Python basic interview questions

Released on February 20, 1991, Python was developed by Guido Van Rossum. Python is amongst the most valued and widely-used programming languages. This programming language is popular thanks to its flexibility in sustaining dynamic semantics.

It is an open-source and free language with straightforward and clean syntax. This attribute of Python allows developers to work with the language quickly. Python also supports object-oriented programming. It is also popular for general-purpose programming. Developers will have to get a rudimentary knowledge of the language to excel when asked Python interview questions.

Python can help build wide varieties of data visualizations such as the bar graphs and lines, pie charts, 3D plots, and histograms. Python consists of several libraries to allow developers to write programs for data analysis and machine learning. Beginners can use Python for automating simpler tasks on the computer like renaming files, finding, and downloading online content.

The simplistic qualities and the ability to achieve several functionalities through less coding make Python the most sought-after profession. Python has tremendously grown and is popular among developers. Python incorporates AI, machine learning, web scraping, web development, and other domains, due to its proficiency in supporting rigid computations of powerful libraries. Due to this, the need for Python developers is roaring worldwide.

In this post, we will talk about the Python basic interview questions and answers which can help you excel and secure an amazing job.

Facts

Facts About Python

• Python is more equivalent to the English language as we speak. In addition to simplicity. 
• Python is a compelling and high-level language.
• Python runs on the interpreter system. In other words, you can also say that you can execute the code as soon as you write it.
• Python offers quick prototyping.
• Python is a computer programming language used for building sites, conducting data analysis, and software. 
• Python incorporates high-level dynamic data types, exceptions, dynamic typing, and classes. 
• There are four primary Python coding styles such as functional, imperative, object-oriented, and procedural.

1 . What is Python? What are the benefits of using Python.

Python is a high-level, interpreted, general-purpose programming language. Being a general-purpose language, you can also write almost any type of application with the right tools/libraries. Additionally, python supports objects, modules, threads, exception-handling, and automatic memory management which help in modelling real-world problems and building applications to solve these problems.

Benefits of using Python:

• Python is a general-purpose programming language that has a simple, easy-to-learn syntax that emphasizes readability and therefore reduces the cost of program maintenance. Moreover, the language is capable of scripting, is completely open-source, and supports third-party packages encouraging modularity and code reuse.
• Its high-level data structures, combined with dynamic typing and dynamic binding, attract a huge community of developers for Rapid Application Development and deployment.

2 . What is a dynamically typed language?

Before we understand a dynamically typed language, we should learn about what typing is. Typing refers to type-checking in programming languages. In a strongly-typed language, such as Python, “1” + 2 will result in a type error since these languages don’t allow for “type-coercion” (implicit conversion of data types). On the other hand, a weakly-typed language, such as Javascript, will simply output “12” as result.

There are two ways of type-checking –

• Static – You can also check data types before execution.
• Dynamic – Moreover, yopu can check data types during execution.

Python is an interpreted language. It executes each statement line by line. Therefore, type-checking is done on the fly, during execution. Hence, you can say that Python is a Dynamically-Typed Language.

3 . What is an Interpreted language?

An Interpreted language executes its statements line by line. Languages such as Python, Javascript, R, PHP, and Ruby are prime examples of Interpreted languages. Programs written in an interpreted language runs directly from the source code, with no intermediary compilation step.

4 . What is PEP 8 and why is it important?

PEP stands for Python Enhancement Proposal. A PEP is an official design document providing information to the Python community, or describing a new feature for Python or its processes. PEP 8 is especially important since it documents the style guidelines for Python Code. Apparently contributing to the Python open-source community requires you to follow these style guidelines sincerely and strictly.

5 . What is Scope in Python?

Every object in Python functions within a scope. A scope is a block of code where an object in Python remains relevant. Namespaces uniquely identify all the objects inside a program. However, these namespaces also have a scope defined for them where you could use their objects without any prefix. A few examples of scope created during code execution in Python are as follows:

• A local scope refers to the local objects available in the current function.
• A global scope refers to the objects available throughout the code execution since their inception.
• A module-level scope refers to the global objects of the current module accessible in the program.
• An outermost scope refers to all the built-in names callable in the program.

Note:  Generally, you can use Local scope objects with global scope objects using keywords such as global.

6 . What are lists and tuples? What is the key difference between the two?

Lists and Tuples are both sequence data types that can store a collection of objects in Python. The objects stored in both sequences can have different data types. Lists are represented with square brackets['sara', 6, 0.19], while tuples are represented with parantheses('ansh', 5, 0.97).
But what is the real difference between the two? The key difference between the two is that while lists are mutable, tuples on the other hand are immutable objects. This means that you can also modify, append and also append lists on the go. However, tuples remain constant and cannot modified in any manner. You can run the following example on Python IDLE to confirm the difference:

my_tuple = ('sara', 6, 5, 0.97)
my_list = ['sara', 6, 5, 0.97]
print(my_tuple[0])     # output => 'sara'
print(my_list[0])     # output => 'sara'
my_tuple[0] = 'ansh'    # modifying tuple => throws an error
my_list[0] = 'ansh'    # modifying list => list modified
print(my_tuple[0])     # output => 'sara'
print(my_list[0])     # output => 'ansh'

7 . What are the common built-in data types in Python?

There are several built-in data types in Python. Although, You don’t need to define data types explicitly in python. However, during variable declarations, type errors are likely to occur. This is true if you negelect the knowledge of data types and their compatibility with each other. Python provides type() and isinstance() functions to check the type of these variables. You can group these data types into the following categories-

• None Type:
  None keyword represents the null values in Python. You can perform Boolean equality operation using these NoneType objects.

• Numeric Types:
  There are three distinct numeric types – integers, floating-point numbers, and complex numbers. Additionally, booleans are a sub-type of integers.

Note: The standard library also includes fractions to store rational numbers and decimal to store floating-point numbers with user-defined precision.

• Sequence Types:
  According to Python Docs, there are three basic Sequence Types – lists, tuples, and range objects. Sequence types have the in and not in operators defined for their traversing their elements. These operators share the same priority as the comparison operations.

Note

Note: The standard library also includes additional types for processing:
1. Binary data such as bytearray bytesmemoryview , and
2. Text strings such as str.

• Mapping Types:

A mapping object can map hashable values to random objects in Python. Mappings objects are mutable and there is currently only one standard mapping type, the dictionary.

• Set Types:
  Currently, Python has two built-in set types – set and frozenset. set type is mutable and supports methods like add() and remove(). frozenset type is immutable and can’t be modified after creation.

Note: set is mutable and thus cannot be used as key for a dictionary. On the other hand, frozenset is immutable and thus, hashable, and can be used as a dictionary key or as an element of another set.

• Modules:
  Module is an additional built-in type that Python Interpreter supports. It supports one special operation, i.e., attribute access: mymod.myobj, where mymod is a module and myobj references a name defined in m’s symbol table. The module’s symbol table resides in a very special attribute of the module __dict__.
• Callable Types:
  Callable types are the types to which you can apply the function. They can be user functions, instance methods, and also generator functions. And also some other built-in functions, methods and classes.
  Refer to the documentation at docs.python.org for a detailed view of the callable types.

8 . What is pass in Python?

The pass keyword represents a null operation in Python. It is generally popular for the purpose of filling up empty blocks of code which you may execute during runtime. Without the pass statement in the following code, we may run into some errors during code execution.

def myEmptyFunc():
   # do nothing
   pass
myEmptyFunc()    # nothing happens
## Without the pass keyword
# File "<stdin>", line 3
# IndentationError: expected an indented block

9 . What are modules and packages in Python?

Python packages and Python modules are two mechanisms that allow for modular programming in Python. Modularizing has several advantages –

• Simplicity: Working on a single module helps you focus on a relatively small portion of the problem at hand. This makes development easier and less error-prone.
• Maintainability: Modules are popular to enforce logical boundaries between different problem domains. If you write it in a manner that reduces interdependency, it is less likely that modifications in a module might impact other parts of the program.
• Reusability: Functions that you define in a module is easy to access by other parts of the application.
• Scoping: Modules typically define a separate namespace, which helps avoid confusion between identifiers from other parts of the program.

