Interview Questions

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Python Interview Questions

1. What are data types in Python?
A. Int, string, float and Boolean are few data types in Python.

2. What is Variable? Why it is called Variable?
A. A variable is a named memory location that holds a value. The contents of a variable can change or vary over time; this is why it’s called a variable.

3. How to add multi line comment in Python?
A. using Triple Quotes
‘’’ This is a Block of
Python Code for
Interview Training ‘’’

4. Difference Between append() and extend() in Python?
A. In Python, both append() and extend() are methods used with lists, but they serve different purposes. Here's a breakdown of their differences:
append()
Purpose: Adds a single element to the end of a list.
Argument:Takes one argument, which can be any object (including lists, numbers, strings, etc.).
Effect:The list grows by one element, regardless of the type of the argument.
Example:
my_list = [1, 2, 3]
my_list.append([4, 5])
print(my_list) # Output: [1, 2, 3, [4, 5]]

In this example, the list [4, 5] is added as a single element at the end of my_list.

extend()
Purpose: Extends the list by appending elements from an iterable (e.g., list, tuple, string) to the end of the list.
Argument:Takes one argument, which must be an iterable.
Effect:The list grows by the number of elements in the iterable.
Example:
my_list = [1, 2, 3]
my_list.extend([4, 5])
print(my_list) # Output: [1, 2, 3, 4, 5]

In this example, the elements 4 and 5 are added individually to my_list

5. What are local variables and global variables in Python explain with examples?
A. In Python, variables can be categorized based on their scope, which determines where the variable can be accessed or modified. The two main types are local variables and global variables.
Local Variables
Definition:Local variables are variables that are defined within a function and can only be accessed within that function.
Scope:They exist only within the block of code where they are defined, typically a function. Once the function execution is finished, the local variable is destroyed.
Usage:Local variables are useful for temporary storage of data that is only relevant within a specific function.
Example:
def my_function():
local_var = 100 # local_var is a local variable
print(local_var) # This will print 100
my_function()
# print(local_var) # This will cause an error because local_var is not accessible outside the function

Global Variables
Definition: Global variables are variables defined outside any function or class and can be accessed from anywhere in the code, including inside functions and classes.
Scope:They exist throughout the program's runtime and can be accessed and modified from any part of the program, unless overridden by a local variable of the same name within a function.
Usage: Global variables are used when data needs to be shared across multiple functions or when a value needs to persist throughout the program's execution.
Example:
global_var = 20 # global_var is a global variable
def my_function ():
print(global_var) # This will print 20
my_function()
print (global_var) # This will also print 20

6. Is Python a case sensitive language?
A. Yes, Python is a case-sensitive language. This means that it distinguishes between uppercase and lowercase letters in identifiers such as variable names, function names, class names, and keywords. For example, the variables variable, Variable, and VARIABLE would be considered three distinct identifiers in Python.
Example:
name = "Alice"
Name = "Bob"
NAME = "Charlie"

print(name) # Output: Alice
print(Name) # Output: Bob
print(NAME) # Output: Charlie

In this example, name, Name, and NAME are three different variables, each storing a different string. Using the wrong case will result in an error if the identifier has not been defined.

Case Sensitivity with Keywords

Python's keywords (reserved words) are also case-sensitive. For example, print, Print, and PRINT are not the same:
print("Hello") # This is valid and will print "Hello"
Print("Hello") # This will raise a NameError, as "Print" is not defined
PRINT("Hello") # This will also raise a NameError, as "PRINT" is not defined
Only print (all lowercase) is the correct keyword for outputting text in Python. Using any other case will result in an error because Python does not recognize them as the built-in function print

7. Difference between Lists and Tuples with examples?
A. Lists and tuples are both sequence data types in Python that can store collections of items. However, they have distinct characteristics and usage patterns.
Lists
Definition: Global variables are variables defined outside any function or class and can be accessed from anywhere in the code, including inside functions and classes.
Syntax: Lists are defined using square brackets [].
Mutability: Lists are mutable, meaning you can change their content by adding, removing, or modifying elements.
Usage: Lists are used when you need a collection of items that may need to be modified.
Example:
my_list = [1, 2, 3, "four", 5.0]
my_list[1] = 10 # Modifying the second element
print(my_list) # Output: [1, 10, 3, 'four', 5.0]
my_list.append("six") # Adding an element to the end
print(my_list) # Output: [1, 10, 3, 'four', 5.0, 'six']

