Top 40 Python Programming Interview Question And Answers

Safalta Expert Published by: Aryan Rana Updated Mon, 26 Sep 2022 11:52 PM IST

Highlights

Python is a high-level, all-purpose programming language. Its design philosophy places a strong emphasis on code readability through the usage of substantial indentation. Python uses dynamic typing and trash collection. It supports a variety of programming paradigms, including functional, object-oriented, and structured programming.

One of the most used programming languages nowadays is Python. This object-oriented language is used by significant corporations throughout the globe to create programmes and applications. You will find some of the most frequently asked questions about Python in various fields in this article. You can prepare for interviews by using our list of Python interview questions for both experienced and new developers. Here are some of the most well-known and important Python programming interview questions and answers.  If you are interested in Digital Marketing or Graphic Designing and want to learn these interesting courses then click on the links mentioned Digital Marketing Course and Graphic Designing course

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Top Python Programming Question And Answers

 

1. What is Python?


Python is a powerful, interactive, object-oriented, and interpreted scripting language.

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It commonly makes use of English keywords. Python has fewer syntactic constructions than other languages, which also employ punctuation.
Python is made to be very legible and works on a variety of operating systems, including Mac, Windows, Linux, Raspberry Pi, etc.

2. Python is an interpreted language. Explain.


Any programming language that runs its statements line by line is said to be an interpreted language. Python programmes don't require a compilation phase before running because they can be run directly from the source code.

3. What are Python's main characteristics?


These are Python's main characteristics:
  • In contrast to languages like C, Python is an interpreted language, which means it doesn't require compilation before execution.
  • There is no need to declare a variable with the data type because Python is dynamically typed. The value of the variable will be used by the Python interpreter to determine the data type.
  • The one exception to Python's object-oriented programming paradigm is the presence of access specifiers. Python has classes, inheritance, and many other common OOPs principles in addition to access specifiers (public and private keywords).
  • A Python programme created on a Windows machine will also run on a Linux system with little to no adjustments because Python is a cross-platform language.
  • Python is a general-purpose language in the truest sense of the word; as such, it is used in a wide range of fields including web application development, automation, data science, machine learning, and more.
 

4. How does Python manage its memory?


Python's private heap space is responsible for managing memory. There is a private heap where all Python objects and data structures are kept. A programmer does not have access to this private heap; it is maintained by the Python Interpreter itself.
The Python private heap area is allocated by the Python memory manager.
The built-in Python garbage collector, which recycles and frees up all the unneeded memory, provides memory for the private heap space.

5. Describe PythonPATH.


PYTHONPATH performs a similar function as PATH. This variable instructs the Python Interpreter where to find the imported module files. Both the directories containing Python source code and the Python source library should be included. The Python Installer will occasionally set PYTHONPATH.

6. How do Python Modules work?


Python Modules are the name given to files that contain Python code. The programmer is given the predefined features when they are required by this code, which can either be classes, functions, or variables. It is an executable code file with the ".py" extension.

Following is a list of frequently used constructed modules:

os
sys
data time
math
random
JSON

7. What are namespaces in Python?


A Python namespace makes guarantees that object names are distinct and can be used without any conflicts in a programme. These namespaces are implemented in Python as dictionaries with each name as a key and a corresponding object as the value.

Let's look at some namespace examples:
  • Within a function, local names make up the local namespace. When a function is called, it is momentarily formed, and after the function completes, it is cleared.
  • The names used in the ongoing project's global namespace come from several imported modules and packages. It is produced as soon as the package is imported into the script and remains till the script is executed.
  • Built-in Namespace consists of built-in Python core functions and names specifically designated for different kinds of exceptions.
 

8. Give an example of inheritance in Python?


Classes in Python have the ability to inherit the properties of another class because it adheres to the object-oriented programming paradigm. Inheritance is the name given to this process. The feature of code reuse is provided by inheritance. Inheriting classes are referred to be superclasses or parent classes, while classes that do so are referred to as derived or child classes. Python supports the following types of inheritance:
  • When a class only inherits from one superclass, this is known as single inheritance.
  • When a class inherits from numerous superclasses, this is known as multiple inheritances.
  • Multiple-level inheritance When a class derives from a superclass and another class derives from this derived class, this creates a "parent, child, and grandchild" class structure.
  • When several derived classes inherit from a single superclass, this is known as hierarchy inheritance.
 

9. Definition of scope resolution


In Python, a scope is a section of code where an object is still applicable. Every single Python object has a certain scope within which it operates. Although namespaces are used to uniquely identify each object in a programme, these namespaces also have a defined scope where their objects may be used without a prefix. It establishes how long a variable is accessible.