Modules, in general, are simply Python files with a .py extension and can have a set of functions, classes, or variables defined and implemented. They can be imported and initialized once using the import statement. If partial functionality is needed, import the requisite classes or functions using from foo import bar.

Packages allow for hierarchial structuring of the module namespace using dot notation. As, modules help avoid clashes between global variable names, in a similar manner, packages help avoid clashes between module names.
Creating a package is easy since it makes use of the system’s inherent file structure. So just stuff the modules into a folder and there you have it, the folder name as the package name. Importing a module or its contents from this package requires the package name as prefix to the module name joined by a dot.

Note: You can technically import the package as well, but alas, it doesn’t import the modules within the package to the local namespace, thus, it is practically useless.

10 . What are global, protected and private attributes in Python?

• Global variables are public variables that you define in the global scope. To use the variable in the global scope inside a function, we use the global keyword.
• Protected attributes are attributes that you define with an underscore prefixed to their identifier eg. _sara. You can access and modify it from outside the class also.
• Private attributes are attributes with double underscore in the prefix to their identifier eg. __ansh. You can’t access or modify it from the outside directly. Moreover, it will result in an AttributeError if you make such a attempt.

11 . What is the use of self in Python?

Self represents the instance of the class. With this keyword, you can access the attributes and methods of the class in python. It binds the attributes with the given arguments. You can use self in different places and often may think it to be a keyword. But unlike in C++, self is not a keyword in Python.

12 . What is __init__?

__init__ is a contructor method in Python. It is automatically called to allocate memory when a new object/instance is created. All classes have a __init__ method associated with them. It helps in distinguishing methods and attributes of a class from local variables.

# class definition
class Student:
   def __init__(self, fname, lname, age, section):
       self.firstname = fname
       self.lastname = lname
       self.age = age
       self.section = section
# creating a new object
stu1 = Student("Sara", "Ansh", 22, "A2")

13 . What is break, continue and pass in Python?

pat = [1, 3, 2, 1, 2, 3, 1, 0, 1, 3]
for p in pat:
   pass
   if (p == 0):
       current = p
       break
   elif (p % 2 == 0):
       continue
   print(p)    # output => 1 3 1 3 1
print(current)    # output => 0

14 . What are unit tests in Python?

• Unit test is a unit testing framework of Python.
• Unit testing means testing different components of software separately. Can you think about why unit testing is important? Imagine a scenario, you are building software that uses three components namely A, B, and C. Now, suppose your software breaks at a point time. How will you find which component was responsible for breaking the software? Maybe it was component A that failed, which in turn failed component B, and this actually failed the software. There can be many such combinations.
• This is why it is necessary to test each and every component properly so that we know which component might be highly responsible for the failure of the software.

15 . Find What docstring in Python Is

• Documentation string or docstring is a multiline string used to document a specific code segment.
• The docstring should also describe what the function or method does.

16 . What is slicing in Python?

• As the name suggests, ‘slicing’ is taking parts of.
• Syntax for slicing is [start : stop : step]
• start is the starting index from where to slice a list or tuple
• stop is the ending index or where to sop.
• step is the number of steps to jump.
• Default value for start is 0, stop is number of items, step is 1.
• You can do slicing on strings, arrays, lists, and tuples.

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(numbers[1 : : 2])  #output : [2, 4, 6, 8, 10]

17 . Explain how can you make a Python Script executable on Unix?

Script file must begin with #!/usr/bin/env python

18 . What is the difference between Python Arrays and lists?

• Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
• Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.

import array
a = array.array('i', [1, 2, 3])
for i in a:
    print(i, end=' ')    #OUTPUT: 1 2 3
a = array.array('i', [1, 2, 'string'])    #OUTPUT: TypeError: an integer is required (got type str)
a = [1, 2, 'string']
for i in a:
   print(i, end=' ')    #OUTPUT: 1 2 string

19 . Python Interview Questions for Experienced

Memory management in Python

• Memory management in Python is done by the Python Memory Manager. The memory allocated by the manager is in form of a private heap space dedicated to Python. You store all Python objects in this heap. Alos, being private, it is inaccessible to the programmer. Though, python does provide some core API functions to work upon the private heap space.
• Additionally, Python has an in-built garbage collection to recycle the unused memory for the private heap space.

