100+ Real-time Python Interview Questions and Answers [2023]

Are you preparing for Python interviews? Or just curious to know how much Python you know? No, problem. Here we cover your problems with questions and answers.

The article will help you understand what type of question you might face in interviews. Or helps you evaluate your Python skills. Make sure your answer the questions before seeing the answers to evaluate yourself accurately. Without any further ado, let’s dive into the questions.

The questions are divided into different sections based on the type of topics. Each section has questions along with curated answers. You may modify the answer with your own language with the same meaning. So, the interviewer won’t feel that you are reading something.

What is Python?

Answer: Python is an interpreted high-level, general-purpose programming language. We can build almost any type of application using Python with third-party libraries and frameworks. Python is one of the most popular programming languages in advanced technologies like AI, Data Science, etc.

What is the main difference between an interpreter and a compiler?

Answer: The interpreter translates one statement at a time into machine code, whereas the compiler translates the entire code at a time into machine code.

Is Python statically typed or dynamically typed language?

Answer: Python is a dynamically typed language.

What do you mean by dynamically typed language?

Answer: Dynamically typed languages check the types of variables at run-time. Some dynamically typed languages are Python, JavaScript, Ruby, etc.

Bonus: Statically typed languages check the types of variables at compile-time. Some statically typed languages are C++, C, Java, etc..,

Give some applications of Python.

Answer: Python has simpler and easy-to-learn syntax. It may look similar to English. The community of developers for Python is huge. We can find many third-party packages to work with different types of application development. When it comes to development, we can create web applications, GUI applications, CLI applications, etc..,

One of the most popular applications of Python is automation. We can easily create scripts in Python to automate tasks like cleaning disk, send mails, getting data about product prices, etc..,

Python is one of the most popular languages to use in the field of Data Science.

What applications did you build using Python?

Answer: I have written multiple automation scripts to eliminate repetitive and boring tasks. And scripts to get info about product prices, availability, etc.

I have also worked with the frameworks like Django, Flask to build web applications. And build some web applications using both Django and Flask.

Note: The above answer is an example. Your answer may be completely different from the one above. Try to explain different areas that you have worked on using Python. Show the applications if they are available.

What are the built-in data types in Python?

Answer: There are multiples built-in data types in Python. They are int,  float, complex, bool, list, tuple, set, dict, str.

Note: You don’t have to tell all the data types present in Python. Mention some of them you mostly use. The interviewer may ask questions based on your answer.

Both list and tuple are used to store the collection of objects. The main difference between the list and tuple is “the list is mutable object whereas tuple is an immutable object”.

What are mutable and immutable data types?

Answer: Mutable data types can be changed after creating them. Some of the mutable objects in Python are list, set, dict.

Immutable data types can’t be changed after creating them. Some of the immutable objects in Python are str, tuple.

Explain some methods of the list.

Answer:

1. append – the method is used to add an element to the list. It adds the element to the end of the list.

>>> a = [1, 2]
>>> a.append(3)
>>> a
[1, 2, 3]

2. pop – the method is used to remove an element from the list. It will remove the last element if we don’t provide any index as an argument or remove the element at the index if we provide an argument.

>>> a = [1, 2, 3, 4, 5]
>>> a.pop()
5
>>> a
[1, 2, 3, 4]
>>> a.pop(1)
2
>>> a
[1, 3, 4]

3. remove – the method is used to remove an element from the list. We need to provide the element as an argument that we want to remove from the list. It removes the first occurrence of the element from the list.

>>> a = [1, 2, 2, 3, 4]
>>> a = [1, 2, 3, 2, 4]
>>> a.remove(1)
>>> a
[2, 3, 2, 4]
>>> a.remove(2)
>>> a
[3, 2, 4]

4. sort – the method used to sort the list in ascending or descending order.

>>> a = [3, 2, 4, 1]
>>> a.sort()
>>> a
[1, 2, 3, 4]
>>> a.sort(reverse=True)
>>> a
[4, 3, 2, 1]

5. reverse – the method is used to reverse the list elements.

>>> a = [3, 2, 4, 1]
>>> a.reverse()
>>> a
[1, 4, 2, 3]

Note: There are other methods like clear, insert, count, etc… You don’t have to explain every method of the list to the interviewer. Just explain two or three methods that you mostly use.

Explain some methods of string.

Answer:

1. split – the method is used to split the string at desired points. It returns the list as a result. By default, it splits the string at spaces. We can provide the delimiter as an argument for the method.

>>> a = "This is Geekflare"
>>> a.split()
['This', 'is', 'Geekflare']
>>> a = "1, 2, 3, 4, 5, 6"
>>> a.split(", ")
['1', '2', '3', '4', '5', '6']

2. join – the method is used to combine the list of string objects. It combines the string objects with the delimiter we provide.

>>> a = ['This', 'is', 'Geekflare']
>>> ' '.join(a)
'This is Geekflare'
>>> ', '.join(a)
'This, is, Geekflare'

Note: Some other methods of strings are: capitalize, isalnum, isalpha, isdigit, lower, upper, center, etc..,

What’s the negative indexing in lists?

Answer: The index is used to access the element from the lists. Normal indexing of the list starts from 0.

Similar to normal indexing, negative indexing is also used to access the elements from the lists. But, negative indexing allows us to access the index from the end of the list. The start of the negative indexing is -1. And it keeps on increasing like -2, -3, -4, etc.., till the length of the list.

