Top Python Interview Questions and Answers

Python is interpreted, and it runs the code line by line without compiling.

Example: If there’s an error at line 5, Python won’t show it until it reaches that line.
 

The list is ordered and changeable.
A tuple is ordered but can’t be changed.
The set is unordered, and no duplicates are allowed.

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Mutables can change after creation. (Example: List)
Immutable can’t change once made. (Example: Tuple)

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Local scope means inside a function. Global scope refers to outside all functions.

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__init__() is like a setup function for a class. It runs automatically when you make an object.

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Use + or extend().

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/ gives float division (decimal).
// gives floor division (whole number).

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pass means “do nothing”. Used as a placeholder.

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Python uses indentation to define blocks of code. If it's wrong, we’ll get an error, or the code won’t run as expected.

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An iterator is an object that lets the code loop over elements one at a time.
It uses two methods: __iter__() and __next__().

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A lambda is a small, one-line anonymous function. Use it when you need a quick function for simple tasks.

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*args lets you pass a variable number of positional arguments to a function. These arguments are gathered into a tuple.
**kwargs allows you to pass any number of keyword arguments, which are collected into a dictionary.

Example:
 

*args → packs positional arguments → ('Hi', 'Hello')
**kwargs → packs keyword arguments → {'name': 'Tom', 'age': 25'}
 

A generator is a function that gives back values one at a time using yield.
It doesn’t store all values in memory like a normal function.

Normal Function Example
 

Output

Normal Function Output
0
1
2

Generator Function Example

Output

Generator Function Output
0
1
2

The two functions have the same output, but are implemented differently. A 'normal' function builds the entire list in memory first and returns the completed list. 
A generator function produces values one at a time, pausing after each value is yielded, until the next value is needed through an invocation of next(). This makes generators memory efficient and great when working with large data or infinite data sets. Therefore, although it looks the same, generators are normally more efficient.

A context manager handles setup and cleanup actions like opening and closing files. It uses __enter__() to start and __exit__() to finish.

Example:
 

Output

Starting the context
Inside the context
Ending the context

Shallow copy copies top-level only (inner lists stay linked).
Deep copy copies everything (makes a full, separate copy).
 

Mutable default arguments cause bugs because Python uses the same object for all calls.

Here, Python creates the default list (item_list=[]) only once when the function is defined, not every time it's called. So, every call shares the same list, causing old data to appear in new calls.

Never use mutable types (like lists, dicts) as default arguments.
 

A KeyError happens when you ask for a dictionary key that doesn’t exist. 
To handle it, we can add an error handler block in the code. In the code below, when the key “city” isn’t available, the error block will run.

GIL stands for Global Interpreter Lock, which is a lock in CPython that ensures that only one thread can execute Python code at any given time. The GIL prevents problems with memory management, but it limits the level of performance of multi-threading programs that are CPU-bound. I/O-bound threads can continue to run without any limitations. 
As a workaround, use multiprocessing for CPU-heavy tasks.
 

Functions as first-class objects means functions can be assigned to variables, passed as arguments, or returned from other functions.

A decorator in Python is a unique kind of function that adds functionality to another function or method, without actually modifying the original function. Decorators are generally used for logging, access control, caching, and some other tasks in cleaner and reusable ways.
 

Output
Before the function runs
Hello!
After the function runs
 

A metaclass is a special class in Python that specifies how classes are built. It can be used to customize or alter the behavior of classes upon creation. 
You would use a metaclass if you want to enforce rules, inject code, or create a flexible framework.
 

This example uses a metaclass, AddMethodMeta, to inject a greet() method into any class that uses it. You don’t write the method in the class; it’s added during class creation, making your code cleaner and reusable.
 

Local Namespace is created inside a function. It holds variables defined within that function.
Global Namespace is created at the top level of a script or module. It holds global variables and functions.
Built-in Namespace comes with Python by default. Includes built-in functions and exceptions like print(), len(), and ValueError.

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Instance variables belong to each object. Class variables are shared by all objects of the class.

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Method overriding takes place when a child class overrides a method inherited from the parent class. When a method is called, the child's method runs rather than the parent's method, allowing for personalized behavior in the subclasses.
 

The Dog class replaces the sound() method of the Animal class. When called from a Dog object, the child class method runs, not the parent one. This is called method overriding in inheritance.
 

Open the file in binary mode using 'rb' or 'wb'.

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Multithreading runs multiple threads in the same process, but only one runs Python code at a time due to the GIL (Global Interpreter Lock).
Multiprocessing runs in separate processes, allowing true parallel execution and bypassing GIL limits.
Use multiprocessing for CPU-heavy tasks.
GIL is a lock in CPython that allows only one thread to execute Python bytecode at a time, even on multi-core processors. It prevents true parallelism in Python threads.
 

map() applies a function to each item.
filter() keeps items that pass a test.
reduce() combines all items into one result (needs functools.reduce).

Example:
 

Define __enter__() and __exit__() methods.
 

# Output:
# Entering the context
# Resource Ready
# Exiting the context
 

Read line by line using a loop.
Example: