Preparing for a Python interview can be daunting, whether you’re a fresher or experienced professional. This guide covers latest Python interview questions and answers to help you succeed.
Python is a versatile and widely-used programming language known for its simplicity and readability. Basic Python interview questions assess a candidate’s understanding of fundamental concepts and core principles. These questions cover topics such as data types, operators, control flow, and basic syntax. Mastery of these basics is crucial for anyone looking to advance in Python programming and is often the first step in any technical interview process.
Python is a high-level, interpreted, and general-purpose programming language known for its readability and support for multiple programming paradigms.
Key features include simplicity, readability, extensive libraries, dynamic typing, interpreted nature, and support for multiple programming paradigms.
Variables in Python are declared by assigning a value to a name using the ‘=’ operator, for example, x = 10
Common built-in data types include integers (int), floating-point numbers (float), strings (str), lists (list), tuples (tuple), dictionaries (dict), and sets (set).
Comments in Python are written with the # symbol for single-line comments and triple quotes ”’ or “”” for multi-line comments.
#This is single line comment
'''This is
multi line
comment '''
Functions are created using the def keyword, e.g.:
def my_function():
print("Hello, World!")
Modules are imported using the import statement, e.g.:
import math
print(math.sqrt(16))
Data structures are essential for organizing and managing data efficiently in Python. Interview questions in this category focus on core structures like lists, tuples, dictionaries, and sets. Candidates are tested on operations such as insertion, deletion, traversal, and searching, as well as the performance and application of these structures.
Lists are ordered, mutable collections of items, defined using square brackets, e.g., my_list = [1, 2, 3].
Tuples are ordered, immutable collections of items, defined using parentheses, e.g., my_tuple = (1, 2, 3)
A dictionary is an unordered collection of key-value pairs, defined using curly braces, e.g., my_dict = {“key1”: “value1”, “key2”: “value2”}.
A list of dictionaries can be sorted using the sorted() function with a lambda function as the key.
my_list = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 20}]
sorted_list = sorted(my_list, key=lambda x: x["age"])
print(sorted_list)
append() adds a single element to the end of a list, while extend() adds all elements of an iterable to the end of a list.
Tuples are ordered, immutable collections of items, defined using parentheses, e.g., my_tuple = (1, 2, 3)
my_list = [1, 2, 2, 3, 4, 4, 5]
my_list = list(set(my_list))
print(my_list) # Output : [1, 2, 3, 4, 5]
A shallow copy creates a new object but inserts references to the original objects, while a deep copy creates a new object and recursively copies all objects found in the original.
my_list = [1, 2, 2, 3, 4, 4, 5]
# shallow copy :
my_list1 = my_list
# deep copy:
my_list2 = my_list.copy()
Control flow in Python determines the order in which code is executed, encompassing conditional statements, loops, and exception handling. Interview questions in this category assess a candidate’s ability to use if-else statements, for and while loops, and try-except blocks to manage the logical flow of their programs.
‘==’ checks for value equality, while ‘is’ checks for object identity (whether two variables point to the same object in memory).
Exceptions are handled using try, except, and optionally finally blocks, e.g.:
try:
x = 1 / 0
except ZeroDivisionError:
print("Cannot divide by zero")
finally:
print("Execution finished")
Multiple exceptions can be handled using a tuple, e.g.:
try:
x = int("abc")
except (ValueError, TypeError) as e:
print(e)
The with statement simplifies exception handling by encapsulating common preparation and cleanup tasks.
# 1) without using with statement
file = open('file_path', 'w')
file.write('hello world !')
file.close()
# 2) without using with statement
file = open('file_path', 'w')
try:
file.write('hello world')
finally:
file.close()
The pass statement is a null operation used as a placeholder for future code.
The in keyword is used to check if a key exists or not, e.g.:
my_dict = {"a": 1, "b": 2}
if "a" in my_dict:
print("Key exists")
File I/O is performed using the open() function, e.g.:
with open("file.txt", "r") as file:
content = file.read()
for index, value in enumerate(['a', 'b', 'c']):
print(index, value)
Looping questions in Python interviews test your ability to use for-loops and while-loops to iterate over data structures. These questions assess your understanding of loop control, optimization, and nested loops, which are essential for efficient data processing and repetitive tasks.
enumerate() adds a counter to an iterable and returns it as an enumerate object.
x = ('apple', 'banana', 'cherry')
y = enumerate(x)
#output: [(0, 'apple'), (1, 'banana'), (2, 'cherry')]
A list comprehension is a concise way to create lists.
Syntax :
newlist = [expression for item in iterable if condition == True]
Example :
squares = [x**2 for x in range(10)]
The zip function combines multiple iterables into a single iterator of tuples, e.g.:
list1 = [1, 2, 3]
list2 = ['a', 'b', 'c']
zipped = zip(list1, list2)
print(list(zipped))
The itertools module provides functions to create iterators for efficient looping, e.g.:
import itertools
counter = itertools.count(start=10, step=2)
print(next(counter)) # 10
print(next(counter)) # 12
Generator expressions are similar to list comprehensions but use parentheses instead of square brackets, e.g.:
gen_exp = (x**2 for x in range(10))
for num in gen_exp:
print(num)
A generator is a function that returns an iterator using the yield keyword, allowing iteration over a sequence of values.
def generator_name(arg):
# statements
yield something
String handling questions assess your ability to manipulate and process text in Python. Proficiency in these areas is crucial for effective text manipulation and data parsing.
