Python Interview Questions for Freshers

Python Interview Questions for Freshers Python has become one of the most popular languages in IT sector due to its simplicity and usage. It consists of many libraries that can be used in various fields; it might be web development, artificial intelligence, machine learning, and data science. Python has become one of the finest language for freshers to enter into an industry and grow in their career.

But, it has become little difficult to many freshers to crack the interview and get into the job. It is very important to prepare ourselves in order to crack the interview. So, we are providing some interview questions here, which can be asked for both a fresher and experienced. We hope these questions and answers will help you prepare for the interview and get placed in a job.

Without no more discussions, let’s dive into some Python Interview Questions and Answers.

1. What is Python? List some popular applications of Python in the world of technology.
   Ans: Python is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation. It was designed with an emphasis on code readability, and its syntax allows programmers to express their concepts in fewer lines of code. It is used for:
   System Scripting, Web Development, Game Development, Software Development, Complex Mathematics

2. Is Python a compiled language or an interpreted language?
   Ans: Python is a partially compiled language and partially interpreted language. The compilation part is done first when we execute our code and this will generate byte code internally this byte code gets converted by the Python virtual machine (p.v.m) according to the underlying platform.

3. What is the difference between a Mutable datatype and an Immutable data type?
   

   Ans: Mutable data types can be edited i.e., they can change at runtime. Eg – List, Dictionary, etc.
   Immutable data types cannot be edited i.e., they cannot change at runtime. Eg – String, Tuple, etc.

4. How are arguments passed by value or by reference in Python?
   

   Ans: Everything in Python is an object and all variables hold references to the objects. The reference values are according to the functions; as a result, you cannot change the value of the references. However, you can change the objects if it is mutable.

5. What is the difference between a Set and a dictionary?

   Ans: The set is an unordered collection of data types that is iterable, mutable and has no duplicate elements.
   A dictionary in Python is an ordered collection of data values, used to store data values like a map.

6. Explain the differences between lists and tuples in Python.
   

   Ans: In Python, both lists and tuples are used to store collections of items. The key differences between them are mutability, performance, and, of course, the syntax. Lists are mutable, meaning we can modify their contents (add, remove, or change items). In contrast, Tuples are immutable; their contents cannot be changed once created. Therefore, Tuples is slightly faster than lists due to its immutability.
   Lists are defined with square brackets [], while tuples are defined with parentheses ().

7. Can you tell me about the usage of Lists and Tuples in Python?
   

   Ans: Lists are used when the data may change over time. Unlike lists, tuples are preferred for data that should remain constant throughout the program and can be used as keys in dictionaries.

8. How is memory managed in Python?
   

   Ans: Python manages memory automatically through the use of a private heap containing all Python objects and data structures. The management of this private heap is ensured by the Python memory manager. The key components of Python’s memory management include automatic garbage collection, memory pools, and dynamic typing. Python utilizes reference counting to detect and reclaim memory from unused objects, along with a cyclic garbage collector to free up circular references.
   Moreover, Python organizes the memory in the heap into pools, which are blocks of memory for objects of similar size, managed by the “pymalloc” algorithm to minimize overhead. Another important aspect is that in Python, the memory allocation and deallocation for objects are handled dynamically, ensuring efficient memory use and reducing memory waste.

9. What do you mean by Python literals?
   

   Ans: Literals can be defined as a data which is given in a variable or constant. Python supports the following literals:
   String Literals: Text can be enclosed in either single or double quotes to create string literals. String literals, for instance, are values for strings.
   Numeric Literals: Python supports three types of numeric literals integer, float and complex.
   Boolean Literals: Boolean literals are used to denote Boolean values. It contains either True or False.
   Special Literals: Python contains one unique exacting, or at least, ‘None’. This exceptional strict is utilized for characterizing an invalid variable. In the event that ‘None’ is contrasted and whatever else other than a ‘None’, it will get back to false.

10. What is List Comprehension? Give an Example.
    

    Ans: List comprehension is a syntax construction to ease the creation of a list based on existing iterable.
    For Example: my_list = [i for i in range(1, 10)].

11. What is a pass in Python?
    

    Ans: Pass means performing no operation, or, in other words, it is a placeholder in the compound statement, where there should be a blank left and nothing has to be written there.

12. Describe the Python Functions?
    

    Ans: The Python functions are named blocks of code which perform a single operation. They are defined by the `def` keyword, which is followed by a function name, parameters inside parentheses, and a colon Within the body of the function, the code that gets executed when the function is invoked is indented. Functions can take input arguments, carry out operations and optionally return the results by using the ‘return’ statement. They encourage code reusability, organization, and abstraction.
    Functions fall into three categories:
    Built-in Functions: duplicate (), Len (), count () are the a few implicit capabilities.
    User-defined Functions: User-defined functions are functions that are defined by a user.
    Anonymous functions: Because they are not declared using the standard def keyword, these functions are also referred to as lambda functions.

13. What is Python’s parameter passing system?
    

    Ans: In Python, there are two mechanisms for passing parameters:
    • Pass by references
    • Pass by value
    By default, all arguments (parameters) are passed to the functions “by reference.” In this manner, in the event that you change the worth of the boundary inside a capability, the change is reflected in the calling capability too. It shows the first factor. For instance, if a variable is passed to a function with the declaration “a = 10” and its value is changed to “a = 20,” The same value is represented by both variables. The pass by esteem is that at whatever point we pass the contentions to the capability, just qualities pass to the capability, no reference passes to the capability. It makes it unchangeable, or immutable. The original value of either variable remains the same despite being modified in the function.
    Python has a default contention idea which assists with calling a technique utilizing an erratic number of contentions.

14. What is the difference between a Shallow Copy and a Deep Copy?
    

    Ans: The key difference between a shallow copy and a deep copy is how they handle references within the copied object.
    A shallow copy creates a new object but only copies the references to the objects within the original object, not the actual nested objects. As a result, modifying a mutable object inside the shallow copy will also reflect in the original object because both objects share references to the same nested items. Shallow copies can be created using the copy () method or the copy module in Python.
    A deep copy creates a new object and recursively copies all the objects inside the original object. This means that changes to the deep copy won’t affect the original object, as they are entirely independent. Deep copies can be made using the deepcopy() method from the copy module.

15. How is Multithreading achieved in Python?
    

    Ans: Multithreading in Python is achieved using the threading module, which allows you to run multiple threads (smaller units of a process) concurrently. However, due to the Global Interpreter Lock (GIL), Python threads do not run in true parallelism for CPU-bound tasks. Still, they can be helpful in I/O-bound operations like file handling, network requests, or database queries.
    Here’s how you can achieve multithreading in Python:
    Using the Threading Module: The threading module provides a way to create and manage threads. You can create a new thread by defining a target function and starting the thread.
    Using Subclassing: We can also subclass the Thread class to create your own threads.
    Thread Synchronization: To prevent race conditions, Python provides synchronization primitives like locks (threading.Lock()), which ensure that only one thread accesses a shared resource at a time.

These Python Interview Questions will help you to prepare for your interview and crack the interview to be placed in a job. Not only just these questions, but many more to come. We will discuss more Interview questions in our other posts. To do not miss any of the post keep checking our website and stay connected with us.

All the best for your preparation.