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Are you preparing for a Python interview? If so, you've come to the right place. Here, you'll find the top Python interview questions that employers commonly ask. We'll review various topics, ranging from beginner to advanced, to help you better understand the subject matter.
Reviewing these Python Tricky Interview Questions before your interview can help increase your confidence and give you an edge in hiring. So, let's dive in and brush up on your Python skills!
Important Python Interview Questions and Answers
What is Python, and what are its key features?
Python is an interpreted, high-level programming language with straightforward and understandable grammar. In 1991, Guido van Rossum produced it, and it became available. Python is widely utilised in many industries, including data research, web development, machine learning, artificial intelligence, and automation.
Python's simplicity and usability are among its most important characteristics. Because of its syntax's simplicity, it's a popular option for novices. Moreover, it contains a big library of modules and packages, which makes it extremely flexible and adaptable for many applications.
Python's platform freedom is another advantage. It doesn't require any code modifications to run on various operating systems, including Windows, macOS, and Linux. As a result, it is very portable and gives programmers the ability to construct cross-platform programmes.
Moreover, Python supports procedural, functional, and object-oriented programming paradigms. Because of this, it is very adaptable and can handle many programming paradigms.
Python's large developer community, which contributes to its development and maintenance, is another key characteristic. Thanks to the community's wealth of resources, guides, and documentation, developers may easily learn and utilise Python.
Additionally, Python has a strong collection of tools and frameworks, like NumPy, Pandas, and Django, which make it a great language for machine learning, web development, and data analysis.
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What is the difference between Python 2. x and Python 3. x?
The two main Python programming versions are Python 2. x and Python 3 x. While there are numerous similarities between the two versions, there are also some significant variances. Python 2. x came out in 2000, while Python 3. x came out in 2008.
The syntax is among the most important differences between Python 2. x and Python 3. x. To increase the uniformity and clarity of the language syntax, Python 3.x made several improvements. For instance, Python 3. x's print function has replaced the print statement from Python 2.x. The print function is much simpler to use in various situations, and more sophisticated output formatting is now possible.
The way Python 2.x and Python 3.x handle Unicode is another significant distinction between them. Python 2.x does not have built-in Unicode support. Thus programmers must utilise specialised libraries and modules to work with Unicode strings. In contrast, Python 3.x has complete Unicode support, simplifying working with characters other than ASCII.
Python 3.x has many performance enhancements. For instance, Python 3.x's interpreter is more effectively tuned for multi-core CPUs, which can result in notable speed improvements in particular applications.
One thing to remember is that Python 3.x is the current and next language version; Python 2.x is no longer actively developed or supported. To ensure that their code will be supported and maintained in the future, developers should start new projects with Python 3. x rather than Python 2.x.
The two main Python programming versions, Python 2.x and Python 3.x, differ significantly in syntax, Unicode support, and performance. To guarantee long-term maintenance and compatibility with the most recent tools and libraries, developers should use Python 3.x for new applications.
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What are the benefits of using Python?
If you want to Learn Python, you need to know about its benefits. Python is a flexible and potent programming language that has become increasingly popular in various industries, including data research, web development, machine learning, and artificial intelligence. Using Python has several advantages, some of which include the following:
- Easy to learn and use: Python is a good language for novices to learn programming since it has straightforward and easy-to-read grammar. Because its syntax resembles English, it is simpler to read and comprehend.
- Huge community and ecosystem: Python has a sizable and vibrant developer community that actively supports and contributes to its growth. Developers may easily learn and use Python because of the variety of materials, modules, and frameworks provided by this community.
- Cross-platform compatibility: Python is portable and versatile since it can be used on various operating systems, including Windows, Linux, and macOS.
- Large module and package library: It has a large module and package library that may be utilised for various applications. These libraries offer a variety of functionalities, including web development, machine learning, data analysis, and more.
- Great for data analysis and visualization: This language has gained popularity for data analysis and visualization because of packages like NumPy, Pandas, and Matplotlib. These libraries give programmers various tools for data analysis and visualization, facilitating the discovery of new information and formulating well-informed judgments.
- Good for automation and scripting: Python is an excellent language for activities requiring automation and scripting. System administrators and DevOps experts frequently use it because it is simple to develop scripts that automate repetitive activities.
What is a decorator in Python, and how does it work?
In Python, a decorator is a special type of function that can be used to modify the behavior of another function. Decorators are defined using the '@' symbol followed by the decorator function's name and placed immediately before the function they are modifying.
Here is an example of a simple decorator that adds a message to the output of a function:
In this example, the 'message_decorator' function is defined to take a function as its argument and return a new function that adds a message before calling the original function. The '@message_decorator' line above the 'hello' function tells Python to apply the 'message_decorator' to the 'hello' function.
When the 'hello' function is called, the modified version of the function is executed since the '@message_decorator' line has caused Python to wrap the 'hello' function inside the 'message_decorator' function.
Decorators can be used for a variety of purposes, such as adding logging or error handling to a function or modifying the behavior of a class or method. Since decorators are just functions, they can be combined with Python features, such as closures and higher-order functions, to create complex behavior.
If you want to Learn Python Programming, you need to know how the decorator works in Python. The interviewee needs to prepare these kinds of Python Interview Questions and Answers to ace the interview round.
How does garbage collection work in Python?
To Learn Python you must know how garbage collection works. Garbage collection is a process of automatically freeing up memory that is no longer needed by a program. Python has an automatic garbage collection mechanism that handles the memory management for the programmer. This means the programmer does not have to manually allocate and deallocate memory.
