Python interview questions for freshers

Python interview questions for freshers

On February 20, 1991, Guido van Rossum released Python, the first version of the programming language. One of the most popular programming languages, it is interpreted in nature, allowing for dynamic semantics to be easily incorporated. Available as an open language with a basic and clean syntax, it is also very easy to learn and use. This makes learning python simple for developers. Python is the most popular language for general-purpose programming because of its support for object-oriented programming.

Python’s evolution

Among the most popular programming languages of 2015, according to various websites, is Python. One of Python’s most notable features is that it’s both object oriented & open source at the same time. In the meantime, Python is being used by a large number of programmers all around the world to construct graphical user interfaces, websites, and mobile applications. Python’s unique selling point is that it encourages programmers to use less and more understandable code to elaborate on their ideas. A number of Python frameworks are also available to developers to help reduce the amount of time and effort needed to create large and complicated software applications.


Google, Yahoo Communities, Yahoo Map, Linux Weekly News, Shopzilla, and Web Therapy are just a few of the high-traffic websites currently using the programming language. Similarly, Python may be used to create games, financial, scientific, and instructional software. However, multiple dialects of the computer language are still in use by programmers. While Python 2 is utilised by 99.4% of all websites, Python 3 is only used by 0.6% of internet sites. As a result, it’s critical for every Python programmer to be familiar with the language’s many iterations over the years.


How Python Has Been Evolving over the Years?


Programming as a hobby turned into a full-time job. Guido van Rossum originally developed Python as a side project in November 1989 and it has become one of the greatest widely used programming languages today. Since Van Rossum’s office was closed for the holidays, he was in search of a side project to keep him busy. On his list of projects was a Python interpreter for a fresh programming language. As a result, Python was originally conceived as a replacement for ABC. In February 1991, Van Rossum published the code he had written for the interpreter available to the public. However, the Python Software Society is currently in charge of the open-source programming language.

  • Python’s very first release.


In January 1994, Python 1.0 was released. Lambda, filter, map, and reduce were among the functional programming tools included in the major release. More features were added to the latest edition including support for complex integers and a basic data concealing mechanism. Version 1.5 was released in December 1997, followed by version 1.6 in November 2000. In comparison to other computer languages of the era, Python’s first version lacked many essential capabilities. A strong basis was laid for the creation of a robust and cutting-edge programming language by the earliest versions, though.

  • It’s time for Python 2!


List comprehension and garbage collection were added to Python 2.0 in October 2000. List comprehension’s syntax was influenced by Haskell and other functional programming languages. As a result, Python 2.0 prioritised alphabetic keywords above punctuation characters, unlike Haskell. In addition, the garbage collection mechanism gathered up reference cycles that were being thrown away. Many smaller releases came after this large one. Support for nesting scopes and an overhaul of Python’s class and type hierarchy were among the features included in these revisions. Python 2.8 has already been ruled out by the Python Software Foundation, which has said that it will not be released. However, the Foundations will continue to support the programming language’s version 2.7 until the year 2020.

  • It’s time for Python 3!


In December of 2008, Python 3.0 was released. It included a number of new and improved features, as well as a handful of deprecated ones. Features that have been deprecated and backwards incompatibilities have made Python 3 an entirely new version. The deprecated features of Python 2.6 and 2.7 are still used by a large number of developers. However, Python 3’s new capabilities have elevated it to a more current status and broadened its appeal. Many programmers have even made the jump to C# 3.0 in order to take use of these fantastic new capabilities.


As a result, the print statement was omitted from Python 3.0 and replaced instead with the constructed print() function. It also made ordering comparison easier to understand. The ordering comparison operations can now throw a TypeError if the operands are not arranged in a logical order. Text and data are used instead of Unicode & 8-bit strings in version 3 of the computer language. Binary data is represented as encoded Unicode while all code is treated as Unicode by default.


As Python 3 is backwards incompatible, the programmers are unable to utilize capabilities like string exceptions, ancient classes, and implicitly relative imports. In addition, the programmers must be aware of syntax and API updates. Their application can be migrated from Python 2 to Python 3 using a tool named “2to3.” Using comments and warnings, the programme identifies incompatibilities and areas of concern. Helps programmers make improvements and upgrade existing apps to the current version of the programming language.

  • Latest Versions of Python


Programmers currently have the option of using Python version 3.4.3 or 2.7.10. Improved numerical handling and additions to the standard library are included in Python 2.7. The new release makes it much easier for programmers to switch to Python 3. There are numerous new features and libraries, as well as security and CPython implementation updates, in Python 3.4. However, a handful of Python API & programming language features have been deprecated. Python 3.4 can still be used by developers in the long run to get support.

