Python technical interview questions and answers are widely asked in software development, data science, automation, and machine learning roles. Python is one of the most popular languages due to its simplicity and power, so companies expect candidates to understand core topics like variables, loops, functions, OOP, modules, file handling, exceptions, and libraries. Companies like TCS, Infosys, Cognizant, Capgemini, and Wipro frequently include Python questions in interviews and online tests. This guide provides clear explanations for the most important Python questions, making it useful for freshers preparing for placement interviews as well as experienced developers. You can also download Python interview PDFs and practice coding exercises to strengthen your preparation.
Python developers can enhance their capabilities by exploring data science applications and machine learning implementations
Showing 10 of 69 questions
51. How does the Python Global Interpreter Lock (GIL) affect multi-threading
The GIL in Python allows only one thread to execute at a time per interpreter process, which can limit the performance of CPU-bound multi-threaded programs. However, it does not affect I/O-bound multi-threaded programs significantly.
52. What are Python decorators and how are they used
Decorators are a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure. They are used by prefixing a function with the @ symbol followed by the decorator function.
53. Describe how Python handles memory management
Python uses automatic memory management, primarily via reference counting and garbage collection. Reference counting keeps track of the number of references to each object in memory, and when an object’s reference count drops to zero, the memory is freed. Garbage collection is used to detect and clean up cycles of references that cannot be freed by reference counting alone.
54. How would you implement a singleton pattern in Python
A singleton pattern in Python can be implemented by defining a class with a class-level attribute that holds the single instance. The __new__ method can be overridden to ensure that no more than one instance is created.
55. Explain the difference between @staticmethod and @classmethod in Python
@staticmethod defines a method that does not require an instance or class reference and cannot modify object state or class state. @classmethod receives the class as the first argument and can modify class state that applies across all instances.
56. How would you optimize Python code to reduce memory usage
To optimize memory usage in Python, you can use generators instead of lists for large data sets, use built-in data structures that are more memory-efficient, apply the del statement to delete unnecessary variables, and use the slots feature in classes to prevent the creation of instance dictionaries.
57. What is the purpose of the yield keyword in Python
The yield keyword in Python is used in a function to make it a generator, which can return a value and later resume execution from where it left off. This allows for the creation of iterators in a memory-efficient manner.
58. Explain how Python’s list comprehension works and provide an example of its use
List comprehension provides a concise way to create lists by embedding an expression inside square brackets, optionally followed by a for clause, and then one or more for or if clauses. Example: [x**2 for x in range(10) if x % 2 == 0] returns [0, 4, 16, 36, 64].
59. How do you handle exceptions in Python and how can you create custom exceptions
Exceptions in Python are handled using try-except blocks. Custom exceptions can be created by defining a new class that inherits from the built-in Exception class, and raising it using the raise statement.
60. What is the difference between is and == in Python
The == operator compares the values of two objects, while the is operator compares their identities, i.e., whether they refer to the same object in memory.