20 . What are Python namespaces and their use?

A namespace in Python ensures that object names in a program are unique. You can use it without any conflict. Python implements these namespaces as dictionaries with ‘name as key’ mapped to a corresponding ‘object as value’. This allows for multiple namespaces to use the same name and map it to a separate object. A few examples of namespaces are as follows:

• Local Namespace includes local names inside a function. the namespace is temporarily created for a function call and gets cleared when the function returns.
• Global Namespace includes names from various imported packages/ modules that you can use in the current project. You can create this namespace when you import the package in the script and lasts until the execution of the script.
• Built-in Namespace includes built-in functions of core Python and built-in names for various types of exceptions.

The lifecycle of a namespace depends upon the scope of objects they can map to. If the scope of an object ends, the lifecycle of that namespace comes to an end. Hence, it isn’t possible to access inner namespace objects from an outer namespace.

21 . What is Scope Resolution in Python?

Sometimes objects within the same scope have the same name but function differently. In such cases, scope resolution comes into play in Python automatically. A few examples of such behavior are:

• Python modules namely ‘math’ and ‘cmath’ have a lot of functions that are common to both of them – log10(), acos(), exp() etc. To resolve this ambiguity, it is necessary to prefix them with their respective module, like math.exp() and cmath.exp().
• Consider the code below, initialize an object temp to 10 globally and then to 20 on function call. However, the function call didn’t change the value of the temp globally. Here, we can observe that Python draws a clear line between global and local variables, treating their namespaces as separate identities.

temp = 10   # global-scope variable
def func():
     temp = 20   # local-scope variable
     print(temp)
print(temp)   # output => 10
func()    # output => 20
print(temp)   # output => 10

This behavior can be overridden using the global keyword inside the function, as shown in the following example:

temp = 10   # global-scope variable
def func():
     global temp
     temp = 20   # local-scope variable
     print(temp)
print(temp)   # output => 10
func()    # output => 20
print(temp)   # output => 20

22 . What are decorators in Python?

Decorators in Python are essentially functions that add functionality to an existing function in Python without changing the structure of the function itself. They are represented the @decorator_name in Python and are called in a bottom-up fashion. For example:

Example

# decorator function to convert to lowercase
def lowercase_decorator(function):
   def wrapper():
       func = function()
       string_lowercase = func.lower()
       return string_lowercase
   return wrapper
# decorator function to split words
def splitter_decorator(function):
   def wrapper():
       func = function()
       string_split = func.split()
       return string_split
   return wrapper
@splitter_decorator # this is executed next
@lowercase_decorator # this is executed first
def hello():
   return 'Hello World'
hello()   # output => [ 'hello' , 'world' ]

The beauty of the decorators lies in the fact that besides adding functionality to the output of the method, they can even accept arguments for functions and can further modify those arguments before passing it to the function itself. The inner nested function, i.e. ‘wrapper’ function, plays a significant role here. You can implement it to enforce encapsulation. However, hide it from the global scope.

# decorator function to capitalize names
def names_decorator(function):
   def wrapper(arg1, arg2):
       arg1 = arg1.capitalize()
       arg2 = arg2.capitalize()
       string_hello = function(arg1, arg2)
       return string_hello
   return wrapper
@names_decorator
def say_hello(name1, name2):
   return 'Hello ' + name1 + '! Hello ' + name2 + '!'
say_hello('sara', 'ansh')   # output => 'Hello Sara! Hello Ansh!'

23 . What are Dict and List comprehensions?