  • A new version of Python is now available


This year, Python 3.9 will be released, while the next version of Python is planned to be released in 2023. It will provide improvements to make the transition from v3 to v4 easier for programmers. A variety of collected features allow experienced Python developers to quickly and easily modernize their existing programmes as they gain more and more experience with Python. However, it will be years before the details of Python 4.0 are revealed to the public. They must, however, keep an eye on the most recent versions in order to smoothly transition to the widely used current version of the coding language.


Python’s second and third versions are diametrically opposed. As a result, each programmer must be familiar with the features of such separate versions, and evaluate their functionality to the project’s requirements. In addition, he must verify the Python version supported by each framework. Python’s new features & long-term support can only be accessed if each developer takes advantage of the most recent version of the language.


The Future Scope of a Python Developer:


Aside from its simplicity and ability to do numerous functions in less lines of code, python’s popularity has skyrocketed. Because of its capacity to handle sophisticated computations utilizing powerful libraries, Python is widely utilized in a variety of different fields, such as machine learning, machine intelligence, web development, and web scraping. In India and around the world, there is a high demand for Python programmers as a result of this. Companies are eager to provide these developers with a wide range of bonuses and incentives.


The world is becoming more digital. It’s all about the data! We’re moving toward an era of petabytes of data of data, then zettabytes & yottabytes, and so on, as the digital transition continues. Automating operations and analyzing vast amounts of data are the keys to the future. Machine Learning (AI), machine & deep learning, the Iot (IoT), and other cutting-edge technologies are at the forefront of this.


The programming languages linked with these new technologies are already becoming popular as they provide the groundwork for the future. As a result, languages like R and Python, amongst many others, are in a very strong position. Using this blog article, we’ll look at Python’s future as a programming and as a career choice for developers. What does this mean for Python programmers in the future? Clearly, the answer is “promising!”


Python is a key component of the future of technology:


Big Data, machine learning, supervised learning, neural networks, & natural language processing (NLP) all rely significantly on the Python programming language. Python was first released in 1989 and is an object-oriented coding language that provides for simple task execution, greater stability, and improved code readability. In comparison to other programming languages, this one is more user-friendly and time-saving. The Anaconda platform, in contrast to previous versions, now operates at a higher clip. Furthermore, it works with Hadoop, a most widely used open source platform for big data. Learn more about this here and some of the errors that Python developers should avoid while working with Big Data in Python by reading this.


Interview Questions For Freshers :


As a matter of fact, Python is steadily gaining ground as the language of choice for those working in the field for Data Science. Python comes out on top in IEEE Spectrum’s interactive ranking of the best programming languages. C, Java, and C++ round out the top three. A similar sentiment is echoed in a HackerRank poll. The Love-Hate index shows that Python is favored by developers of all ages, regardless of their level of experience with the language. Learn how to ace your python interviews by studying the most frequently encountered issues that come up during the interview process :

  • What is Python, and how does it differ from other programming languages? What are some of the advantages of Python?


Python is a general-purpose, high-level programming language that may be interpreted. With the appropriate tools and libraries, it may be used to create nearly any form of application. Objects, modules, threads, outlier, and memory management are all features of python that make it easier to model real-world issues and design solutions for them.


  • A few advantages to learning Python


Program maintenance costs can be reduced by using Python because it offers a basic, understandable programming syntax that places a high value on readability. Additionally, the language is scriptable, open-source, and compatible with third-party packages, all of which promote modularity & code reuse.

High-level data structures with dynamic typing and binding attract a huge developer community for quick application development and deployment..

  • What is a “dynamically typed” language?


Typing is an essential part of understanding a dynamically typed language. In programming languages, type-checking is referred to as “typing.” “1” + “2” will produce a type error in a strongly-typed language like Python because “type-coercion” is not allowed in such languages (implicit conversion of data types). Javascript, on the other hand, will simply return “12” as a result of a weakly-typed language.


  • Typing can be checked in two ways:


Prior to the execution of a programme, all data types are verified.

During execution, data types are checked.

In Python, type-checking is performed on the fly, as each statement is executed one at a time. As a result, Python is a type-free language.

  • What does it mean to speak a language that has been translated into another language?


Line by line, the statements of an Interpreted language are put into action. Interpreted languages include Python, Javascript, R, PHP, & Ruby, to name just a few. An interpreted language software performs directly from the original code without the need for a compilation phase.