Strings can be concatenated using the ‘+’ operator, e.g.:
str1 = "Hello"
str2 = "World"
result = str1 + " " + str2
Strings can be reversed using slicing, e.g.:
reversed_string = "hello"[::-1]
print(reversed_string) #Output: "olleh"
str() is used for creating readable string representations of objects, while repr() is used for creating unambiguous string representations that can be used to recreate the object.
Anagrams can be checked by sorting and comparing the strings, e.g.:
s1 = input("Enter the first word: ")
s2 = input("Enter the second word: ")
sort_s1 = sorted(s1)
sort_s2 = sorted(s2)
if len(s1) == len(s2) and sort_s1 == sort_s2:
print("Words are anagrams")
else:
print("Words are not anagrams")
OOP questions evaluate your understanding of concepts like classes, objects, inheritance, polymorphism, and encapsulation in Python. Mastery of OOP principles is essential for designing modular, reusable, and maintainable code.
class Car:
def __init__(self, make, model):
self.make = make
self.model = model
def display(self):
print(f"Car: {self.make} {self.model}")
from abc import ABC, abstractmethod
class Shape(ABC):
@abstractmethod
def area(self):
pass
class Animal:
def __init__(self, name):
self.name = name
def speak(self):
raise NotImplementedError
class Dog(Animal):
def speak(self):
return "Woof!"
class Bird:
def fly(self):
print("Flying")
class Airplane:
def fly(self):
print("Flying")
for obj in [Bird(), Airplane()]:
obj.fly()
class Parent:
pass
class Child(Parent):
pass
class Base1:
pass
class Base2:
pass
class Derived(Base1, Base2):
pass
_).__).
An abstract class cannot be instantiated and often contains one or more abstract methods.
from abc import ABC, abstractmethod
class AbstractClassExample(ABC):
@abstractmethod
def do_something(self):
pass
Exceptions are events that occur during the execution of programs that disrupt the normal flow of instructions.
Errors are serious issues arising mostly due to the environment in which an application is running, while exceptions are events that can be handled programmatically.
class Example:
def __init__(self):
self.data = 42
class Example:
def __init__(self, data):
self.data = data
Encapsulation is the bundling of data and methods that operate on the data within one unit, e.g., a class, restricting access to some of the object’s components.
Using the super() function:
class Parent:
def display(self):
print("Parent method")
class Child(Parent):
def display(self):
super().display()
print("Child method")
The self keyword is used to represent an instance (object) of the class and allows access to the attributes and methods of the class.
Decorators are functions that modify the functionality of another function, method, or class.
Decorators are applied using the @decorator_name syntax above the function definition.
Special methods (also known as dunder methods) are predefined methods in Python classes that start and end with double underscores, e.g., __init__, __str__, __repr__, etc.
def decor(func):
def decorated_func():
print("I got decorated")
func()
return decorated_func
@decor
def ordinary():
print("I am ordinary")
ordinary()
# output : I got deocrated
# I am ordinary
Advanced Python questions delve into complex topics like memory management, metaclasses, decorators, and concurrency. These questions assess your deep understanding of Python’s inner workings and your ability to tackle sophisticated programming challenges.
The GIL is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes simultaneously, thus limiting the concurrency in Python multithreaded programs.
Python uses automatic memory management through reference counting and garbage collection to manage the allocation and deallocation of memory.
Virtual environments are created using venv or virtualenv, e.g.:
python -m venv myenv
A metaclass is a class of a class that defines how a class behaves. A class is an instance of a metaclass.
Python built-in modules are pre-installed set of libraries that come with the Python installation.
Examples include sys, os, math, datetime, itertools, functools, etc.
__init__ initializes a newly created object, while __new__ is responsible for creating a new instance of the class.
@staticmethod defines a method that does not operate on an instance or class, while @classmethod takes a class as its first parameter and can operate on the class itself.
None is a special constant in Python representing the absence of a value or a null value.
Abstract classes are created using the ABC module from the abc library and must include at least one abstract method using the @abstractmethod decorator.
Shallow copy using copy.copy() and deep copy using copy.deepcopy().
Static type checking can be performed using type hints and tools like mypy.
The model that you construct in data science are based on algorithms, so knowledge of algorithm is core concept of data science. Algorithm-specific questions are mentioned below :
Built-in functions include map(), filter(), reduce(), and lambda.
A lambda function is an anonymous function defined with the lambda keyword, e.g.:
add = lambda x, y: x + y
print(add(2, 3)) # 5
Miscellaneous questions cover a wide range of Python topics that don’t fit neatly into other categories. These may include Python’s built-in functions, module usage, best practices, and various utility features essential for well-rounded Python proficiency.
The __name__ variable determines if a module is being run directly or imported, e.g.:
if __name__ == "__main__":
# Code block
The itertools module provides functions to create iterators for efficient looping, e.g.:
import itertools
counter = itertools.count(start=10, step=2)
print(next(counter)) # 10
print(next(counter)) # 12
Variables can be swapped using tuple unpacking, e.g.:
a, b = b, a
Python uses the LEGB (Local, Enclosing, Global, Built-in) rule for variable scope resolution.
Custom exceptions are created by subclassing the Exception class, e.g.:
class MyCustomException(Exception):
pass
A context manager is a construct that allows the setup and cleanup of resources, typically used with the with statement.
Mastering the questions and concepts outlined in this guide is essential for excelling in Python interviews. By thoroughly preparing with these common interview questions and their answers, you’ll be well-equipped to showcase your expertise and confidence during the interview process.
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