Python comprehensions, like decorators, are syntactic sugar constructs that help build altered and filtered lists, dictionaries, or sets from a given list, dictionary, or set. Using comprehensions saves a lot of time and code that might be considerably more verbose (containing more lines of code). Let’s check out some examples, where comprehensions can be truly beneficial:

Performing mathematical operations on the entire list

my_list = [2, 3, 5, 7, 11]
squared_list = [x**2 for x in my_list]    # list comprehension
# output => [4 , 9 , 25 , 49 , 121]
squared_dict = {x:x**2 for x in my_list}    # dict comprehension
# output => {11: 121, 2: 4 , 3: 9 , 5: 25 , 7: 49}

Performing conditional filtering operations on the entire list

my_list = [2, 3, 5, 7, 11]
squared_list = [x**2 for x in my_list if x%2 != 0]    # list comprehension
# output => [9 , 25 , 49 , 121]
squared_dict = {x:x**2 for x in my_list if x%2 != 0}    # dict comprehension
# output => {11: 121, 3: 9 , 5: 25 , 7: 49}

• Combining multiple lists into one

Comprehensions allow for multiple iterators and hence, can be used to combine multiple lists into one. 

a = [1, 2, 3]
b = [7, 8, 9]
[(x + y) for (x,y) in zip(a,b)]  # parallel iterators
# output => [8, 10, 12]
[(x,y) for x in a for y in b]    # nested iterators
# output => [(1, 7), (1, 8), (1, 9), (2, 7), (2, 8), (2, 9), (3, 7), (3, 8), (3, 9)]

• Flattening a multi-dimensional list

You can apply a similar approach of nested iterators (as above) to flatten a multi-dimensional list and also work upon its inner elements. 

my_list = [[10,20,30],[40,50,60],[70,80,90]]
flattened = [x for temp in my_list for x in temp]
# output => [10, 20, 30, 40, 50, 60, 70, 80, 90]

24 . What is lambda in Python and it’s use?

Lambda is an anonymous function in Python, that can accept any number of arguments, but can only have a single expression. It is generally popular in situations requiring an anonymous function for a short time period. You can use Lambda functions in either of the two ways:

• Assigning lambda functions to a variable:

mul = lambda a, b : a * b
print(mul(2, 5))    # output => 10

Wrapping lambda functions inside another function:

def myWrapper(n):
 return lambda a : a * n
mulFive = myWrapper(5)
print(mulFive(2))    # output => 10

25 . How do you copy an object in Python?

In Python, the assignment statement (= operator) does not copy objects. Instead, it creates a binding between the existing object and the target variable name. To create copies of an object in Python, we need to use the copy module. Moreover, there are two ways of creating copies for the given object using the copy module –

Shallow Copy is a bit-wise copy of an object. The copied object created has an exact copy of the values in the original object. If either of the values is a reference to other objects, just the reference addresses for the same are copied.
Deep Copy copies all values recursively from source to target object, i.e. it even duplicates the objects referenced by the source object.

from copy import copy, deepcopy

list_1 = [1, 2, [3, 5], 4]
## shallow copy
list_2 = copy(list_1) 
list_2[3] = 7
list_2[2].append(6)
list_2    # output => [1, 2, [3, 5, 6], 7]
list_1    # output => [1, 2, [3, 5, 6], 4]
## deep copy
list_3 = deepcopy(list_1)
list_3[3] = 8
list_3[2].append(7)
list_3    # output => [1, 2, [3, 5, 6, 7], 8]
list_1    # output => [1, 2, [3, 5, 6], 4]

26 . What is the difference between xrange and range in Python?

xrange() and range() are quite similar in terms of functionality. They both generate a sequence of integers, with the only difference that range() returns a Python list, whereas, xrange() returns an xrange object.

So how does that make a difference? It sure does, because unlike range(), xrange() doesn’t generate a static list, it creates the value on the go. This technique is commonly used with an object-type generator and has been termed as “yielding“.

Yielding is crucial in applications where memory is a constraint. Creating a static list as in range() can lead to a Memory Error in such conditions, while, xrange() can handle it optimally by using just enough memory for the generator (significantly less in comparison).

for i in xrange(10):    # numbers from o to 9
   print i       # output => 0 1 2 3 4 5 6 7 8 9
for i in xrange(1,10):    # numbers from 1 to 9
   print i       # output => 1 2 3 4 5 6 7 8 9
for i in xrange(1, 10, 2):    # skip by two for next
   print i       # output => 1 3 5 7 9

Note: xrange has been deprecated as of Python 3.x. Now range does exactly the same as what xrange used to do in Python 2.x, since it was way better to use xrange() than the original range() function in Python 2.x.

