Introduction
This post was submitted by Arpit Jain from NearLearn, one of the top-rated Corporate Training Companies in Bangalore, India. They offer training on 500+ Technical, Management, and Non-Technical Courses, including learning Python.
Guido van Rossum created Python, which was originally released on February 20, 1991.
It is one of the most frequently used and beloved programming languages, and because it is interpreted, it allows for the incorporation of dynamic semantics.
Additionally, it is a free and open-source language with very straightforward and clear syntax.
These characteristics simplify the process of learning Python for developers.
Additionally, Python allows object-oriented programming and is frequently used for general-purpose programming.
Python’s popularity is soaring due to its simplicity and ability to accomplish several functions with fewer lines of code.
Python is also utilized in Machine Learning, Artificial Intelligence, Web Development, and Web Scraping, among other fields, due to its ability to handle sophisticated calculations through the usage of strong libraries.
As a result, demand for Python developers is high in India and throughout the world.
Companies are willing to offer these developers incredible advantages and privileges.
We’ll look at the most often asked python interview questions and answers in this article, which will help you thrive and land excellent job offers.
Python Interview Questions and Answers for Newcomers
First and foremost, Python is a programming language.
Python is a general-purpose, high-level, interpreted programming language.
As a general-purpose language, Python may be used to create virtually any form of application with the appropriate tools/libraries.
Additionally, Python includes objects, modules, threads, exception handling, and automatic memory management, all of which aid in the modeling of real-world issues and the development of applications to address them.
Python’s advantages include the following:
Python is a general-purpose programming language with a straightforward, easy-to-learn syntax that places a premium on readability, hence lowering the cost of program maintenance.
Additionally, the language enables scripting, is totally open-source, and encourages modularity and code reuse through the usage of third-party packages.
Its high-level data structures, paired with dynamic typing and binding, attract a sizable developer community for Rapid Application Development and deployment.
Before we can comprehend a dynamically typed language, it is necessary to grasp what typing is.
Typing is a term that refers to the type-checking that occurs in programming languages.
For instance, “1” + 2 will result in a type error in a highly typed language, such as Python because these languages do not provide “type-coercion” (implicit conversion of data types).
On the other hand, a language with a weak type system, such as Javascript, will simply print “12” as the result.
Typing can be accomplished in two stages –
Static – Prior to execution, data types are validated.
Dynamic – During execution, data types are verified.
Python is an interpreted language, which means that each statement is executed line by line. As a result, type checking occurs on the fly during execution.
Python is therefore a Dynamically Typed Language – it’s both written and checked simultaneously .
A language that is interpreted executes its statements line by line.
Python, Javascript, R, PHP, and Ruby are all excellent instances of interpreted languages.
Programs created in an interpreted language execute immediately from the source code, without a compilation step in between.
Python Enhancement Proposal is an acronym for Python Enhancement Proposal.
A PEP is an officially sanctioned design document that informs the Python community or describes a new feature for Python or its processes.
PEP 8 is particularly significant since it establishes stylistic rules for Python code.
Contributing to the Python open-source community appears to require a sincere and tight adherence to these stylistic rules.
Each object in Python has a scope. In Python, a scope is a piece of code within which an object remains relevant.
Namespaces are used to uniquely identify all items contained within a program.
However, these namespaces have a scope established for them in which their objects can be used without a prefix.
The following are a few examples of scope created during Python code execution:
- A local scope denotes the items accessible within the current function.
- The term global scope refers to the items that have been available throughout the execution of the code since their genesis.
- A module-level scope refers to the global objects accessible in the current module.
- An outermost scope encompasses all built-in names that can be invoked within the program.
The scope’s objects are searched last for the name referenced.
Nota bene: Using keywords such as global, local scope objects can be synchronized with global scope objects.
Both Lists and Tuples are sequence data types in Python that can be used to hold a collection of things.
Both sequences can contain objects of varying data kinds.
They are given distinct visual indications of which is which. Square brackets denote lists [‘sara’, 6, 0.19], while parentheses denote tuples (‘ansh’, 5, 0.97).
However, what is the true distinction between the two?
The critical distinction between the two is that whereas lists are mutable, tuples are immutable objects.
This means that whereas lists can be updated, appended, or sliced in real-time, tuples remain constant and cannot be altered in any way.
To verify the difference, execute the following example in Python IDLE:
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’
Python packages and Python modules are two techniques that enable Python programmers to write modular code.
Modularization has a number of advantages – simplicity and reusability.
Simplicity: Concentrating on a single module enables you to concentrate on a manageable amount of the task at hand.
