Have you wondered why big shots like Intel, IBM, NASA, Pixar, Netflix, Facebook, JP Morgan Chase, and Spotify, vouch for Python? Firstly it is one of the leading programming languages. Apart from that, the tech was developed by Guido van Rossum back in the year 1991. It was an open-source, free-to-use object-oriented programming language featuring simple and clean syntax.
Over a few years, there has been a thread that repeatedly keeps meeting my eye. It says how to successfully crack Python interview questions and answers.
What are the best practices to follow if you want to aspire to become a successful Python developer? But before that let us take a bit of a detour and find out why more and more people are trending towards Python and even after 30+ years, why it still holds the position of the best programming languages across the globe.
The programming language has been around for more than 3 decades and many more to come. So without further delay, let’s dive into the brief history of Python.
Introducing Python
As we all know it is one of the leading programming languages found across the globe. And not just any, this one is a high-level well-interpreted programming language featuring simple syntax that assures seamless readability and extremely user as well as beginner-friendly purpose.
Python came into existence because of Guido Van Rossum, he desired to come up with a programming language that was simple to use and beautiful to look at.
Slowly and steadily, the language seems to have widespread applicability among different industry verticals such as mobile app developers, web developers, data scientists, researchers, and a lot more.
Python
print(“Hello world.”)
Java
public class HelloWorld {
public static void main (String[]args) {
System.out.println.(“Hello world”);
}
}
Can you see the difference? In addition, Python is a general-purpose language which means it is highly recommended for developing a wide range of applications. And not just that, Python is also considered for automating tasks, developing websites or software, and analyzing data to a great extent.
- It is easy to read
- It works wonders when it comes to performance
- It is an open-source
- It is a cross-platform language
- It has a broad standard library
- It is more productive
- An active and large community of users
- It has a high corporate demand
- It has become a go-to language in academic scenarios such as solving different equations, performing statistical analyses, simulating and tracking particle diffusion, and more.
Above all, Python was the third most demanded language in 2022 by recruiters worldwide. So where and when exactly Python can be used?
● For conducting web and software development projects
Python is one of the leading and most popular languages which is highly recommended for conducting web and software development projects. Here it is possible to create complex, multi-protocol applications while maintaining concise, readable syntax. You can google yourself and find out how many applications are developed using Python.
Not just that, the programming language features an open-source community that offers developers an extensive amount of reusable code, frameworks, and support. Django is one of the best or top-rated Python frameworks which is designed by experienced developers to assist others in accelerating their application development projects by rectifying issues well in advance which could block their progress.
● For task automation
Another better way of considering Python is task automation. It has a sincere ability to automate manual, repetitive tasks. It may quite interest you to know that Python enables one to automate anything by using built-in modules or pre-written code from its robust library. At the same time, it is possible to write your custom scripts to perform specific actions. For example, it is now possible to automate emails with the “smtplib” module or copy files with the “shutil” module.
In addition, the programming language comprises a robust set of testing frameworks which makes it one of the excellent languages to consider for test automation. Frameworks such as Pytest, Behave, and Robot enable developers to write simple yet effective tests to ensure the quality of their builds.
● For machine learning and data science
The next excellent use of Python is considered for machine learning and data-science-based projects. As we know Python is the top preferred language considered for data science and research purposes. As mentioned earlier, Python’s syntax is completely easy to understand and adapt so even professionals with little-to-no development experience can easily learn Python and use it to manipulate data for research, reporting, predictable or regression analyses, and more.
In such cases, collecting and parsing data can be way more time-consuming, especially for data scientists. No wonder this is one of the top languages to consider for training machine learning (ML) models. You see specific algorithms, these models can be analyzed and different patterns can be identified within the data to ensure to come up with predictions or decisions based on that data.
These keep on evolving constantly based on outputs of previous datasets just to confront new variables. Data scientists and developers training ML models often utilize libraries, such as NumPy, Pandas, and Matplotlib, to automate functions like cleaning, data transformation, and visualization.
● For financial analysis
Much like the fact that Python can work wonders with data scientists with the heavy lifting of large data sets, the programming language is widely used within the financial industry to perform quickly, especially in regards to performing some of the most complex computations. Stock market tends to generate large and large amounts of data and Python is highly recommended for importing data on stock prices and generating relevant strategies via algorithms to identify a wide range of trading opportunities.
These days python is highly recommended for portfolio optimization, risk management, financial modeling and visualization, cryptocurrency analysis, and even fraud detection.
● For artificial intelligence
Python interestingly can be encountered in some of the most complex, artificial intelligence technologies. It turns out to be one of the most preferred languages for AI. Python’s concise and readable code allows developers to create consistent, reliable systems, and its vast library.
Some of its best frameworks include Pybrain which offers developers to come up with power-packed algorithms especially to conduct a wide range of successful machine learning tasks. In addition, Python’s visualization capabilities can help convert these large datasets for AI or ML into comprehensible graphs or reports. Interestingly enough, OpenAI, the artificial intelligence research lab, utilises the Python framework, Pytorch, as its standard framework for deep learning, which trains its AI systems.
