What exactly is Python?

 

PYTHON

Guido van Rossum created Python, initially released on February 20, 1991. It is one of the most popular and well-liked programming languages, and since it is interpreted, it allows for the incorporation of     dynamic semantics.        

It's also a free and open-source   language  with  straightforward syntax, making it simple for programmers to learn Python.

Python also allows object-oriented programming and is the most widely used programming language.

Python's popularity is skyrocketing, thanks to its ease of use and ability to perform several functions with fewer lines of code. Because of its capacity to handle strong calculations utilizing powerful libraries, Python is also utilized in Machine Learning, Artificial Intelligence, Web Development, Web Scraping, and many other disciplines. As a result, Python developers are in high demand in India and worldwide, and companies are eager to provide these professionals with incredible advantages and privileges.

Interview Questions in Python

1.   What exactly is Python?

Python is a general-purpose, high-level, interpreted programming language. The correct tools/libraries may be used to construct practically any application because it is a general-purpose language. Python also has features like objects, modules, threads, exception handling, and automated memory management, which aid in modelling real-world issues and developing programs to solve them.

2.   What are the advantages of Python?

Python is a general-purpose programming language with a simple, easy-to- learn syntax that prioritizes readability and lowers program maintenance

costs. Furthermore, the language is scriptable, open-source, and enables third-party packages, promoting modularity and code reuse.

Its high-level data structures, along with the dynamic type and dynamic binding, have attracted a large developer community for Rapid Application Development and deployment.

3.   What is the definition of dynamically typed language?

We must first learn about typing before comprehending a dynamically typed language. In computer languages, typing refers to type-checking. Because these languages don't allow for "type-coercion," "1" + 2 will result in a type error in a strongly-typed language like Python (implicit conversion of data types). On the other hand, a weakly-typed language, such as JavaScript, will simply return "12" as a result.

There are two steps to type-checking

 

Static - Data Types are checked before execution. Dynamic - Data Types are checked during execution.

Python is an interpreted language that executes each statement line by line. Thus type-checking happens in real-time while the program is running, and python is a Dynamically Typed Language as a result.

4.   What is the definition of an Interpreted Language?

The sentences in an Interpreted language are executed line by line. Interpreted languages include Python, JavaScript, R, PHP, and Ruby, to name just a few. An interpreted language program executes straight from the source code without a compilation phase.

5.   What is the meaning of PEP 8, and how significant is it?

Python Enhancement Proposal (PEP) is an acronym for Python Enhancement Proposal. A Python Extension Protocol (PEP) is an official design document that provides information to the Python community or describes a new feature or procedure for Python. PEP 8 is particularly important since it outlines the Python code style rules. Contributing to the

Python open-source community appears to need a serious and tight adherence to these stylistic rules.

6.   What is the definition scope in Python?

In Python, each object has its scope. In Python, a scope is a block of code in which an object is still relevant. Namespaces uniquely identify all the objects in a program. On the other hand, these namespaces have a scope set for them, allowing you to utilize their objects without any prefix. The following are a few instances of scope produced during Python code execution:

 

Those local objects available in a particular function are a local scope.

A global scope refers to the items that have been available from the beginning of the code execution.

The global objects of the current module that are available in the program are referred to as a module-level scope.

An outermost scope refers to all of the program's built-in names. The items in this scope are searched last to discover the name reference.

Note: Keywords like global can sync local scope items with global scope ones.

9. What is the meaning of pass in Python?

In Python, the pass keyword denotes a null operation. It is commonly used to fill in blank blocks of code that may execute during runtime but has not yet been written. We may encounter issues during code execution if we don't use the pass statement in the following code.

20.   How does Python handle memory?

The Python Memory Manager is in charge of memory management in Python. The memory allotted by the manager is in the form of a Python- only private heap area. This heap holds all Python objects, and because it is private, it is unavailable to the programmer. Python does, however, have several basic API methods for working with the private memory area.

Python also features a built-in garbage collection system that recycles unneeded memory for the private heap area.

21.   What are namespaces in Python? What is their purpose?

In Python, a namespace ensures that object names are unique and used without conflict. These namespaces are implemented in Python as dictionaries with a 'name as key' and a corresponding 'object as value.' Due to this, multiple namespaces can use the same name and map it to a different object. Here are a few instances of namespaces:

 

Within a function, local names are stored in the Local Namespace. A temporary namespace is formed when a function is called, removed when the function returns.

The names of various imported packages/modules used in the current project are stored in the Global Namespace. When the package is imported into the script, this namespace is generated and persists until executed.

Built-in Namespace contains essential Python built-in functions and built-in names for different sorts of exceptions.

The lifespan of a namespace is determined by the scope of objects to which it is assigned. The lifespan of a namespace comes to an end when the scope of an object expires. As a result, accessing inner namespace objects from an outside namespace is not feasible.

22.   What is Python's Scope Resolution?

Objects with the same name but distinct functions exist inside the same scope. In certain instances, Python's scope resolution kicks in immediately. Here are a few examples of similar behavior:

Many functions in the Python modules 'math' and 'cmath' are shared by both

-   log10(), acos(), exp(), and so on. It is important to prefix them with their corresponding module, such as math.exp() and cmath.exp(), to overcome this problem.