>>> a = [1, 2, 3, 4, 5]
>>> a[-1]
5
>>> a[-3]
3
>>> a[-5]
1

Explain some methods of dict.

Answer:

1. items – the method returns key: value pairs of dictionaries as a list of tuples.

>>> a = {1: 'Geekflare', 2: 'Geekflare Tools', 3: 'Geekflare Online Compiler'}
>>> a.items()
dict_items([(1, 'Geekflare'), (2, 'Geekflare Tools'), (3, 'Geekflare Online Compiler')])

2. pop – the method is used to remove the key: value pair from the dictionary. It accepts the key as an argument and removes it from the dictionary.

>>> a = {1: 2, 2: 3}
>>> a.pop(2)
3
>>> a
{1: 2}

Note: Some other methods of dict are: get, keys, values, clear, etc.

What is slicing in Python?

Answer: Slicing is used to access the subarray from a sequence data type. It returns the data from the sequence data type based on the arguments we provide. It returns the same data type as the source data type.

Slicing accepts three arguments. They are the start index, end index, and increment step. The syntax of slicing is variable[start:end:step]. Arguments are not mandatory for slicing. You can specify an empty colon (:) which returns the entire data as the result.

>>> a = [1, 2, 3, 4, 5]
>>> a[:]
[1, 2, 3, 4, 5]
>>> a[:3]
[1, 2, 3]
>>> a[3:]
[4, 5]
>>> a[0:5:2]
[1, 3, 5]

Which data types allow slicing?

Answer: We can use slicing on list, tuple, and str data types.

What are unpacking operators in Python? How to use them?

Answer: The * and ** operators are unpacking operators in Python.

The * unpacking operator is used to assign multiple values to different values at a time from sequence data types.

>>> items = [1, 2, 3]
>>> a, b, c = items
>>> a
1
>>> b
2
>>> c
3
>>> a, *b = items
>>> a
1
>>> b
[2, 3]

The ** unpacking operator is used with dict data types. The unpacking in dictionaries doesn’t work like unpacking with sequence data types.

The unpacking in dictionaries is mostly used to copy key: value items from one dictionary to another.

>>> a = {1:2, 3:4}
>>> b = {**a}
>>> b
{1: 2, 3: 4}
>>> c = {3:5, 5:6}
>>> b = {**a, **c}
>>> b
{1: 2, 3: 5, 5: 6}

Note: You can refer to this article for more info on these operators.

Does Python have switch statements?

Answer: No, Python doesn’t have switch statements.

How do you implement the functionality of switch statements in Python?

Answer: We can implement the functionality of switch statements using if and elif statements.

>>> if a == 1:
...     print(...)
... elif a == 2:
...     print(....)

What is break and continue statements?

Answer:

break – the break statement is used to terminate the running loop. The execution of the code will jump to the outside of the break loop.

>>> for i in range(5):
...     if i == 3:
...             break
...     print(i)
...
0
1
2

continue – the continue statement is used to skip the execution of the remaining code. The code after the continue statement doesn’t execute in the current iteration, and the execution goes to the next iteration.

>>> for i in range(5):
...     if i == 3:
...             continue
...     print(i)
...
0
1
2
4

When is the code in else executed with while and for loops?

Answer: The code inside the else block with while and for loops is executed after executing all iterations. And the code inside the else block doesn’t execute when we break the loops.

What are list and dictionary comprehensions?

Answer: List and dictionary comprehensions are syntactic sugar for the for-loops.

>>> a = [i for i in range(10)]
>>> a
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> a = {i: i + 1 for i in range(10)}
>>> a
{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10}
>>>

How does the range function work?

Answer: The range function returns the sequence of numbers between the start to stop with a step increment. The syntax of the range function is range(start, stop[, step]).

The stop argument is mandatory. The arguments start and step are optional. The default value of start and step are 0 and 1, respectively.

>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 10))
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 10, 2))
[1, 3, 5, 7, 9]
>>>

What are the parameters and arguments?

Answer: Parameters are the names listed in the function definition.

Arguments are the values passed to the function while invoking.

What are the different types of arguments in Python?

Answer: There are mainly four types of arguments. They are positional arguments, default arguments, keyword arguments, and arbitrary arguments.

Positional Arguments: the normal arguments that we define in user-defined functions are called positional arguments. All positional arguments are required while invoking the function.

>>> def add(a, b):
...     return a + b
...
>>> add(1, 2)
3
>>> add(1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: add() missing 1 required positional argument: 'b'
>>>

Default Arguments: we can provide the value to the arguments in the function definition itself as default value. When the user didn’t pass the value, the function will consider the default value.

>>> def add(a, b=3):
...     return a + b
...
>>> add(1, 2)
3
>>> add(1)
4

Keyword Arguments: we can specify the name of the arguments while invoking the function and assign values to them. The keyword arguments help us to avoid ordering which is mandatory in positional arguments.

>>> def add(a, b):
...     print("a ", a)
...     print("b ", b)
...     return a + b
...
>>> add(b=4, a=2)
a  2
b  4
6

Arbitrary Arguments: we use arbitrary arguments to collect a bunch of values at a time when we don’t know the number of arguments that function will get. We * and ** operators in the function definition to collect the arguments.