  • What is the significance of PEP 8?


For those unfamiliar, PEP means for Python Improvement Proposal. In the Python community, a PEP is a design document that provides information or describes a major update for Python and its processes. It is a formal design document. This version of PEP 8 is critical since it outlines the standards for writing Python code. According to the Python open source community, it appears that adherence to these style requirements is a prerequisite for participation in the community.

  • What does Python’s term “Scope” mean?


Every item in Python has a defined scope in which it operates. Objects in Python stay relevant in a Python scope. A program’s objects are all identified by their namespaces. Namespaces, on the other hand, have a scope where you can use their items without prefixing them with the suffix. The following are a few instances of scopes that are produced during Python code execution:


  • A local scope focuses on the current function’s local objects.
  • To have an object in the global scope means having access to it at any point in time during the execution of your programme.
  • A module-level perspective defined as a global objects of both the current module that can be accessed in the application.
  • When we talk about an outer scope, we’re referring to all of the program’s built-in names. The names of the items in this range are searched for last.

  • Are lists & tuples the same? In what ways are they different?


A collection of items can be stored in a list or tuple in Python. The data types of the objects recorded in the two sequences may differ. Tuples are denoted by parenthesis (‘ansh’, 5, 0.97), while lists use square brackets (‘sara’, 6, 0.19). How do these two vary in reality? The main distinction between lists and tuples is that tuples are immutable, but lists can be changed. Lists can be reordered and sliced on the fly, but tuples can’t be changed in any way whatsoever.

  • What are some of the more frequent Python data types?


Python has a variety of built-in data types. The knowledge of data types & their compatibility with one another is important even if Python does not need them to be stated explicitly during variable declarations. These variables can be checked for their type using the type & isinstance functions provided by Python. There are several ways to categorise these types of data.


  • Python’s null values are represented by the ‘none’ keyword. These NoneType objects can be used to do a boolean comparison.


  • Complex numbers, floating-point numbers, and integers are the three main forms of numeric data. Integers are a subclass of booleans.


  • Lists, tuples, & range objects are the 3 main Sequence Types listed in the Python Docs. The in but not in functions are defined for traversing elements of sequence types. The comparison operators take precedence over these operators.


  • It is possible to convert hashtable values into random Python objects using a mapping object. There is currently just one standard mapping class, the dictionary, which can be changeable.


  • Python currently offers two constructed set kinds – set as well as frozenset – that can be used to store data. The set type is changeable and it can be added and removed using methods like these. Once a frozenset is created, its properties cannot be changed.


  • Modules: Using a module is a way to add more functionality to the Python interpreter. Attribute access is supported using the following command: mymod.myobj, wherein mymod is the module name and myobj is the name declared in m’s lookup table. Direct assignment to the __dict__ module is not possible or advised since the module’s symbols table is located in a highly specific attribute of that module.


  • Types That Can Be Called: Types that can be called using a function call are known as callable types. Instance methods, generating functions and other built-in functions are all examples of these. For more information on callable types, see the documentation at

  • What is the Python term for “pass”?


In Python, a null operation is represented by the pass keyword. Runtime code that has not yet been written but may be executed is typically filled in with this technique. If the code below doesn’t contain a pass statement, we run the risk of encountering runtime issues.

  • In Python, what else are modules and packages?


Python’s modularity is enabled by two mechanisms: packages and modules. There are various advantages to modularizing a project:


  • Focusing on a single component simplifies things because it allows you to concentrate on a more manageable section of the problem. Developers will find it easier and much less error-prone to work with this.
  • In order to ensure maintainability, modules are used to create logical separations between distinct areas of concern. When constructed in a way that minimizes interdependency, it’s much less probable that changes in one module would affect other portions of the programme.
  • Functionality: A module’s functions are readily available to be employed in other portions of the programme.
  • In order to avoid confusion among identifiers from different portions of the programme, modules often establish their own namespace.
  • Python files with extension can include functions, methods, or variables that are declared and implemented as modules. The import statement can be used to import and initialise them only once. Using the from foo import bar, you can import the necessary classes or functions if you just need a portion of their capabilities.


The module namespace can be hierarchically organized using dot notation thanks to the use of packages. In the same way that modules help keep global variable names separate, packages do the same for module names.


Building a package is simple because the system’s built-in file structure is utilized. So, all you have to do is place the modules in a folder, and your package will be known by the folder name. Modules and their contents imported from this package need to have their names preceded by the package name and then connected by a dot.

  • In Python, what do the terms “global,” “protected,” and “private” mean?