Let's look at the scope that was established at the moment the code was executed:

  • The local objects present in the current function are referred to as being in a local scope.
  • The items that are accessible throughout code execution are referred to as being in the global scope.
  • The global objects connected to the program's current module are referred to as a module-level scope.
  • All of the built-in names that can be called by the programme are referred to as being in an outermost scope.
 


10. What does a Python dictionary mean?


One of the supported data types in Python is the dictionary. It consists of an unorganised group of components. Dictionary elements are saved as key-value pairs. Keys are used to index dictionaries.

For instance, the dictionary named "dict" is shown below. India and New Delhi are the values for the two keys Country and Capital, respectively.

11. What are the typical Python built-in data types?


Python is compatible with the following built-in data types:

Types of immutable data
  • Number
  • String
  • Number

Types of mutable data
  • List
  • Dictionary
  • List
 

 


12. What do Python's local and global variables mean?


Local Variable: Any variable that is declared inside of a function is referred to as a "local variable," and its accessibility is restricted to that function only.

Global Variable: Any variable defined outside of a function is referred to as a global variable, and any function found throughout the programme can simply access it.

13. What does Python's type conversion mean?


Python offers you the much-needed type conversion capability, which allows you to change one data type into the one you require.

The types of type conversion include:

1. Implicit Sort Conversion: In this type of type conversion, the Python interpreter assists in automatically transforming one data type into another data type without the involvement of the user.

2. Explicit Type Conversion: In this type of type conversion, the user explicitly changes the data type into the desired type.

14. Is case sensitive in Python?


Yes, the case-sensitive nature of Python is true. This indicates that, unlike SQL and Pascal, Python's function and functions are distinct from one another.

15. What are packages in Python?


A Python package is a grouping of many sub-packages and modules depending on how similar their functions are.

16. What do Python decorators do?


To add functionality to an existing function in Python without altering the function's structure, decorators are essential functions. These are called bottom-up and are represented in Python by the @decorator name.

17. Does Python require indentation?


Python requires indentation as a part of its syntax, and it is required.

The scope and size of the block of codes can be defined in some way in all programming languages. It is the indentation in Python. Because it makes the code easier to read, Python presumably made indentation a requirement.

18. How do pass, continue, and break operate?


These statements are referred to as loop control statements since they assist in altering the execution phase relative to the usual flow.

Python break: This command helps end a loop or a statement and transfer control to the following command.

Python continue: When a particular condition is met, this statement forces the execution of the following iteration rather than terminating it.

Python pass: This statement aids in the syntactic construction of the code and seeks to forego execution. Given that nothing happens when you execute the pass statement, it is also regarded as a null operation.

19. In Python, how can you remark over many lines?


In Python, each line must be preceded by a # before adding a remark that spans multiple lines.

20. Which category does Python fall under? Do you programme or script?


Scripting is another function that Python can accomplish, making it a general-purpose programming language.

21. What do the split(), sub(), and subn() methods of the Python "re" module mean?


These methods are used to alter strings and are a part of the Python RegEx or "re" package.

Splitting a string into a list is done using the split() method.
Substrings that match a regex pattern are found using the sub() method, which then substitutes the matching substring with a different string.
Unlike the sub() method, which only produces the new string, subn() also returns the number of replacements.


22. What does Python's map function do?


Python's map() function takes two arguments: iterable and function. A function is supplied to the map() method as an argument, and that function is subsequently applied to all of the iterable's elements. An object list of results is returned.

23. What do Python generators do?


The function that returns an iterable set of things is referred to as a generator.

24. What are iterators in Python?


These are the specific things that can be quickly navigated and iterated over as necessary.

25. Do Python variables need to be declared with their data types?


No. The Python Interpreter automatically determines the data type of a variable based on the kind of value that has been assigned to it because Python is a dynamically typed language.

26. What are comprehensions for dicta and lists?


Similar to decorators, Python comprehensions assist in creating modified and filtered lists, dictionaries, or sets from a given list, dictionary, or set. Time and potentially far more complex and time-consuming code are saved by comprehension.

The following scenarios call for comprehension:
  • Putting all the list's items via a mathematical operation
  • Operating on the entire list with conditional filtering
  • Combining several lists into a single one
  • A multidimensional list being flattened
 

27. How should comments be written in Python?


Python comments are statements that programmers use to make their code easier to read. You can define a single comment with the help of #, and you can also comment using docstrings (strings enclosed within triple quotes).