>>> def add(*args):
...     return sum(args)
...
>>> add(1, 2, 3, 4, 5)
15
>>> def dict_args(**kwargs):
...     print(kwargs)
...
>>> dict_args(a='Geekflare', b='Geekflare Tools', c='Geekflare Online Compiler')
{'a': 'Geekflare', 'b': 'Geekflare Tools', 'c': 'Geekflare Online Compiler'}

What is the lambda function?

Answer: Lambda functions are small anonymous functions in Python. It has single expressions and accepts multiples arguments.

>>> add = lambda a, b: a + b
>>> add(1, 3)
4

What’s the difference between normal function and lambda function?

Answer: The functionality of both normal functions and lambda functions are similar. But, we need to write some extra code in normal functions compared to lambda functions for the same functionality.

Lambda functions come in handy when there is a single expression.

What is the pass keyword used for?

Answer: The pass keyword is used to mention an empty block in the code. Python doesn’t allow us to leave the blocks without any code. So, the pass statement allows us to define empty blocks (when we decide to fill the code later).

>>> def add(*args):
...
...
  File "<stdin>", line 3

    ^
IndentationError: expected an indented block
>>> def add(*args):
...     pass
...
>>>

What is a recursive function?

Answer: The function calling itself is called a recursive function.

What are packing operators in Python? How to use them?

Answer: The packing operators are used to collect multiple arguments in functions. They are known as arbitrary arguments.

Note: you can refer to this article for more info on packing operators in Python.

What keyword is used to create classes in Python?

Answer: The class keyword is used to create classes in Python. We should follow the pascal case for naming the classes in Python as an industry-standard practice.

>>> class Car:
...     pass
...

How to instantiate a class in Python?

Answer: We can create an instance of a class in Python by simply calling it like function. We can pass the required attributes for the object in the same way as we do for function arguments.

>>> class Car:
...     def __init__(self, color):
...             self.color = color
...
>>> red_car = Car('red')
>>> red_car.color
'red'
>>> green_car = Car('green')
>>> green_car.color
'green'
>>>

What is self in Python?

Answer: The self represents the object of the class. It’s used to access the object attributes and methods inside the class for the particular object.

What is the __init__ method?

Answer: The __init__ is the constructor method similar to the constructors in other OOP languages. It executes immediately when we create an object for the class. It’s used to initialize the initial data for the instance.

What is docstring in Python?

Answer: The documentation strings or docstrings are used to document a code block. They are also used as multi-line comments.

These docstrings are used in the methods of a class to describe what a certain method does. And we can see the method docstring using the help method.

>>> class Car:
...     def __init__(self, color):
...             self.color = color
...
...     def change_color(self, updated_color):
...             """This method changes the color of the car"""
...             self.color = updated_color
...
>>> car = Car('red')
>>> help(car.change_color)
Help on method change_color in module __main__:

change_color(updated_color) method of __main__.Car instance
    This method changes the color of the car

>>>

What are dunder or magic methods?

Answer: The methods having two prefix and suffix underscores are called dunder or magic methods. They are mainly used to override the methods. Some of methods that we can override in classes are __str__, __len__, __setitem__, __getitem__, etc..,

>>> class Car:
...     def __str__(self):
...             return "This is a Car class"
...
>>> car = Car()
>>> print(car)
This is a Car class
>>>

Note: There are a lot of other methods that you can override. They come in handy when you want to customize the code in depth. Explore the documentation for more info.

How do you implement inheritance in Python?

Answer: We can pass the parent class to the child class as an argument. And we can invoke the init method parent class in the child class.

>>> class Animal:
...     def __init__(self, name):
...             self.name = name
...
>>> class Animal:             e):
...     def __init__(self, name):
...             self.name = name
...
...     def display(self):
...             print(self.name)
>>> class Dog(Animal):        e):ame)
...     def __init__(self, name):
...             super().__init__(name)
...
>>> doggy = Dog('Tommy')
>>> doggy.display()
Tommy
>>>

How to access the parent class inside the child class in Python?

Answer: We can use the super(), which refers to the parent class inside the child class. And we can access attributes and methods with it.

How to use single-line and multi-line comments in Python?

Answer: We use hash (#) for single-line comments. And triple single quotes (”’comment”’) or triple double quotes (“comment”) for multi-line comments.

What is an object in Python?

Answer: Everything in Python is an object. All the data types, functions, and classes are objects.

What is the difference between is and ==?

Answer: The == operator is used to check whether two objects have the same value or not. The is operator is used to check whether two objects are referring to the same memory location or not.

>>> a = []
>>> b = []
>>> c = a
>>> a == b
True
>>> a is b
False
>>> a is c
True
>>>

What is shallow and deep copy?

Answer:

Shallow Copy: it creates the exact copy as the original without changing references of the objects. Now, both copied and original objects refer to the same object references. So, changing one object will affect the other.

The copy method from the copy module is used for the shallow copy.

>>> from copy import copy
>>> a = [1, [2, 3]]
>>> b = copy(a)
>>> a[1].append(4)
>>> a
[1, [2, 3, 4]]
>>> b
[1, [2, 3, 4]]

Deep Copy: it copies the values of the original object recursively into the new object. We have to use the slicing or deepcopy function from the copy module for the deep copying.

