100+ Python Developer Intern Interview Questions and Answers

A set of questions related to Python programming language and its concepts.

• Python is a high-level, interpreted programming language.

• A list is a collection of ordered and mutable elements.

• A tuple is a collection of ordered and immutable elements.

• A set is an unordered collection of unique elements.

• Lists are mutable while tuples are immutable.

• A class is a blueprint for creating objects, while an object is an instance of a class.

• Lambda function is an anonymous function that can h...read more

Create a Django CRUD application with models for employee and category, allowing users to view employee details based on selected category.

• Create models for employee and category in Django.

• Implement CRUD operations for employee model.

• Use category model to create new categories.

• Display employee details based on selected category.

Endpoints and methods for a book store API

• Endpoints: /books (GET, POST), /books/{id} (GET, PUT, DELETE), /authors (GET, POST), /authors/{id} (GET, PUT, DELETE)

• Methods: GET (retrieve data), POST (create new data), PUT (update existing data), DELETE (delete data)

• Example: GET /books - retrieve all books, POST /books - add a new book to the store

Django's ORM can be used to find product details by querying the database.

• Define a model for products in models.py

• Create a view function to handle the request

• Use the ORM to query the database for the product details

• Render the details in a template

Flask is a lightweight web framework for Python that allows you to easily handle HTTP requests.

• Create a Flask application by importing the Flask class

• Define routes using the @app.route decorator

• Handle different types of requests (GET, POST, etc.) using route functions

• Return responses using the jsonify function for JSON data

• Run the Flask application using the app.run() method

Static typing requires variable types to be declared at compile time, while dynamic typing allows types to be determined at runtime.

• Static typing requires explicit declaration of variable types, while dynamic typing infers types at runtime.

• Static typing helps catch errors at compile time, while dynamic typing may lead to runtime errors.

• Python is dynamically typed, but can be used with type hints for static type checking.

• Example of static typing: int x = 5

• Example of dynamic ty...read more

Consider input validation, handling leap years, and using datetime library for accurate date calculations.

• Validate input date format and ensure it is a valid date

• Account for leap years when adding days to the date

• Use datetime library in Python for accurate date calculations

Python ML can be used to find twisted data by detecting anomalies and outliers in the dataset.

• Use anomaly detection algorithms like Isolation Forest, Local Outlier Factor, or One-Class SVM.

• Visualize the data using scatter plots or box plots to identify any outliers.

• Preprocess the data by removing any missing values or scaling the features.

• Use clustering algorithms like K-Means or DBSCAN to group similar data points and identify any anomalies.

• Use dimensionality reduction techn...read more

Lists are mutable, ordered collections of items, while tuples are immutable, ordered collections of items.

• Lists are defined using square brackets [], while tuples are defined using parentheses ().

• Lists can be modified after creation (mutable), while tuples cannot be modified (immutable).

• Lists are typically used for collections of similar items that may need to be changed, while tuples are used for fixed collections of items.

• Example: list_example = [1, 2, 3] and tuple_example ...read more

CSV files are simple, human-readable formats for storing and exchanging structured data efficiently.

• Easy to read and write: CSV files can be easily created and edited using text editors or spreadsheet software like Excel.

• Lightweight: CSV files are generally smaller in size compared to other formats like JSON or XML, making them faster to load and process.

• Compatibility: CSV is a widely accepted format, making it easy to import and export data across different systems and progr...read more

A module in Python is a file containing Python code that can define functions, classes, and variables, and can be imported into other modules.

• Modules are created by saving Python code in a .py file.

• You can import a module using the 'import' statement, e.g., 'import math'.

• To import specific functions or variables, use 'from module import function', e.g., 'from math import sqrt'.

• Modules can also be imported with an alias using 'import module as alias', e.g., 'import numpy as np...read more

The `__init__` method initializes a new object's attributes when an instance of a class is created.

• The `__init__` method is a special method in Python classes, known as a constructor.

• It is automatically called when a new instance of a class is created.

• The primary purpose is to initialize the object's attributes with specific values.

• Example: In a class `Person`, `__init__` can set `name` and `age` attributes.

• Usage: `def __init__(self, name, age): self.name = name; self.age = a...read more

My project utilizes various Python modules for data processing, web scraping, and machine learning tasks.

• 1. NumPy: Used for numerical computations and handling arrays efficiently. Example: Performing matrix operations.

• 2. Pandas: Utilized for data manipulation and analysis. Example: Reading CSV files and performing data cleaning.

• 3. Requests: A library for making HTTP requests to interact with APIs. Example: Fetching data from a web service.

• 4. BeautifulSoup: Used for web scrapi...read more

Multi threading and Multiprocessing are techniques used in Python to achieve parallelism and improve performance.

• Multi threading allows multiple threads to run concurrently within the same process, sharing the same memory space.

• Multiprocessing involves creating multiple processes to run tasks in parallel, each with its own memory space.

• Multi threading is more suitable for I/O-bound tasks, while multiprocessing is better for CPU-bound tasks.

• Example: Using threading module for ...read more

Joins in SQL are used to combine rows from two or more tables based on a related column between them.