28. Is Python capable of supporting multiple inheritances?


Yes, Python offers users a wide range of support for inheritance and its application, unlike Java. When a class is created from more than one distinct parent class, this is referred to as multiple inheritances. Users benefit from a lot of functionality and advantages thanks to this.

29. What are pickling and unpickling?


The Python object is accepted by the Pickle module, which then uses the dump method to transform it into a string representation and store it in a file. Pickling is the name of this procedure. On the other side, unpickling refers to the act of obtaining the original Python objects from the string representation.

30. Is Python entirely object-oriented?


With the exception of access specifiers, Python does adhere to the paradigm of object-oriented programming and includes all of the fundamental OOPs principles, including inheritance, polymorphism, and more. Python does not allow robust encapsulation (adding a private keyword before data members). However, it provides a standard that can be used to hide data, namely prefixing a data member with two underscores.

31. What do Python modules that deal with files do? Can you list some Python modules that deal with files?


Python has a few file-related modules with tools for working with both text and binary files in a file system. These modules provide for the creation of the text or binary files, as well as for updating, copying, deleting, and other operations.

os, os. path, and shutil.os are a few modules that deal with files. While the shutil.os module can be used to copy or remove files, the os. path module includes functions for accessing the file system.

32. What do the operators is, not, and in do?


The term "operator" refers to a specific function that accepts one or more operands (values) and returns a related result.

is: returns true when both operands are true (for instance, "x" is "x").
not: based on the operands, this function returns the opposite of the boolean value (for instance, "1" returns "0" and vice versa).
Assists in determining whether an element is present in a given Sequence.

33. Why isn't all the RAM released when Python exits?

  • Particularly those Python modules that have circular references to other objects or that reference objects from the global namespaces are not always de-allocated or deleted when Python quits.
  • De-allocating the memory blocks that the C library has reserved is not possible.
  • Python would attempt to de-allocate every object upon exit due to its own effective cleanup mechanism.
 


34. Why would you use Python's NumPy arrays over lists?


Users benefit from three key advantages of NumPy arrays, as seen below:
  • Because NumPy arrays use much less memory, the code is more effective.
  • NumPy arrays run more quickly and don't require a lot of runtime processing.
  • Because of its very legible syntax, NumPy is simple and practical for programmers.
 

35. What does Python's polymorphism mean?


The capacity of the code to take various forms is known as polymorphism. Consider the scenario where the parent class has a method called XYZ and the child class is able to have a method with the same name but different variables and parameters.

36. What does Python's encapsulation mean?


In Python, the term "encapsulation" refers to the act of combining various functions and variables into a single unit.

The best illustration of encapsulation in Python is the class.

37. What benefits do NumPy arrays have over Python (nested) lists?


Nested Lists:
  • Python lists are effective general-purpose data structures that allow insertion, appending, deletion, and concatenation operations.
  • Because lists might include objects of different types, they are not compatible with "vectorized" operations like element-wise addition and multiplication. As a result, Python must maintain type information for each element and execute type dispatching code when operating on each element.

Numpy:

NumPy is more convenient and efficient since it offers many free vector and matrix operations, reducing the need for extra effort and the complexity of the code.

In addition, Numpy is more effectively implemented than nested
Faster and with many built-in functions, NumPy arrays are useful for FFTs, convolutions, quick searching, linear algebra, fundamental statistics, histograms, etc.

38. What distinguishes the techniques append() and extend() from one another?
 

  • The methods extend() and append() are both used to add elements to the end of a list.
  • Appends the specified element to the end of the list that is called append(); extends (another-list): Extends the list by the specified amount. increases the length of the list that invoked this extend() method by adding the members of another list.
 


39. How is Python's multithreading implemented?


Python provides a multi-threading package, however, using it is typically not regarded as best practice because it will lengthen the time it takes for code to execute.
  • The Global Interpreter Lock function Object() { [native code] } in Python (GIL). Your only "thread" can execute at a time because of the GIL. The procedure ensures that a thread obtains the GIL, performs some work, and then transfers the GIL to the following thread.
  • Because of how quickly this occurs, it appears to the human eye that your threads are working in parallel, but in reality, they are only switching off using the same CPU core to execute one at a time.
 


40. What is slicing in Python?


Slicing is a method for choosing a variety of sequence data types, such as list, text, and tuple. Slices make it simple and advantageous to separate the ingredients. It needs a: (colon) to demarcate the field's start index and end index. each form of data sequence We are able to utilise slicing to obtain the required elements from a list or tuple. Even while we can obtain items by supplying an index, doing so yields just a single element; however, by utilising slicing, we can obtain a collection or suitable range of the required elements.

 

 

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