>>> from copy import deepcopy
>>> a = [1, [2, 3]]
>>> b = deepcopy(a)
>>> a[1].append(4)
>>> a
[1, [2, 3, 4]]
>>> b
[1, [2, 3]]
>>> b[1].append(5)
>>> a
[1, [2, 3, 4]]
>>> b
[1, [2, 3, 5]]
>>>

What are iterators?

Answer: Iterators are objects in Python which remember their state of iteration. It initializes the data with the __iter__ method and returns the next element using the __next__ method.

We need to call the next(iterator) to get the next element from the iterator. And we can convert a sequence data type to an iterator using the iter built-in method.

>>> a = [1, 2]
>>> iterator = iter(a)
>>> next(iterator)
1
>>> next(iterator)
2
>>> next(iterator)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration
>>>

What are generators?

Answer: Generators are the functions that return an iterator like a generator object. It uses the yield to generate the data.

>>> def numbers(n):
...     for i in range(1, n + 1):
...             yield i
...
>>> _10 = numbers(10)
>>> next(_10)
1
>>> next(_10)
2
>>> next(_10)
3
>>> next(_10)
4

What are Python namespaces?

Answer: Consider your phone’s contact list as a namespace. You might have multiple friends named John, but by specifying a contact as ‘John Smith’ or ‘John Doe,’ you can uniquely identify them.

In this example, namespaces provide a method to identify and manage entities with similar names distinctly, thus avoiding confusion and conflicts. Similarly, in Python, namespaces prevent naming clashes and offer a structured approach to organizing variables, functions, classes, and other identifiers within a program.

What is encapsulation? 

Answer: Consider a modern secure door lock system. The lock’s inner mechanisms, such as the electronic keypad and motorized bolt, are encapsulated within the lock. Users interact with the lock through a keypad or a mobile app.

Class: SecureLock
Attributes: 
    - access_codes
    - lock_status
    - user_settings

Methods:
    - enter_code()
    - lock()
    - unlock()
    - get_lock_status()

By encapsulating the lock’s mechanics, users can enjoy a user-friendly experience. They can securely control access to their property without handling the technical complexities within the lock itself. Encapsulation ensures efficient and secure door control.

In summary, encapsulation in Python involves bundling data (Variables) and methods within a class, where private variables are accessed and modified only through methods. This practice enhances data protection, code maintainability, and overall software design.

Explain what inheritance is and its types.

Answer: Inheritance allows programmers to create a class that can acquire the methods and attributes of another class. There are five types of inheritance:

• Single Inheritance: A class inherits attributes and methods from a single base class.

• Multiple Inheritance: A class can inherit from multiple base classes, acquiring their attributes and methods.

• Multilevel Inheritance: In this hierarchy, a derived class serves as the base class for another class.

• Hierarchical Inheritance: Multiple derived classes inherit from a common base class.

• Hybrid Inheritance: A combination of multiple inheritances and other forms of inheritance.

Every form of inheritance provides a unique approach to organizing classes and exchanging functionality within Python programs.

Explain polymorphism in Python.

Answer: Polymorphism in Python is one name and many jobs. It’s like a tool with different uses depending on what’s needed. Handles different data and instructions. Imagine that you have a program that deals with different shapes, like circles and rectangles. You want to calculate their areas. Instead of creating separate functions for each shape, that’s where polymorphism comes in place.

class Shape:
    def calculate_area(self):
        pass

class Circle(Shape):

    def __init__(self, radius):

        self.radius = radius


    def calculate_area(self):

        return 3.14159 * self.radius * self.radius


class Rectangle(Shape):

    def __init__(self, width, height):

        self.width = width

        self.height = height

    def calculate_area(self):

        return self.width * self.height

# Using polymorphism

shapes = [Circle(5), Rectangle(4, 6)]

for shape in shapes:

    area = shape.calculate_area()

    print(f"Area: {area}")

In the above example, the Shape class has a method calculate_area(), which is overridden in the Circle and Rectangle classes. Even though you’re calling the same method (calculate_area()), it behaves differently based on the object’s type (polymorphism). This way, you can handle different shapes using a unified approach.

Explain multithreading in Python.

Answer: Multithreading means a processor doing many tasks at once. Even on a basic, one-part CPU, it handles this by quickly switching between tasks. Python’s threading module makes it easy to use multiple threads. It’s like having different workers working together in a program. Here’s a quick example:

import threading

def task1():

    # Your task here

def task2():

    # Your task here

thread1 = threading.Thread(target=task1)

thread2 = threading.Thread(target=task2)


thread1.start()

thread2.start()


thread1.join()

thread2.join()

print("Both tasks are done")

Explain how Python manages memory.

Answer: Memory management in Python is a private storage location where all Python elements and structures are stored. Python’s memory manager manages this private storage area, which performs various dynamic storage operations such as sharing, dividing, reserving, and caching. This behind-the-scenes manager is divided into several parts, each addressing a different memory management aspect.

Is Python case-sensitive?

Answer: Yes, Python is a case-sensitive language. 

What is the Pythonpath?

Answer: Pythonpath acts like a signpost for the Python interpreter, showing it where to locate different tools and libraries. Think of it as a map for Python to navigate through its world. While other languages have a similar system called PATH, Pythonpath is more versatile, containing extra directories specifically for Python modules.

What are Python modules, packages, and libraries?

Answer:

Modules: A Python module is like a labeled box for your code stuff. It keeps functions, classes, and things you want to remember. You can also put code inside it that runs. It’s like having tidy boxes for your code tools, making your programming easier.