27 . What is pickling and unpickling?

Python library offers a feature – serialization out of the box. Serializing an object refers to transforming it into a format that can be stored, so as to be able to deserialize it, later on, to obtain the original object. Here, the pickle module comes into play.

Pickling:

• Pickling is the name of the serialization process in Python. Any object in Python can be serialized into a byte stream and dumped as a file in the memory. The process of pickling is compact but pickle objects can be compressed further. Moreover, pickle keeps track of the objects it has serialized and the serialization is portable across versions.
• The function used for the above process is pickle.dump().

Unpickling:

• Unpickling is the complete inverse of pickling. It deserializes the byte stream to recreate the objects stored in the file and loads the object to memory.
• The function used for the above process is pickle.load().

Note: Python has another, more primitive, serialization module called marshall, which exists primarily to support .pyc files in Python and differs significantly from the pickle.

28 . What are generators in Python?

Generators are functions that return an iterable collection of items, one at a time, in a set manner. Generators, in general, are used to create iterators with a different approach. They employ the use of yield keyword rather than return to return a generator object.
Let’s try and build a generator for fibonacci numbers –

operators with a different approach. They employ the use of yield keyword rather than return to return a generator object.

Let's try and build a generator for fibonacci numbers -

## generate fibonacci numbers upto n
def fib(n):
   p, q = 0, 1
   while(p < n):
       yield p
       p, q = q, p + q
x = fib(10)    # create generator object 
 
## iterating using __next__(), for Python2, use next()
x.__next__()    # output => 0
x.__next__()    # output => 1
x.__next__()    # output => 1
x.__next__()    # output => 2
x.__next__()    # output => 3
x.__next__()    # output => 5
x.__next__()    # output => 8
x.__next__()    # error
 
## iterating using loop
for i in fib(10):
   print(i)    # output => 0 1 1 2 3 5 8

29 . What is PYTHONPATH in Python?

PYTHONPATH is an environment variable which you can set to add additional directories where Python will look for modules and packages. This is especially useful in maintaining Python libraries that you do not wish to install in the global default location.

30 . What is the use of help() and dir() functions?

help() function in Python is used to display the documentation of modules, classes, functions, keywords, etc. If no parameter is passed to the help() function, then an interactive help utility is launched on the console.
dir() function tries to return a valid list of attributes and methods of the object it is called upon. It behaves differently with different objects, as it aims to produce the most relevant data, rather than the complete information.

• For Modules/Library objects, it returns a list of all attributes, contained in that module.
• For Class Objects, it returns a list of all valid attributes and base attributes.
• With no arguments passed, it also returns a list of attributes in the current scope.

31 . What is the difference between .py and .pyc files?

• py files contain the source code of a program. Whereas, .pyc file contains the bytecode of your program. We get bytecode after compilation of .py file (source code). .pyc files are not created for all the files that you run. It is only created for the files that you import.
• Before executing a python program python interpreter checks for the compiled files. If the file is present, the virtual machine executes it. If not found, it checks for .py file. If found, compiles it to .pyc file and then python virtual machine executes it.
• Having .pyc file also saves you the compilation time.

32 . How Python is interpreted?

• Python as a language is not interpreted or compiled. Interpreted or compiled is the property of the implementation. Python is a bytecode(set of interpreter readable instructions) interpreted generally.
• Source code is a file with .py extension.
• Python compiles the source code to a set of instructions for a virtual machine. The Python interpreter is an implementation of that virtual machine. This intermediate format is also popular “bytecode”.
• .py source code is first compiled to give .pyc which is bytecode. This bytecode can be then interpreted by the official CPython or JIT(Just in Time compiler) compiled by PyPy.

33 . How are arguments passed by value or by reference in python?

• Pass by value: Copy of the actual object is passed. Changing the value of the copy of the object will not change the value of the original object.
• Pass by reference: Reference to the actual object is passed. Changing the value of the new object will also change the value of the original object.