The term “global variables” refers to public variables which are defined at a global level. The global keyword is used in a function to refer to a global variable.

An underscore is prefixed to the identifier of a protected attribute, like _sara. They can be accessed and updated from outside of the object they are specified in but a competent developer should avoid from doing so.

__ansh is an example of a private attribute, which has an identifier prefixed with a double underscore. The attempt to modify them from the outside will result inside an AttributeError since they are not accessible from the outside.

  • In Python, what is the purpose of self?

To represent a single instance of a class, the term “self” is used. With this phrase, you can retrieve the properties and behaviors of the object in python. It ties the properties to the arguments they’re passed. self used in many locations and often assumed to be a word. However, unlike C++, the keyword “self” is not used in Python.

  • What does the function __init__ do?


Python’s __init__ function Object() { [native code] } method is automatically invoked when a new object or instance is created, and it allocates memory. The __init__ method can be found in every class. A class’s methods and attributes can be distinguished from local variables thanks to this feature.

  • In Python, what do the terms “break,” “continue,” and “pass” mean?


  • Break : Control immediately moves to the next statement just after body of both the loop following a break statement.


  • Continue : There will be no further statements in this cycle, so all code inside the current incarnation will be skipped, and control will go on to its next iteration.


  • Pass : A semicolon in languages like Java, C++, Javascript, and others represents an empty statement in Python, which is analogous to the way an empty block is represented by a semicolon in Python.

  • In Python, what are unit tests?


Python’s unit test framework is known as unit test.

Unit testing is the process of examining the software’s components one at a time. Why do you think unit testing is so critical? In this hypothetical situation, you’re tasked with creating software that makes use of three different components: A, B, and C. Let’s say at some point your software becomes unusable. How are you going to figure out which part of the programme was the problem? If component A failed, then component B may have failed, and the software may have failed as well. There are countless combinations that could be made.

We need to thoroughly test all and every component in order to determine which one is most likely to cause the software to fail.

  • In Python, what is a docstring?


Docstrings are multiline strings used to describe a specific section of code.

The docstring should explain what the procedure or method is used for in plain English.

  • In Python, what is slicing?


Cutting anything into pieces is what “slicing” refers to.

the [start: stop: step] syntax can be used to slice

the index at which to begin slicing a list and tuple

Stop is the last index, or the location at which sop should be applied.

The series of phases to jump is called a step.

Start is set to zero, stop to the number of items, and step is set to one by default.

Strings, arrays, lists, & tuples can all be sliced.

  • Do lists and arrays have the same purpose?


You can only use the same types of data in python arrays; this is known as homogeneity. In comparison to lists, it’s a much lighter wrapper for C language arrays.

Using python, it is possible to have a list with elements of multiple data kinds. It has the drawback of taking up a lot of memory.

  • How does Python handle memory?


The Python Memory Manager is responsible for all aspects of memory management in Python. For Python, there is a private heap area allocated by the manager. This private heap is where all of Python’s objects are kept, and it cannot be accessed by the programmer. Despite this, python provides some basic API functions for accessing the private heap region.

The unused memory in the private stack space is recycled by Python’s built-in garbage collection.

  • What are namespaces in Python? What is the purpose of their use?


By using a namespace, you’re ensuring that each program’s object names are distinct and can’t be confused with any other names. These namespaces are implemented in Python as dictionaries, where each key corresponds to an object. In this way, numerous namespaces can share the same name as well as map this to a different kind of thing. Namespaces include the following:


  • A function’s local names are stored in the local namespace. When a function is called, a namespace is generated for the duration of the call.
  • Imported packages and modules are included in the current project’s Global Namespace. When the package gets imported into the script, this namespace is generated and persists until the script is executed.
  • Core Python functions and the names of various types of exclusions are included in the built-in Namespace.
  • A namespace’s lifespan is determined by the scope of the objects it is mapped to. The namespace’s lifespan finishes when an object’s scope expires. As a result, objects in an inner namespace cannot be accessed through an outer namespace.

  • The resolution of the scope in Python?


Objects with the same name but different functions can exist within the same scope. Python automatically handles scope resolution in these circumstances. The following are some examples of this type of behavior:


  • Math and cmath modules in Python share several functions, such as log10(), acos(), exp(), etc. Prefixing them with their appropriate modules, such as math.exp() and cmath.exp, is important to clear up any confusion ().
  • Consider the following code, which sets a global object temp to 10 before increasing it to 20 on a function call. The call to the function did not, however, alter the global value of the temp. For example, in this example, we can see that Python clearly distinguishes between global and regional variables.