Packages: Packages are collections of related modules grouped together in a directory hierarchy. They allow you to organize your code on a higher level. A package can contain both modules and sub-packages. This helps in creating more structured and organized projects.

Libraries: A library is a collection of modules and packages. It’s like a toolkit that offers a wide range of functionalities, enabling you to perform various tasks without having to reinvent the wheel. Libraries can be built-in (provided by Python itself) or externally (created by other developers). – make it sound simple and understandable.

What are classifiers?

Answer: A classifier is like an intelligent tool in machine learning that figures out what category something belongs to by looking at its unique features. For instance, let’s say you have a bunch of fruits—apples, bananas, and oranges. Each fruit has features like color, shape, and size. A classifier, in this case, would be like a fruit identifier. It examines the characteristics and decides whether a fruit is an apple, a banana, or an orange.

What is the scope in Python? 

Answer: In Python, scope and namespace go hand in hand. Scope decides where a name works in your program. Picture it like areas where a name makes sense. These areas are kept track of using dictionaries, like lists that match names with things. These lists are called namespaces.

What is scope resolution? 

Answer: Scope resolution stands as a vital idea in Python. It decides how the program figures out variables and names as you write your code. This whole process shapes where you can use variables in your program. Knowing how Python handles scopes is critical to creating code that’s neat, fast, and without glitches.

What are the important features of Python? 

Answer: Python is an OOP language that offers numerous features to lighten the load for developers. Here’s a handpicked list of some of the best features:

• Easy to Learn and Read
• Dynamically Typed Programming Language
• Open-Source
• Extensive Library
• Strong Community Support
• GUI Support
• Adaptability to Multiple Platforms
• Effective Memory Management

What are the types of operators in Python? 

Answer: Operators are employed to carry out actions on variables and values. In Python, operators are categorized into the following groups:

• Arithmetic operators
• Assignment operators
• Comparison operators
• Logical operators
• Identity operators
• Membership operators
• Bitwise operators

What are the various join options available in Pandas, and what are their purposes? 

Answer: In Pandas, the join operation combines two or more DataFrames based on their indexes or columns. There are several types of joins available, each serving a distinct purpose:

• Inner Join: Combines matching rows from both DataFrames.
• Left Join: Keeps all rows from the left DataFrame and matching rows from the right.
• Right Join: Keeps all rows from the right DataFrame and matching rows from the left.
• Outer Join: Combines all rows from both DataFrames, filling in missing values with NaN.
• Concatenation: Stacks DataFrames vertically or side by side.

These join options provide multiple possibilities for combining data from numerous sources to meet specific analytical needs.

What are unit tests in Python? 

Answer: Unit tests serve as little checks we create to see if our code is doing its job right. We pick one thing, like a function, and test it separately. These checks are super important because they help us ensure our code works correctly and find any issues before they become big problems.

What is unpickling and pickling? 

Answer: “Pickling” refers to transforming a Python object arrangement into a stream of bytes, while “unpickling,” a stream of bytes sourced from a binary file or bytes-like object, is transformed back into an arrangement of objects. 

What are decorators in Python? 

Answer: In Python, a decorator is like a design trick that lets you enhance an existing object with extra abilities without changing how it’s built. Decorators are typically used before you define a function you want to improve.

How to use decorators in Python? 

Answer: In Python, a decorator is like a design trick that lets you enhance an existing object with extra abilities without changing how it’s built. Decorators are typically used before you define a function you want to improve.

How does the // operator work in Python?

Answer: It is referred to as the Floor Division Operator and falls under the Arithmetic Operator category. This operator conducts division and subsequently rounds down the result to the closest whole number. Here’s an example to provide you with a quick understanding of its functionality.

result = 10 // 3  # 10 divided by 3 is 3.333..., but // rounds down to 3
print(result)     # Output: 3

result = -10 // 3  # -10 divided by 3 is -3.333..., but // still rounds down to -4
print(result)      # Output: -4

How is the pass statement used in Python? 

Answer: The pass statement is like a stand-in for future code. When it’s used, nothing really happens, but it stops errors from popping up when you’re not supposed to have empty code. This rule applies to places like loops, functions, classes, and if statements.

Mention types of sequences in Python. 

Answer: In Python, sequences are like ordered lists of items you can work with. There are different types of sequences:

• Lists: These are collections where you can put various things in a specific order.
• Tuples: Similar to lists, but once you create them, you can’t change what’s inside.
• Strings: These are sequences of characters, which are just letters, numbers, and symbols.
• Ranges: These are sequences of numbers you can generate quickly, like counting from one number to another.
• Bytes and Byte arrays: These sequences handle binary data, which is how computers store information.

These different types of sequences are tools that help programmers work with data in various ways.

Explain monkey patching in Python. 

Answer: Monkey patching in Python involves changing how a module or class behaves while the program is running. You can add, change, or replace things like methods and functions without altering the original code. The name “monkey patching” suggests that you’re making these changes in a fun, informal way, like a monkey playing around.

When might you use monkey patching? Imagine using a tool (a module or class) that does almost what you want, but not quite. You can use monkey patching to tweak it on the fly, fixing problems or adding new features without waiting for an official update. Here’s a simpler explanation of how monkey patching works:

• Getting Ready: You start by bringing in the tool you want to play with (importing the module or class).