So whether you are conducting a web or a software development project, automating tasks, machine learning and data science, financial analysis, or artificial intelligence, Python can be used for anything and at any point in time.
Why Should You Pick Python as Your Career Choice?
- High demand for Python developers – the demand for Python developers keeps on increasing now and then. The growing demand itself shows how stable the current market is and what kind of opportunities might be lined up for you.
- Competitive Salaries – Now this one can be an eye-opener, you see Python developers are pretty much compensated for their skills. As mentioned above, Python is highly recommended for work in complex fields such as data science and machine learning. And if you have specialized here, it is possible to expect higher salaries.
- Active, large, and thriving community – This is given, that Python comprises a wide range of vast and active communities, which means several resources such as libraries, frameworks, and tools are continually being developed and improved. This supportive community also makes it easier to learn and solve problems together.
- Growth opportunities – Learning Python opens the door to mastering different ranges of technologies and programming languages. You see all the foundation skills you gain can successfully switch you into the roles of such as data scientist, machine learning engineer, or cybersecurity expert.
Who is a Python Developer?
A Python developer is a professional person who is highly responsible for designing, coding, and deploying development projects in the Python language. They also require you to work on debugging the same projects just to ensure they are functioning as well as needed.
So as a developer, it is also your responsibility to work closely with other teams for conducting a variety of tasks such as data collection and analysis. You see once you enter Python development, a wide range of job possibilities can be opened. Your roles and responsibilities include:
- Scripting design
- Assistance for automation deployment
- Write reusable, testable, and efficient Python code
- Web frameworks for the new codes
- Data review
- Integrate data storage solutions
- Create integrative systems
- Grazing the interest
- Integrate user-facing elements and understand end-user requirements
- Constructing back-end components to connect
The Right Career Path – How to Become a Python Developer?
- The first and foremost step to take into consideration is learning or getting acquainted with all kinds of Python basics. Try going through all kinds of online courses, and tutorials, and practicing coding. Now here you can begin by assisting on small projects reinforcing whatever you have learned and developing your problem-solving skills.
- Once you begin getting used to working on small projects, start working on more and more significant projects, and do not forget to keep contributing to different open-source initiatives. This can successfully broaden your horizons. A well-rounded portfolio is much needed to showcase your skills to potential employees and clients.
- Becoming a developer whether you are a Java developer or a Python developer is a continuous process. The tech field keeps on evolving at a pretty breathtaking speed. So being up to date is an extremely crucial aspect here. Try participating in as many forums as you can, attend all relevant workshops, and remain competitive, this is the only way to shine.
In addition, further below I would like to mention certain important questions and answers which can assist you in cracking the initial Python interview. I assure you that once you have mastered these questions and answers, it will create vast opportunities and a rewarding career path.
Top Python Interview Questions and Answers
For Freshers
1. Define __init__?
__init__ is a constructor method in Python and is automatically considered for allocating memory, especially in case a new object/instance is developed. Now all classes tend to consist of a __init__ method which is successfully associated. In addition, this method in general tends to assist in successfully differentiating methods as well as attributes of a class from local variables.
# class definition
class Student:
def __init__(self, fname, lname, age, section):
self.firstname = fname
self.last name = lname
self.age = age
self.section = section
# creating a new object
stu1 = Student(“Sara”, “Ansh”, 22, “A2”)
2. Can you spot the difference between Python Arrays and Lists?
Well, Arrays in Python mainly consist of elements of the same data types which means, the data type of the array should be homogenous. Now this is a thin wrapper around C language arrays. And do you know what is the best part here, it has the potential to consume much less memory in comparison to the list.
Whereas a list in comparison in Python comprises different elements of different types. Now data types of lists can be heterogeneous. It has the disadvantage of consuming large amounts of 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
3. 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 stop.
- step is the number of steps to jump.
- The default value for start is 0, the stop is a number of items, and step is 1.
- Slicing can be done 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]
4. What is the use of self in Python?
Self is mainly used to represent the instance of the class. When you come up with the keyword, it is possible to access different attributes and methods of the class in Python. Also, the self has the potential to bind the attribute with the given arguments. Self is successfully used in different places and often misconstrued as a prime keyword. Now don’t be confused between C++ and self, since it is not a keyword in Python.
5. Can you explain the concept of modules and packages in Python?
Python is pretty known for its modular programming. And why not, since modularizing comprises a wide range of advantages such as:
- Great simplicity – When working successfully on a single module, Python can successfully assist in focusing on a pretty small portion of the problem at hand. So as a result the overall development is quite easier and less error-prone.
- Maintainability – Modules are successfully designed to enforce logical boundaries between a wide range of problem domains. So in this case, if they are written in a particular manner which reduces independence, it is less likely that modifications in a module might impact other parts of the program.