  • In Python, what are the decorators for?


Python’s decorators are functions that extend the functionality of an existing function without altering its underlying structure. Using @decorator names in Python, they can be referred to as “bottom-up” decorators.


They are useful because they can receive inputs for methods and change them further before sending them on to the function itself, in addition to just adding functionality towards the method’s output. The ‘wrapper’ function, or inner nested function, is critical here. It is used to ensure encapsulation and keep its implementation out of sight from the rest of the system.

  • In Python, how do you make a copy of an object?


The assignment operator (=) doesn’t really copy objects in Python. Because the target local variable already exists, it creates a binding. The copy module in Python is used to make copies of an object. In addition, the copy module has two methods for making duplicate copies of a given object:


An item is copied bitwise using a shallow copy. The values of the duplicated object are identical to those of the original object. In cases where one of the variables is a reference to another object, only the reference addresses are copied.

Recursively, it replicates all of the source object’s referenced objects as well as its values to the target object.

  • To pickle or not to pickle?


Serialization is available in the Python library. In order to be able to retrieve the original object later on, serializing an object means converting it into a version that can be stored. In this case, the pickle module is activated.


  • Pickling:


Python’s serialization method is referred to as “pickling” by programmers. You can write a file to the memory of any Python object by serializing it into a byte stream. Pickling is a compact process, but pickle items can be shrunk even further. The serialization is also reusable across different versions of pickle because it takes account of the things it has serialized.

The pickle.dump method is used for this task ().


  • Unpickling:


The opposite of pickling is unpickling. In order to construct new objects from the ones saved in the file, it deserializes the bytes stream and loads them into memory.

Pickle is the function that was used in the previous step.


  • Pythonpath is a Python term, Define.


If you want to add extra directories wherein Python will search for modules and packages, you can use the ‘Pythonpath’ environment variable. Managing Python libraries that are not installed in the global default state is made easier with this feature.

  • The help() & dir() functions serve no purpose?


The documentation for modules, classes, functions, and other concepts can be viewed by invoking the help() method in Python. The help() function launches an interactive help programme on the console if no parameters are supplied to it.

An object’s properties and methods are returned using the dir() function. As it seeks to produce the much more relevant data, instead of a whole information, it behaves differently to various objects.


  • For Components objects, it produces a list of any and all characteristics, contained within this module.
  • It provides a list of any and all valid characteristics and base attributes for Class Objects.
  • It produces a set of qualifications in the scope if no arguments are supplied.
  • File extensions ending and.pyc have different meanings in Python.


The source code for an application is stored files. While the.pyc file includes your program’s bytecode. After file has been compiled, we are given bytecode (source code). Not all of the programmes you run produce.pyc files. Only the imported files are used to generate it.

Python interpreter looks for compiled files before running a programme. The virtual machine will run the file if it is present. If file isn’t discovered, it looks for that as well. The python virtualization executes it if it is found in the.pyc file.

A.pyc file reduces the time it takes to compile.

  • How does Python get translated?


Python is not interpreted as well as compiled as a language. Interpreted or generated is the characteristic of both the implementation. In general, Python is interpreted as a bytecode (a set of interpreter-readable instructions).


Python creates a set of virtual machine instructions from the source code. This virtual machine is implemented in the Python interpreter. Bytecode is the name given to this intermediate format, which is created by compiling source code into.pyc. The original CPython or JIT (Just in Time) compiler compiled by PyPy can then interpret this bytecode.

  • Does Python support both value and reference passing of arguments?


When passing by value, a copy of the actual product is passed. Changing the value of an object’s replica will not modify the object’s original value.


  • When sending data using this method, a reference to the actual object is provided. Whenever the value of a new object is changed, the value of the old object is altered as well.
  • An argument in Python programming is the reference towards the real object that an object has to a real object.

  • Calling the parent class without creating an instance is feasible?


If the class diagram is a main stage or is used by other children classes, then the answer is yes.

  • In Python, how do you create an empty class? 


Members of an empty class are not defined in it. The pass keyword is used to build it (the pass command does nothing in python). We don’t have to use the class to create an object for such a class.




Object-oriented programming language Python is a popular choice for web application development. Python’s simplicity, reliability, and ease of interfacing make it a popular choice for 90% of the population. It can be used in a wide range of industries because of its combination of robust scripting and a quick application development process. Open-source systems built on Python offer a wide range of tools for building secure and high-performance web applications. Python is based on the paradigms of procedural and object-oriented coding, and as a result, Python-written applications produce code that is easy to read and maintain.

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