Consider the code below, where an object temp is set to 10 globally and subsequently to 20 when the function is called. The function call, however,

did not affect the global temperature value. Python draws a clear distinction between global and local variables, interpreting their namespaces as distinct identities.

23.   Explain the definition of decorators in Python?

Decorators in Python are simply functions that add functionality to an existing Python function without affecting the function's structure. In Python, they are represented by the name @decorator name and are invoked from the bottom up.

The elegance of decorators comes in the fact that, in addition to adding functionality to the method's output, they may also accept parameters for functions and change them before delivering them to the function. The inner nested function, i.e., the 'wrapper' function, is crucial in this case, and it's in place to enforce encapsulation and, as a result, keep itself out of the global scope.

24.   What are the definitions of dict and list comprehensions?

Python comprehensions, like decorators, are syntactic sugar structures that aid in the construction of changed and filtered lists, dictionaries, and sets from a given list, dictionary, or set. Using comprehensions saves a lot of effort and allows you to write less verbose code (containing more lines of code). Consider the following scenarios in which comprehensions might be highly beneficial:

 

Performing math operations throughout the full list Using conditional filtering to filter the entire list

Multiple lists can be combined into one using comprehensions, which allow for many iterators and hence can be used to combine multiple lists into one.

Taking a multi-dimensional list and flattening it

A similar strategy of nested iterators (as seen before) can be used to flatten a multi-dimensional list or operate on its inner members.

25.   What is the definition of lambda in Python? What is the purpose of it?

In Python, a lambda function is an anonymous function that can take any number of parameters but only have one expression. It's typically utilized when an anonymous function is required for a brief time. Lambda functions can be applied in two different ways:

To assign lambda functions to a variable, do the following: 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

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

The assignment statement (= operator) in Python doesn't duplicate objects. Instead, it establishes a connection between the existing object and the name of the target variable. In Python, we must use the copy module to make copies of an object. Furthermore, the copy module provides two options for producing copies of a given object –

A bit-wise copy of an object is called a shallow copy. The values of the cloned object are an identical replica of the original object's values. If one of the variables references another object, just the references to that object are copied. Deep Copy recursively replicates all values from source to destination object, including the objects referenced by the source object.

28. What are the definitions of pickling and unpickling?

"Serialization out of the box" is a feature that comes standard with the Python library. Serializing an object means converting it into a format that can be saved to be de-serialized later to return to its original state. The pickle module is used in this case.

Pickling

In Python, the serialization process is known as pickling. In Python, any object may be serialized as a byte stream and saved as a memory file.

Pickling is a compact process, but pickle items may be further compacted. Pickle also retains track of the serialized objects, which is cross-version portable. Pickle.dump is the function used in operation mentioned above ().

Unpickling

Pickling is the polar opposite of unpickling. After deserializing the byte stream, it loads the object into memory to reconstruct the objects saved in the file. Pickle.load is the function used in operation mentioned above ().

30.   What is PYTHONPATH?

PYTHONPATH is an environment variable that allows you to specify extra directories in which Python will look for modules and packages. This is especially important if you want to keep Python libraries that aren't installed in the global default location.

31.   What are the functions help() and dir() used for?

Python's help() method displays modules, classes, functions, keywords, and other objects. If the help() method is used without an argument, an interactive help utility is opened on the console.

The dir() function attempts to return a correct list of the object's attributes and methods. It reacts differently to various things because it seeks to produce the most relevant data rather than all of the information.

It produces a list of all characteristics included in that module for Modules/Library objects. It returns a list of all acceptable attributes and basic attributes for Class Objects. It produces a list of attributes in the current scope if no arguments are supplied.

32.   How can you tell the difference between.py and.pyc files?

The source code of a program is stored in.py files. Meanwhile, the bytecode of your program is stored in the .pyc file. After compiling the.py file, we obtain bytecode (source code). For some of the files you run, .pyc files are not produced. It's solely there to hold the files you've imported—the python interpreter checks for compiled files before executing a python program. The virtual computer runs the file if it is present, and it looks for a.py file if

it isn't found. It is compiled into a.pyc file and then executed by the Python Virtual Machine if it is discovered. Having a.pyc file saves you time while compiling.

33.   What does the computer interpret in Python?

Python is not an interpreted or compiled language. The implementation's attribute is whether it is interpreted or compiled. Python is a bytecode (a collection of interpreter-readable instructions) that may be interpreted differently. The source code is saved with the extension .py. Python generates a set of instructions for a virtual machine from the source code. The Python interpreter is a virtual machine implementation. "Bytecode" is the name for this intermediate format. The.py source code is initially compiled into bytecode (.pyc). This bytecode can then be interpreted by the standard CPython interpreter or PyPy's JIT (Just in Time compiler).

34.   In Python, how are arguments delivered by value or reference?

Pass by value: The real object is copied and passed. Changing the value of the object's duplicate does not affect the original object's value.

Pass via reference: The real object is supplied as a reference. The value of the old object will change if the value of the new object is changed. Arguments are supplied by reference in Python, which means that a reference to the real object is passed.