Output: {'name': 'John', 'age': 30, 'city': 'New York'}

In this example, we pickle a dictionary (data) into a file named data.pkl. Later, we unpickle the data from the file and store it in loaded_data. Finally, we print the unpickled data, which should match the original dictionary.

8. What Is the Difference Between Del and Remove() on Lists?

In Python, both the del statement and the remove() method are used to remove elements from a list, but they differ in how they accomplish this:

del Statement:

  • The del statement is a Python keyword used to remove an element from a list by its index.
  • Syntax: del list_name[index]
  • Example: my_list = [1, 2, 3, 4, 5] del my_list[2] # Remove the element at index 2 (value: 3) print(my_list) # Output: [1, 2, 4, 5]
  • The del statement can also be used to delete slices of a list or to delete the entire list.

remove() Method:

  • The remove() method is a built-in method of the list class used to remove the first occurrence of a specified value from the list.
  • Syntax: list_name.remove(value)
  • Example: my_list = [1, 2, 3, 4, 5] my_list.remove(3) # Remove the first occurrence of the value 3 print(my_list) # Output: [1, 2, 4, 5]
  • If the specified value is not found in the list, the remove() method will raise a ValueError.

In summary:

  • Use the del statement when you want to remove an element from a list by its index.
  • Use the remove() method when you want to remove the first occurrence of a specific value from the list.

9. What do you mean by Python literals?

In Python, literals are the representations of fixed values in the source code. They are used to denote data that can be assigned to variables or used directly in expressions.

There are several types of literals in Python:

Numeric Literals:

  • Integers: Whole numbers without any decimal point, e.g., 42, -123, 0.
  • Floating-point numbers: Numbers with a decimal point or in exponential form, e.g., 3.14, 2.0, 1.5e-3.

String Literals:

  • Strings: Sequences of characters enclosed in single quotes (') or double quotes ("), e.g., 'hello', "world", "Python".

Boolean Literals:

  • True and False: Boolean literals representing the truth values True and False, respectively.

None Literal:

  • None: A special literal representing the absence of a value or a null value.

Sequence Literals:

  • Lists: Ordered collections of items enclosed in square brackets ([]), e.g., [1, 2, 3], ['a', 'b', 'c'].
  • Tuples: Ordered collections of items enclosed in parentheses (()), e.g., (1, 2, 3), ('a', 'b', 'c').
  • Sets: Unordered collections of unique items enclosed in curly braces ({}), e.g., {1, 2, 3}, {'a', 'b', 'c'}.
  • Dictionary: Unordered collections of key-value pairs enclosed in curly braces ({}), e.g., {'name': 'John', 'age': 30}, {1: 'a', 2: 'b'}.

Literal Constants:

  • Constants with special meanings in Python, such as True, False, and None.

These literals represent fixed values and can be used directly in Python code without any further evaluation or computation. They are essential building blocks for defining data and expressions in Python programs.

10. What is PEP 8?

PEP 8 is a style guide for Python code written by Guido van Rossum, Barry Warsaw, and Nick Coghlan. PEP stands for Python Enhancement Proposal, and PEP 8 specifically addresses the conventions for writing Python code to improve its readability and consistency.

Here are some key points covered by PEP 8:

  1. Indentation: Use 4 spaces for indentation, not tabs. Continuation lines should align vertically with the opening delimiter.
  2. Whitespace: Use spaces instead of tabs for indentation. Use a single space after commas, colons, and semicolons within data structures. Avoid trailing whitespace at the end of lines.
  3. Naming Conventions: Use lowercase for variable names, ALL_CAPS for constants, CamelCase for class names, and snake_case for function names and method names.
  4. Comments: Write comments to explain non-obvious parts of your code. Use complete sentences and write comments on a separate line rather than on the same line as code.
  5. Line Length: Limit lines to 79 characters to ensure readability in standard terminal windows. Break lines using parentheses or backslashes for long expressions.
  6. Imports: Import modules and packages on separate lines. Put standard library imports first, followed by related third-party imports, and finally local application/library-specific imports.
  7. Whitespace in Expressions and Statements: Use whitespace around operators and after commas in data structures to improve readability.
  8. Documentation Strings (Docstrings): Write docstrings for all public modules, functions, classes, and methods. Use triple quotes for docstrings, and follow the conventions outlined in PEP 257.
  9. Function and Method Annotations: PEP 8 recommends using type hints to annotate function and method parameters and return values. However, this is not mandatory.
  10. Error Handling: Use the try, except, and finally statements for error handling. Avoid using bare except clauses and be specific about the exceptions you catch.

PEP 8 provides guidelines rather than strict rules, and adherence to these guidelines helps to produce code that is consistent, readable, and maintainable. Many Python developers and organizations follow PEP 8 to ensure code quality and readability in their projects.

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