In Python, arguments are passed by reference, i.e., reference to the actual object is passed.

def appendNumber(arr):
   arr.append(4)
arr = [1, 2, 3]
print(arr)  #Output: => [1, 2, 3]
appendNumber(arr)
print(arr)  #Output: => [1, 2, 3, 4]

34 . What are iterators in Python?

• An iterator is an object.
• It remembers its state i.e., where it is during iteration (see code below to see how)
• __iter__() method initializes an iterator.
• It has a __next__() method which returns the next item in iteration and points to the next element. Upon reaching the end of iterable object __next__() must return StopIteration exception.
• It is also self-iterable.
• Iterators are objects with which we can iterate over iterable objects like lists, strings, etc.

class ArrayList:
   def __init__(self, number_list):
       self.numbers = number_list
   def __iter__(self):
       self.pos = 0
       return self
   def __next__(self):
       if(self.pos < len(self.numbers)):
           self.pos += 1
           return self.numbers[self.pos - 1]
       else:
           raise StopIteration
array_obj = ArrayList([1, 2, 3])
it = iter(array_obj)
print(next(it)) #output: 2
print(next(it)) #output: 3
print(next(it))
#Throws Exception
#Traceback (most recent call last):
#...
#StopIteration

35 . Explain how to delete a file in Python?

Use command os.remove(file_name)

import os
os.remove("ChangedFile.csv")
print("File Removed!")

36 . Explain split() and join() functions in Python?

• You can use split() function to split a string based on a delimiter to a list of strings.
• You can use join() function to join a list of strings based on a delimiter to give a single string.

string = "This is a string."
string_list = string.split(' ') #delimiter is ‘space’ character or ‘ ‘
print(string_list) #output: ['This', 'is', 'a', 'string.']
print(' '.join(string_list)) #output: This is a string.

37 . What does *args and **kwargs mean?

*args

• *args is a special syntax used in the function definition to pass variable-length arguments.
• “*” means variable length and “args” is the name used by convention. You can use any other.

def multiply(a, b, *argv):
   mul = a * b
   for num in argv:
       mul *= num
   return mul
print(multiply(1, 2, 3, 4, 5)) #output: 120

**kwargs

• **kwargs is a special syntax used in the function definition to pass variable-length keyworded arguments.
• Here, also, “kwargs” is used just by convention. You can also use any other name.
• Keyworded argument means a variable that has a name when passed to a function.
• It is actually a dictionary of the variable names and its value.

def tellArguments(**kwargs):
   for key, value in kwargs.items():
       print(key + ": " + value)
tellArguments(arg1 = "argument 1", arg2 = "argument 2", arg3 = "argument 3")
#output:
# arg1: argument 1
# arg2: argument 2
# arg3: argument 3

38 . What are negative indexes and why are they used?

• Negative indexes are the indexes from the end of the list or tuple or string.
• Arr[-1] means the last element of array Arr[]

arr = [1, 2, 3, 4, 5, 6] #get the last element print(arr[-1]) #output 6 #get the second last element print(arr[-2]) #output 5

Python OOPS Interview Questions

1 . How do you create a class in Python?

To create a class in python, we use the keyword “class” as shown in the example below:

class InterviewbitEmployee:
   def __init__(self, emp_name):
       self.emp_name = emp_name

To instantiate or create an object from the class created above, we do the following:

emp_1=InterviewbitEmployee("Mr. Employee")

Also, to access the name attribute, we just call the attribute using the dot operator as shown below:

print(emp_1.emp_name)
# Prints Mr. Employee

To create methods inside the class, we include the methods under the scope of the class as shown below:

class InterviewbitEmployee: def __init__(self, emp_name): self.emp_name = emp_name def introduce(self): print("Hello I am " + self.emp_name)

The self parameter in the init and introduce functions represent the reference to the current class instance which is used for accessing attributes and methods of that class. The self parameter has to be the first parameter of any method defined inside the class. The method of the class InterviewbitEmployee can be accessed as shown below:

emp_1.introduce()

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