- Reusability – Functions are defined in a module that can be reused by different parts of the application.
- Scoping – Modules tend to define a separate namespace. So what happens is there is no scope for any kind of confusion between identifiers from other parts of the program.
Modules are Python-based files featuring .py extension which can have a set of functions, classes, and variables successfully defined and implemented.
Modules are the ones that 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 enable successful hierarchical structuring of the module namespace with the assistance of dot notation. Modules successfully assist clashes between global variable names and packages assist in avoiding clashes between module names.
Now developing a package is easy. Do you know why? It uses the system’s inherent file structure. So all required is stuffing the modules within the folder and there you go. Keep the folder name the same as the package name. Now importing a module as well as its contents from the package requires the package itself name as a prefix to the module name joined by a dot.
6. What is the scope of Python?
Every object in Python functions within a scope. So what exactly is a scope is a block code where an object in Python tends to remain relevant. Now namespaces tend to successfully identify all the kinds of objects within a program. However, it may also interest you to know that namespaces tend to have a scope pre-defined where it is possible to use their objects without any prefix. Some of the basic examples to take into account include:
- A local scope where local objects are available in the current function.
- A global scope where objects successfully are 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. The objects in this scope are searched last to find the name referenced.
Note: Local scope objects can be synced with global scope objects using keywords such as global.
7. Define PEP 8 and its importance.
PEP successfully stands for Python Enhancement Proposal. Now this one is an official design document that successfully offers all the relevant information to the Python community. PEP 8 successfully documents the style guidelines for Python Code. Contributing to the Python open-source community requires you to follow these style guidelines sincerely and strictly.
For experienced
1. Can you explain Dict and List comprehensions?
Python-based comprehensions are more like decorators which are syntactic sugar constructs that can successfully assist in developing altered and filtered lists, dictionaries, and sets from a given list, dictionary as well as sets. Now when we use comprehensions, lots and lots of time and code can be saved which might considerably be more verbose.
If you are planning to perform 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}
If you are 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}
When you think of combining 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)]
2. What is Scope resolution in Python?
Scope resolution is considered in special situations, situations where objects within the same scope have the same name but tend to function pretty differently. Some of the common examples of such behavior include:
- Python modules namely ‘maths’ and ‘cmath’ tend to comprise lots and lots of functions which are common log10(), acos(), exp() etc. To successfully resolve such ambiguity, it turns out to be extremely important with their respective modules such as math.exp() and cmath.exp().
- Now here I would like to elaborate with an example where an object temp has been successfully initialized to 10 globally and then to 20 on a function call. Now do you think the function call did change the value of tempt globally? Of course not! Python successfully draws a clear line between global and local variables and this enables us to treat 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 successfully 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
3. Explain the memory management in Python?
Memory management in Python is successfully handled by the Python memory manager. Now the memory allocated by the manager is basically in the form of a private heap space dedicated to Python. So all Python-based objects are stored in this particular heap and tend to remain private.
It is inaccessible to the programmer completely. Python does comprise API functions to successfully work upon the private heap space. Additionally, Python has an in-built garbage collection to recycle unused memory for the private heap space.
4. What does *args and **What do kwargs mean?
- *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
- This one is a special syntax considered in the function to pass variable-length keyworded arguments.
- Kwargs is used by convention and it is also possible to use any other name.
- A keyworded argument means a variable that has a name especially when passed to a function.
- If you are looking for a dictionary of the variable’s names and its value then Kwargs is it.
5. What are iterators in Python?
An iterator is an object that remembers its state, especially where it is during conducting iteration. So what initializes the iterator is __iter__() method.
This one features a __next__() method which successfully returns the next item in iteration and it successfully points to the very next element. Now once it reaches the end of the iterable object __next__() must return a StopIteration exception.
This one 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
6. Can you interpret Python?
If you think Python as a language is interpreted or compiled then you are wrong. Now interpreted or compiled is the property of the implementation. Python is a bytecode(set of interpreter readable instructions) interpreted generally.
The source code is a file with .Py extensions.
Python compiles source code to a set of instructions for a virtual machine. In one way or the other, it can be said that Python is an implementation of that particular virtual machine. The intermediate format is considered as bytecode.
The bytecode can be then interpreted by the official CPython or JIT(Just in Time compiler) compiled by PyPy.
7. How do you copy an object in Python?
In Python technology, the assignment statement does not copy objects. So what happens is that it creates a strong binding between the existing object and the target variable name. So to successfully create copies of an object in Python. Here you can try using the copy module. Now when you create a copy, there are two ways of creating copies for the given object using the copy module.
Shallow copy – Shallow copy is a bitwise copy of an object. The copied object tends to have 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 – Here all values recursively from the source to the target object, i.e. it even duplicates the objects referenced by the source object.
from copy import copy, deep copy
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]
Conclusion
So this is it! I do hope you find the following post worth the read. Becoming a Python developer is quite a promising career. These are the top important Python Interview Questions to crack in your Python Onli