• Making Changes: You can then tinker with the tool by adding new parts, adjusting existing ones, or even swapping things out to make it work better for you.

• Instant Playtime: Once you’re done, your modified tool starts working right away. Anyone using the tool after your changes will see the improvements.

As a suggestion, use monkey patching only when you really need it. Consider other options like making new versions of the tool (subclassing), mixing and matching tools (composition), or using built-in Python features (decorators) to keep your code tidy and reliable.

Discuss the difference between Del and Remove().

Answer: Both “del” and “remove()” are used in Python to remove elements from a list, but they work in slightly different ways:

del is a Python statement, not a method. It removes an item from a list by specifying its index. You provide the index of the element you want to remove. It doesn’t return the removed element; it just removes it from the list.

my_list = [10, 20, 30, 40, 50]
del my_list[2]  # Removes the element at index 2 (value 30)

remove() is a list method. It removes an element from the list by specifying its value. You provide the actual value of the element you want to remove. It searches for the first occurrence of the specified value and removes it. If the value doesn’t exist in the list, it raises a ValueError.

my_list = [10, 20, 30, 40, 50]
my_list.remove(30)  # Removes the element with value 30

Use del when you know the index of the element you want to remove, and use remove() when you know the value of the element you want to remove.

Discuss the difference between append() and extend(). 

Answer: In Python, if you want to put something new into a list that’s already there, you use the append() method. But when you use the extend() method, you’re taking each piece from a group you give and adding them all to the end of the initial list.

What Is the difference between range() and xrange() functions? 

Answer: The range() function produces a list containing constant numbers, whereas the xrange() function generates a distinctive type of generator. In memory efficiency, range() utilizes more memory, whereas xrange() is crafted to conserve memory.

Discuss the difference between Python 2.x and Python 3.x.

Answer: Python 2.x and Python 3.x are distinct versions of the Python programming language. They exhibit notable differences: Python 3.x employs a print function (print(“Hello, World”)) as opposed to Python 2. x’s print statement (print “Hello, World”). 

Division behavior varies; Python 2.x truncates integer division (3 / 2 yields 1), while Python 3.x yields floating-point results (3 / 2 yields 1.5). Unicode handling also contrasts; Python 3.x treats all strings as Unicode by default, whereas Python 2.x requires a prefix for Unicode strings. Iterating through dictionaries differs too: Python 2.x iterates keys (for key in dict), and Python 3.x maintains this default but introduces alternatives like .keys(), .values(), and .items(). 

Overall, Python 3.x introduces significant improvements, such as enhanced division and Unicode handling, making it the recommended choice for new projects.

Discuss the difference between .py and .pyc files.

Answer: PY files serve as containers for the human-readable source code of a Python program. They hold the instructions, logic, and algorithms that developers write. However, when Python code is executed, it goes through an intermediate step for optimization and faster execution. This is where .pyc files come into play.

These files contain the bytecode, which is a lower-level representation of the program’s instructions that the Python interpreter can execute directly. So, while PY files are for developers to write and read, .pyc files are generated by the Python interpreter and used for quicker execution.

What is the split() function in Python? 

Answer: The split() function divides a string into a list, allowing you to define the separator. By default, it uses whitespace as the separator.

What is the help() function? 

Answer: The help() function in Python provides information about a given object when invoked. It accepts an optional parameter and furnishes relevant details. In the absence of an argument, it reveals the Python help console.

What is the join() function? 

Answer: The join() function enables the creation of a string by uniting elements from an iterable, employing a selected string separator.

What is the sub() function? 

Answer: The sub() function is a part of Python’s Regular Expressions (re) module. It furnishes a string in which all instances matching the specified pattern are substituted with the provided replacement string.

What is the dir() function? 

Answer: The dir() function provides a list of all properties and methods belonging to a given object, excluding their values. It encompasses both user-defined attributes and methods, as well as inherent built-in properties that are standard for all objects.

What does an object() do? 

Answer: The object() function in Python returns a new empty object of the built-in object type. The object type is the base class for all classes in Python. It doesn’t have any special attributes or methods, but it serves as a fundamental building block for creating custom classes.

What is PEP 8? 

Answer: PEP 8, or “Python Enhancement Proposal 8,” is the style guide for writing readable and consistent Python code. It outlines conventions and recommendations for formatting code, naming conventions, and organizing code to improve code readability and maintainability.

What are *args and *kwargs? 

Answer: *args allows passing a flexible count of arguments without keywords, enabling tuple operations. Meanwhile, **kwargs permits sending varying keyword arguments as a dictionary, allowing dictionary-like operations within a function.

What is a Numpy array? 

Answer: A numpy array is a matrix of uniform values accessed using a tuple of non-negative integers. The array’s rank is determined by its number of dimensions, and its configuration is represented by an integer tuple that specifies sizes along each dimension.

Discuss the difference between Matrices and Arrays.

Answer: Matrices are structured as rows and columns, primarily containing numerical values. They excel in linear algebra tasks like matrix multiplication and solving systems of linear equations.

Arrays, while versatile, can have various data types beyond numbers, making them essential for general-purpose programming. They encompass vectors, matrices, and higher-dimensional structures, serving as fundamental tools for data representation and manipulation in diverse fields such as image processing, scientific computing, and machine learning.

Discuss the difference between a list and a tuple.