Tuples
Definition: A tuple is an ordered, immutable (unchangeable) collection of elements. Like lists, tuples can contain elements of different data types.
Syntax: Tuples are defined using parentheses () or without any delimiters (in certain contexts).
Mutability: Tuples are immutable, meaning once they are created, their elements cannot be changed, added, or removed.
Usage: Tuples are used when you need a collection of items that should not change throughout the program. They can also be used as dictionary keys because of their immutability.
Example:
my_tuple = (1, 2, 3, "four", 5.0)
print(my_tuple) # Output: (1, 2, 3, 'four', 5.0)
my_tuple[1] = 10 # This would raise a TypeError because tuples are immutable

Key Differences
Mutability:
Lists are mutable; their elements can be changed, and you can add or remove elements.
Tuples are immutable; once created, their elements cannot be changed, nor can elements be added or removed.
Syntax:
Lists use square brackets [].
Tuples use parentheses () or no delimiters.
Performance:
Tuples can be slightly more efficient in terms of performance and memory usage because of their immutability.
They are faster to iterate over and have a smaller memory footprint compared to lists.
Use Cases:
Lists are typically used when the data needs to be modified during the program execution.
Tuples are used when the data should remain constant, ensuring data integrity, and when using a sequence as a dictionary key.
Functions and Methods:
Lists have various methods for modifying the list (e.g., append(), remove(), sort(), etc.).
Tuples have a limited set of methods since they cannot be modified (e.g., count(), index()).

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8. What is slicing in Python, explain with example?
A. Slicing in Python is a technique used to extract a portion of a sequence (such as a list, tuple, string, or other sequence types) by specifying a range of indices. Slicing allows you to create a new sequence containing elements from the original sequence, from a specified start index to an end index (but not including the end index). The syntax for slicing is:
sequence[start:stop:step]

start: The starting index of the slice (inclusive). If omitted, the slice starts from the beginning of the sequence.
stop: The ending index of the slice (exclusive). If omitted, the slice extends to the end of the sequence.
step: The step size or stride between elements. If omitted, the default is 1.

Example with a List
# Original list
my_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
# Slicing to get elements from index 2 to 5 (excluding 5)
sub_list = my_list[2:5]
print(sub_list) # Output: [30, 40, 50]
# Slicing with a step
sub_list_step = my_list[1:8:2]
print(sub_list_step) # Output: [20, 40, 60, 80]
# Slicing without specifying start and stop (copying the entire list)
full_copy = my_list[:]
print(full_copy) # Output: [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
# Slicing with negative indices
reverse_slice = my_list[-5:-2]
print(reverse_slice) # Output: [60, 70, 80]
# Slicing with a negative step (reversing the list)
reversed_list = my_list[::-1]
print(reversed_list) # Output: [100, 90, 80, 70, 60, 50, 40, 30, 20, 10]

Example with a String
# Original string
my_string = "Hello, World!"
# Slicing to get characters from index 7 to 12 (excluding 12)
sub_string = my_string[7:12]
print(sub_string) # Output: 'World'
# Slicing with a step
sub_string_step = my_string[0:5:2]
print(sub_string_step) # Output: 'Hlo'
# Slicing to reverse the string
reversed_string = my_string[::-1]
print(reversed_string) # Output: '!dlroW ,olleH'

Explanation
In the list example, my_list[2:5] extracts the elements from index 2 up to (but not including) index 5.
my_list[1:8:2] extracts elements starting from index 1 up to index 8 with a step of 2, meaning it skips every other element.
my_list[-5:-2] uses negative indices to extract elements from the fifth-last to the third-last element.
my_list[::-1] reverses the list by specifying a negative step.

Slicing is a powerful feature in Python that can be used for a variety of purposes, such as extracting sub-sequences, reversing sequences, and more

9. What is the difference between a Mutable datatype and an Immutable data type?
A. In Python, data types can be categorized as mutable or immutable based on whether their state or contents can be changed after they are created. This distinction is crucial for understanding how Python handles objects and memory.