• Joins in SQL are used to retrieve data from multiple tables based on a related column.

• Types of joins include inner join, left join, right join, and full join.

• Union combines the result sets of two or more SELECT statements, while full join returns all rows when there is a match in either table.

I am eager to join this internship to deepen my skills in Python and contribute to innovative ML/AI projects.

• I have a strong foundation in Python programming, which I want to apply in real-world ML/AI scenarios.

• I am passionate about data analysis and have completed projects using libraries like Pandas and NumPy.

• I am excited to learn from experienced professionals and collaborate on cutting-edge AI solutions.

• I aim to contribute to impactful projects, such as developing predict...read more

Lists and tuples are both sequence data types in Python, but the main difference is that lists are mutable while tuples are immutable.

• Lists are enclosed in square brackets [], while tuples are enclosed in parentheses ().

• Lists can be modified by adding, removing, or changing elements, while tuples cannot be modified once created.

• Lists are typically used for collections of similar items, while tuples are used for heterogeneous data.

• Lists have more built-in methods and are more ...read more

In Python, the keyword 'def' is used to define a function, followed by the function name and parentheses.

• The 'def' keyword starts the function definition.

• Example: def my_function(): defines a function named my_function.

• Functions can take parameters: def add(a, b): adds two numbers.

• The function body is indented under the 'def' line.

• Functions can return values using the 'return' statement.

Python is a high-level programming language known for its simplicity and readability. Lists and sets are two different data structures in Python.

• Python is a high-level, interpreted, and general-purpose programming language.

• Lists are ordered collections of items that allow duplicates, while sets are unordered collections of unique items.

• Lists are defined using square brackets [], while sets are defined using curly braces {}.

• Example: list_example = [1, 2, 3, 4, 5] and set_examp...read more

I am passionate about coding and enjoy problem-solving using Python.

• I have a strong interest in programming and have been learning Python for a while.

• I find Python to be a versatile and powerful language that can be used in various applications.

• I enjoy the challenge of writing efficient and clean code to solve complex problems.

• I believe working as a Python developer will allow me to further enhance my skills and contribute to innovative projects.

Multithreading and multiprocessing are techniques to achieve concurrent execution in programs, improving performance and resource utilization.

• Multithreading allows multiple threads to run in a single process, sharing memory space.

• Example: A web server handling multiple requests simultaneously using threads.

• Multiprocessing involves multiple processes, each with its own memory space, running independently.

• Example: A data processing application using separate processes for diffe...read more

Exceptions in Python are handled using try-except blocks to catch and handle errors gracefully.

• Use try-except blocks to catch and handle exceptions in Python.

• Specify the type of exception to catch or use 'except' to catch all exceptions.

• Use 'finally' block to execute code regardless of whether an exception was raised or not.

• Reraise exceptions using 'raise' keyword within except block if needed.

• Handle specific exceptions with multiple except blocks or use 'as' keyword to acces...read more

Polymorphism in C++ can be implemented using function overloading, function overriding, and virtual functions.

• Use function overloading to have multiple functions with the same name but different parameters.

• Use function overriding to have a function in a derived class with the same name and parameters as a function in the base class.

• Use virtual functions to achieve runtime polymorphism by allowing a function in a base class to be overridden in a derived class.

Deep copy creates a new object with copies of nested objects, while shallow copy copies references to nested objects.

• Shallow copy: Uses the same references for nested objects. Example: `list2 = list1.copy()`.

• Deep copy: Creates entirely new objects for nested structures. Example: `import copy; list2 = copy.deepcopy(list1)`.

• Modifying a nested object in a shallow copy affects the original. In deep copy, it does not.

• Shallow copy is faster and uses less memory, while deep copy is ...read more

List comprehension is a concise way to create lists in Python, ideal for transforming and filtering data efficiently.

• Creating a new list from an existing one: squares = [x**2 for x in range(10)]

• Filtering elements: evens = [x for x in range(10) if x % 2 == 0]

• Flattening a list of lists: flat = [item for sublist in nested for item in sublist]

• Applying a function to each element: lengths = [len(word) for word in ['apple', 'banana', 'cherry']]

Lists are mutable and can change, while tuples are immutable and cannot be altered after creation.

• Mutability: Lists are mutable (e.g., list.append(4)), tuples are immutable (e.g., tuple[0] = 1 raises an error).

• Syntax: Lists use square brackets (e.g., my_list = [1, 2, 3]), tuples use parentheses (e.g., my_tuple = (1, 2, 3)).

• Performance: Tuples are generally faster than lists due to their immutability.

• Use Cases: Use lists for collections that may change, tuples for fixed collec...read more

The Fibonacci sequence is generated using a recursive or iterative function in Python, producing a series of numbers where each is the sum of the two preceding ones.

• Fibonacci sequence starts with 0 and 1.

• Recursive function example: def fib(n): return n if n <= 1 else fib(n-1) + fib(n-2).

• Iterative function example: def fib(n): a, b = 0, 1; for _ in range(n): a, b = b, a + b; return a.

• Using memoization can optimize the recursive approach.

• The sequence can also be generated using...read more