Answer: Tuples and lists differ primarily in their mutability. Tuples are static and cannot be modified, while lists can undergo changes. In Python, it’s possible to alter a list, but the immutability of tuples ensures that their contents remain unchanged after creation.

Discuss the difference between NumPy and SciPy. 

Answer: NumPy and SciPy are both Python libraries used extensively for scientific and numerical computing.

NumPy provides the foundation for numerical computations through array operations, while SciPy extends its capabilities by offering specialized functions for a broad range of scientific and engineering applications. They are often used together to cover a wide spectrum of numerical and scientific computing needs in Python.

How to add values and remove values to an array in Python? 

Answer: In Python, arrays can be easily manipulated using lists, a fundamental data structure. Adding elements is straightforward: the append() method adds a value to the list’s end, while the insert() method inserts a value at a specified index. 

Removing elements is equally intuitive: the remove() method deletes the first occurrence of a value, and the pop() method eliminates an element at a given index and returns its value. 

Additionally, the del statement can be used to remove elements by index or to clear the entire list. These techniques provide flexible means to modify arrays, adapting them to specific programming needs. For more complex array operations, libraries like NumPy offer advanced functionalities.

How do you create a class in Python? 

Answer: To create a class in Python, you use the class keyword followed by the class name and a colon. The class body is indented and contains the attributes and methods that define the class. Here’s a simple example of a Person class with a constructor and a method:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age
    
    def greet(self):
        print(f"Hello, my name is {self.name} and I am {self.age} years old.")

# Creating an instance of the Person class
person1 = Person("Alice", 30)

# Calling the greet method
person1.greet()

This creates a class called Person with an initializer that sets the name and age attributes and a greet method that prints a greeting using those attributes.

Explain how to locate the positions of values that are divisible by three in a Series using Python. 

Answer: To locate the positions of values that are divisible by three in a series using Python, you can use the “Pandas” library. First, you need to create a series with your data and then use Boolean indexing to filter the positions where the values are divisible by three. 

import pandas as pd

# Create a Series with some data
data = [10, 5, 9, 12, 6, 8, 3, 7, 18]
my_series = pd.Series(data)

# Find positions where values are divisible by three
divisible_by_three_positions = my_series[my_series % 3 == 0]

# Print the positions
print("Positions of values divisible by three:", divisible_by_three_positions)

How to find the Euclidean distance between two series in Python? 

Answer: The expression “norm(x-y))” calculates the Euclidean distance between two series objects by utilizing the np.linalg.norm() function from the NumPy library. This function computes the Euclidean distance by evaluating the norm between the vectors created from ‘x’ and ‘y’ values.

Explain how to swap out a substring with a new string in Python. 

Answer: Utilize the replace() method for substituting substrings. Indicate the original string ‘old’ as the initial parameter and the replacement string ‘new’ as the second parameter. To eliminate ‘old’, set ‘new’ as an empty string “.

Explain how to copy objects in Python.

Answer: In Python, the = operator is employed to create a replica of an object. Nonetheless, it can be mistakenly interpreted as producing an entirely new object, which is not accurate. Instead, it produces a new variable that references the same object as the original. To exemplify, envision a situation where we create a list named “old_list” and then assign the object reference to “new_list” using the = operator.

How can I read and write files in Python? 

Answer: In Python, you can read and write files using the built-in open() function, which provides various modes for file processing.

What are the different modes available for file processing? 

Answer: The open() function allows you to specify different modes for file processing. Here are some commonly used modes:

• ‘r’: Read mode (default) opens the file for reading.
• ‘w’: Write mode opens the file for writing (creates a new file or truncates an existing one).
• ‘a’: Append mode opens the file for writing (appends to an existing file without truncating).
• ‘b’: Binary mode for reading or writing binary data (e.g., images, audio files).
• ‘t’: Text mode (default) for reading or writing text data.
• ‘x’: Exclusive creation mode creates a new file but raises an error if it already exists.
• ‘+’: Update mode for both reading and writing.

Can you write a Python program to generate the Fibonacci sequence? 

Answer: Python program to generate the Fibonacci sequence up to a specified number of terms:

def generate_fibonacci(n):
    fibonacci_sequence = [0, 1]  # Initialize with the first two terms
    
    while len(fibonacci_sequence) < n:
        next_term = fibonacci_sequence[-1] + fibonacci_sequence[-2]
        fibonacci_sequence.append(next_term)
    
    return fibonacci_sequence

# Get the number of terms from the user
num_terms = int(input("Enter the number of Fibonacci terms to generate: "))

# Generate and print the Fibonacci sequence
fibonacci_sequence = generate_fibonacci(num_terms)
print("Fibonacci sequence:", fibonacci_sequence)

When you run this program, it will prompt you to enter the number of Fibonacci terms you want to generate, and then it will display the generated sequence.

For example, if you enter 10, the output might be:

Enter the number of Fibonacci terms to generate: 10

Fibonacci sequence: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

Can you write a program in Python that checks if a sequence is a palindrome? 

Answer: Python program that checks if a given sequence is a palindrome using recursion:

def is_palindrome_recursive(sequence):
    sequence = sequence.lower()
    sequence = ''.join(filter(str.isalnum, sequence))

    

    if len(sequence) <= 1:

        return True

    

    if sequence[0] != sequence[-1]:

        return False

    return is_palindrome_recursive(sequence[1:-1])

test_sequences = ["racecar", "hello", "A man, a plan, a canal, Panama!", "12321"]



for sequence in test_sequences:

    if is_palindrome_recursive(sequence):

        print(f"'{sequence}' is a palindrome.")

    else:

        print(f"'{sequence}' is not a palindrome."):

Can you write a Python program to add two positive integers without using the plus operator? 