Mutable Data Types
Mutable data types are types whose values can be changed after they are created. This means that the internal state of a mutable object can be modified without changing its identity.
Common Mutable Data Types in Python:
Lists (list)
Dictionaries (dict)
Sets (set)

Example:
# List (mutable)
my_list = [1, 2, 3]
my_list[0] = 10 # Modifying the first element
print(my_list) # Output: [10, 2, 3]

# Dictionary (mutable)
my_dict = {"a": 1, "b": 2}
my_dict["a"] = 10 # Modifying the value for key 'a'
print(my_dict) # Output: {'a': 10, 'b': 2}

Immutable Data Types
Immutable data types are types whose values cannot be changed once they are created. Any operation that attempts to modify the value of an immutable object will result in the creation of a new object, rather than changing the original object.
Common Immutable Data Types in Python: Strings (str)
Tuples (tuple)
Numbers (int, float, complex)

Examples:
# String (immutable)
my_string = "hello"
# my_string[0] = 'H' # This would raise a TypeError

# Instead, create a new string
new_string = "H" + my_string[1:]
print(new_string) # Output: 'Hello'

# Tuple (immutable)
my_tuple = (1, 2, 3)
# my_tuple[0] = 10 # This would raise a TypeError

# Instead, create a new tuple
new_tuple = (10,) + my_tuple[1:]
print(new_tuple) # Output: (10, 2, 3)

10. Difference between FOR Loop and WHILE Loop in python ?
A. The “for” Loop is generally used to iterate through the elements of various collection types such as List, Tuple, Set, and Dictionary. Developers use a “for” loop where they have both the conditions start and the end. Whereas, the “while” loop is the actual looping feature that is used in any other programming language. Programmers use a Python while loop where they just have the end conditions.

11. What are modules and packages in Python ?
A. Modules:
Definition: A module is a single file containing Python code. It can include functions, classes, variables, and runnable code. Modules allow you to logically organize your Python code and make it reusable across different programs.
Packages:
Definition: A package is a way of organizing related modules into a directory hierarchy. It contains a special file named __init__.py (which can be empty) that indicates to Python that the directory should be treated as a package. Packages can contain sub-packages, providing a nested structure for organizing code.

Key Differences
Structure:
Modules are individual files containing Python code.
Packages are directories containing multiple modules and possibly sub-packages
Purpose:
Modules help organize code within a single file.
Packages help organize code across multiple modules, providing a hierarchical structure.
Usage:
Modules are imported using the import statement followed by the module name.
Packages are imported by specifying the package and module name, allowing for more organized imports.

Example:
# Importing a module
import math_operations
# Importing from a package
from math_package import basic_operations.
from math_package.advanced_operations import multiply.

Modules and packages are fundamental to creating modular and maintainable Python applications. They promote code reuse and help manage complex projects by organizing related code into separate namespaces.

12. Explain Break, Continue and Pass Keywords in Python ?
A. Break - The break statement terminates the loop immediately and the control flows to the statement after the body of the loop.

Continue - The continue statement terminates the current iteration of the statement, skips the rest of the code in the current iteration and the control flows to the next iteration of the loop.

Pass - As explained above, the pass keyword in Python is generally used to fill up empty blocks and is similar to an empty statement represented by a semi-colon in languages such as Java, C++, Javascript, etc..

13. What is __init__ in Python and where is it is used ?
A. In Python, __init__ is a special method, also known as a constructor, that is automatically called when a new instance of a class is created. It allows the class to initialize the object's attributes and perform any setup or initialization that is required. The __init__ method can take any number of arguments, but the first argument must always be self, which refers to the instance being created.

Here’s a simple example:

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)
# Accessing the attributes and methods of the instance
print(person1.name) # Output: Alice
print(person1.age) # Output: 30
person1.greet() # Output: Hello, my name is Alice and I am 30 years old.

14. What is exceptional handling in Python?
A. There are 3 main keywords i.e. try, except, and finally which are used to catch exceptions and handle the recovering mechanism accordingly.
Try is the block of a code that is monitored for errors.
Except block gets executed when an error occurs.
Finally The beauty of the final block is to execute the code after trying for an error. This block gets executed irrespective of whether an error occurred or not.

15. What is List Comprehension? Give an Example in Python?
A. The method enables programmers to work with lists (an array-like Python data type) in a readable and single-line format. List comprehensions are a must-know syntax construct, whether you're just learning Python or a well-versed developer.

Example: Squaring Numbers
Let's create a list of squares for the numbers 1 through 5.
# Using list comprehension to create a list of squares
squares = [x**2 for x in range(1, 6)]
# Output: [1, 4, 9, 16, 25]
print(squares)
In this example, x**2 is the expression that squares each number in the range 1 to 5, creating the list [1, 4, 9, 16, 25].

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