This simplifies development and reduces the likelihood of errors.
Modules are intended to impose logical boundaries between distinct issue domains.
If modules are constructed in a way that minimizes interdependence, it is less likely that changes to one will affect other sections of the program.
Reusability: Module-defined functions can be easily repurposed by other components of the application.
Modules generally establish their own namespace, which helps avoid confusion with other parts of the program’s identifiers.
Modules are essentially Python files with an a.py extension that contain a collection of declared and implemented functions, classes, or variables.
The import statement can be used to import and initialize them all at once.
If only a portion of the capability is required, use from foo import bar to import the necessary classes or functions.
Packages enable the module namespace to be structured hierarchically using dot notation.
As modules assist in avoiding conflicts between global variable names, packages assist in avoiding conflicts between module names.
The process of creating a package is straightforward since it makes use of the system’s built-in file structure.
Therefore, simply place the modules in a folder, and voila, the folder name serves as the package name.
When importing a module or its contents from this package, the package name must be used as a prefix followed by a dot.
Note that you can technically import the package as well, however it does not import the modules contained within the package into the local namespace, making it nearly worthless.
Global variables are defined in the global scope.
The global keyword is used to reference a variable in the global scope within a function.
Protected attributes are defined by prefixing their identification with an underscore, e.g. _sara.
They are still accessible and modifiable from outside the class in which they are defined, but a prudent developer should avoid doing so.
Private attributes are those that have a double underscore preceding their identifier, for example, __ansh.
They cannot be accessed or updated directly from the outside, and any effort to do so will result in an AttributeError.
Self is used to represent the class’s instance.
In Python, this keyword allows you to access the class’s attributes and methods.
It associates the attributes with the arguments passed in.
Self is used in a variety of contexts and is frequently mistaken for a term.
However, unlike in C++, self is not a Python keyword.
Python’s unit test framework is called the unit test.
Unit testing is the process of evaluating distinct components of software independently.
Consider the following scenario: you are developing software that makes use of three components, namely A, B, and C. Now consider the possibility that your program will fail at some point in the future. How are you going to determine which component caused the software to fail? Perhaps it was component A that failed, which in turn caused component B to fail, and therefore the software as a whole.
Numerous such combinations are possible.
This is why it is critical to thoroughly test each component so that we can determine which component is most likely to be the cause of the software’s failure.
A documentation string, or docstring, is a multiline string that is used to describe a particular section of code. The docstring should contain a description of the function or method.
As the name implies, ‘slicing‘ is the act of removing portions. Specifically, slicing let’s us select a subset of indices.
[start: stop: step] is the syntax for slicing.
- start specifies the index at which to begin slicing a list or tuple.
- stop is the terminating index or the location of the sop.
- step denotes the number of steps required to complete the jump.
- Start is set to zero by default, stop is set to the number of items, and step is set to one.
- Strings, arrays, lists, and tuples can all be sliced.
#!/usr/bin/env python must begin the script file.
In Python, arrays can only contain components of the same data type, i.e., the array’s data type must be homogeneous.It is a lightweight wrapper for C language arrays that uses significantly less memory than lists.
Python lists can contain elements of several data types, i.e., their data types can be heterogeneous.
The drawback for lists is that it consumes a lot of memory.
In Python, a namespace ensures that object names are unique and can be used without conflict.
Python implements these namespaces as dictionaries with a corresponding ‘object as value’ for each ‘name as key’.This enables many namespaces to share the same name and map it to a distinct object.
The following are a few examples of namespaces:
- Local Namespace is used to define local variables within a function.
The namespace is formed for the duration of the function call and is then deleted after the function returns.
- The Global Namespace contains the names of all imported packages/modules used in the current project.
This namespace is formed when the script imports the package and persists until the script is executed.
- The Built-in Namespace contains fundamental Python functions and built-in names for many types of exceptions.
The lifespan of a namespace is determined by the scope of the objects to which it is assigned.When the scope of an object expires, the namespace’s lifespan expires as well.
As a result, it is not possible to access objects in an inner namespace from an outside namespace.
Wrap Up
We’ve just discussed some of the most often requested interview questions for python developers in this article.
These questions, combined with regular problem-solving sessions, will prepare you to succeed in any Python-based interview.
Python has grown in popularity among the developer community over the years due to its simplicity and ability to enable heavy calculations.
As a result, the demand for competent Python developers continues to grow.
Nonetheless, it’s worth noting that the benefits of becoming a Python developer are rather good.
Along with theoretical knowledge of Python, a strong focus is placed on the ability to write high-quality code.