Answer: In the below program, the add_without_plus_operator function takes two positive integers, a and b, as input and simulates the addition process using bitwise operations (&, ^, <<). The loop continues until there is no carry left (i.e., b becomes 0), and the final sum is stored in a.

You can replace the values of num1 and num2 with your own positive integers to test the program with different inputs.

def add_without_plus_operator(a, b):
    while b != 0:

        carry = a & b
        a = a ^ b
        b = carry << 1

    return a

# Test cases - Provide your numbers to test!

num1 = 25
num2 = 37

sum_result = add_without_plus_operator(num1, num2)

print(f"The sum of {num1} and {num2} is: {sum_result}")

Can you write a Python program to reverse a list? 

Answer: Yes, a Python program can be written to reverse a list using the “reversed()” function,

def reverse_list_builtin(lst):
    return list(reversed(lst))



# Test

original_list = [1, 2, 3, 4, 5]

reversed_list = reverse_list_builtin(original_list)

print("Original List:", original_list)

print("Reversed List (Using reversed()):", reversed_list)

You may also read how to reverse a list in Python to spin it backwards.

Which Python library is built on top of Matplotlib and Pandas to ease data plotting? 

Answer: Seaborn, built upon Matplotlib, is a widely used Python library for data visualization, offering sophisticated tools to generate plots for your data. With an array of features, Seaborn empowers you to craft impressive graphs using notably fewer lines of code compared to Matplotlib. Its enhanced capabilities enable the creation of compelling visuals with remarkable ease.

What is a pandas dataframe? 

Answer: A Pandas DataFrame is like a flexible table for data. It has rows and columns, and you can change its size. It’s a way to organize data neatly in a grid, just like arranging things in rows and columns on a piece of paper.

How to combine data frames in Pandas? 

Answer: In Pandas, you can combine data frames using various methods depending on your specific requirements. Here, I will be using Appending (df.append()): This method is used to append rows from one dataframe to another.

# Example dataframes
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

# Append df2 to df1

result = df1.append(df2)

What is a flask? 

Answer: Flask, a web application framework written in Python, emerged from the collaborative efforts of international Python enthusiasts under the leadership of Armin Ronacher, part of the Poocco group.

Leveraging the Werkzeug WSGI toolkit and the Jinja2 template engine, both Poocco projects, flask offers a Python module that streamlines web application development. Its defining characteristic is a concise and easily expandable core, distinguishing it as a microframework that intentionally omits features like an ORM (Object Relational Manager).

What is Django? 

Answer: Django is a Python-based web framework that enables the rapid development of secure and maintainable websites. It operates under the model-template-views architectural model and is available as an open-source resource. The Django Software Foundation, an independent establishment in the United States, is responsible for its upkeep and maintenance.

Mention some advantages of Django.

Answer: Django offers several advantages that make it a popular choice for web development:

• Better CDN connectivity 
• Efficient Content Management
• Swift Development
• ORM (Object-Relational Mapping)
• Versatile Template Engine
• Scalable
• Security Features

Overall, Django’s combination of swift development, scalability, security, and comprehensive toolset makes it a valuable framework for building a wide range of web applications.

Explain Django architecture.

Answer: Django follows the Model-View-Controller (MVC) architectural pattern, but in Django’s context, it’s often referred to as Model-View-Template (MVT). 

The MVT architecture consists of three main components: Models, Views, and Templates, along with additional components for handling URLs and forms.

• Models: Define data structure and database interactions.
• Views: Handle logic, process requests, and generate responses.
• Templates: Generate HTML for the user interface.

• URL Dispatcher: Maps URLs to views.
• Forms: Collect and validate user input.
• Middleware: Process requests and responses globally.
• Static and Media Files: Manage static and user-uploaded files.

This structure promotes clean separation of concerns, simplifying web app development.

What is a Django session? 

Answer: Sessions in Django (and across much of the web) serve as the means to maintain a record of the “state” between a website and a specific browser. Through sessions, you can retain assorted information for each browser, ensuring its accessibility to the website whenever the browser establishes a connection.

What is GIL? 

Answer: The Python Global Interpreter Lock (GIL) operates as a process lock utilized by Python when handling processes. In Python, a single thread is usually employed to execute a sequence of coded instructions. This implies that only one thread can execute at a time within Python. The reason for the comparable performance of both single-threaded and multithreaded processes in Python can be attributed to the existence of the GIL.

What is PIP?

Answer: PIP, short for “Pip Installs Packages,” is a package installer for Python. It’s used to easily manage and install software packages and libraries from the Python Package Index (PyPI) and other sources.

Conclusion 👨‍💻

Questions are not limited, as we see in this article. This article shows how different types of questions can be asked from various topics in Python. However, it’s not limited to the set of questions that we have discussed in this article.

One way to be prepared while learning is to question yourself on different topics. Try to make different types of questions from a concept. And answer them yourself. This way, you probably won’t surprise by the questions in the interview. You can also check out the online Python compiler to practice the code.

All the best for your upcoming Python Interview! 👍