In Python, garbage collection works by using a reference counting mechanism. Whenever an object is created, Python assigns it a reference count of 1. Every time a new reference to the object is created, the reference count is increased by 1. When a reference to an object is deleted or goes out of scope, the reference count is decreased by 1. When an object's reference count reaches 0, there are no more references to the object, which is considered garbage. The memory occupied by the garbage object is then freed up for future use.
However, there are some cases where the reference counting mechanism alone cannot handle garbage collection. In such cases, Python uses a technique called cyclic garbage collection. This technique identifies and removes circular references between objects that are no longer in use. Circular references occur when two or more objects reference each other, forming a loop. In such cases, the reference counting mechanism would be unable to detect the objects that are no longer in use, and cyclic garbage collection is used to break the loop and free up the memory.
Python's garbage collection mechanism runs automatically in the background and does not require any intervention from the programmer. However, programmers need to be aware of memory usage in their programs and avoid creating circular references or large numbers of unused objects, which can cause performance issues.
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What is PEP 8, and why is it important?
There are many important questions to prepare for the interview, however, this can be the most important Python Interview Questions for Freshers. PEP 8 stands for Python Enhancement Proposal 8, a document outlining the coding style guidelines for writing Python code. It provides recommendations on how to write readable, maintainable, and consistent Python code.
PEP 8 covers various topics, including naming conventions, indentation, whitespace, and comments. The document provides detailed guidelines for each of these topics, which help to ensure that the code written by different programmers is consistent and easy to read.
One of the main reasons why PEP 8 is important is that it makes code easier to read and understand. When code follows a consistent style, it is easier to understand what the code is doing and how it works. This is especially important for larger projects with multiple programmers working on the same codebase. When everyone follows the same style, it is easier for programmers to understand each other's code and make changes or additions to the codebase.
Another benefit of following PEP 8 is that it can improve code quality. Following the guidelines outlined in PEP 8 allows programmers to write more organised and easier-to-maintain code. This can lead to fewer bugs and a more stable codebase.
Additionally, PEP 8 is important because it is widely recognised and used in the Python community. Many Python libraries and frameworks follow PEP 8 guidelines, so by following these guidelines, programmers can ensure that their code is consistent with the rest of the Python ecosystem.
Following these guidelines allows programmers to write more readable, maintainable, and consistent code, leading to higher code quality, fewer bugs, and a more stable codebase.
What is a Python module, and how do you import it?
In Python, a module is a file containing Python definitions and statements that can be used in other Python programs. Modules allow code reuse, organisation, and abstraction, making maintaining and developing larger projects easier.
Python has many built-in modules, such as 'os', 'sys', 'math', 'datetime', and many others. In addition to these built-in modules, you can also create your custom modules by defining functions, classes, and variables in a Python file with a '.py' extension.
To use a module in a Python program, you need to import it using the 'import' statement. Here is an example of importing the 'math' module:
The 'import' statement imports the 'math' module in this example. The 'math.sqrt' function is then called to calculate the square root of 25, and the result is printed to the console.
Using the' from' keyword, you can also import specific functions or variables from a module. Here is an example of importing the 'sqrt' function from the 'math' module:
In this example, the 'from' keyword imports the 'sqrt' function from the 'math' module. The 'sqrt' function is called directly without the 'math.' prefix.
When importing, you can also use an alias to rename a module or function. Here is an example of importing the 'math' module with an alias:
In this example, the 'as' keyword creates an alias 'm' for the 'math' module. The 'm.sqrt' function is then called to calculate the square root of 25, and the result is printed to the console.
Read More: Why Learn Python: Advantages | Python Course
This is one of the important and Tricky Python Interview Questions so make sure to prepare before your interview.
What is the difference between a list and a tuple in Python?
In Python, a list and a tuple are data structures that allow you to store a collection of items. However, there are some important differences between the two.
One of the main differences between a list and a tuple is that a list is mutable, while a tuple is immutable. This means that you can change the contents of a list by adding, removing, or modifying elements, while the contents of a tuple cannot be changed once it is created.
Another difference between a list and a tuple is how they are defined. A list uses square brackets , while a tuple is defined using parentheses (). For example, a list of numbers can be defined as [1, 2, 3], while a tuple of numbers can be defined as (1, 2, 3).
Lists and tuples also have different methods available to them. For example, a list has methods such as append(), remove(), and sort(), which allow you to add or remove elements from the list and sort the elements in ascending or descending order. On the other hand, a tuple has fewer methods available since its contents cannot be modified. However, it does have methods such as index() and count(), which allow you to search for elements in the tuple.
When it comes to performance, tuples are generally faster than lists. This is because tuples are immutable and can be optimised by the interpreter in ways that lists cannot. However, the difference in performance is usually small and may not be noticeable in most cases.
In summary, the main differences between a list and a tuple in Python are that a list is mutable, while a tuple is immutable, and that they are defined using different syntax. Lists have more methods for modifying their contents, while tuples are generally faster and have fewer methods available.
What is the difference between Python's range() and xrange() functions?
In Python 2. x, there are two functions for generating a range of numbers: range() and xrange(). While they both produce the same output, there are some differences in how they work.
The range() function returns a list of numbers within a specified range and takes up memory to store the entire list. For example, range(0, 10) would return [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]. Since range() returns a list, it can be indexed and sliced like any other list in Python
On the other hand, the xrange() function returns a generator object that generates numbers on the fly without storing the entire list in memory. This means that it is more memory efficient than range(). For example, xrange(0, 10) would generate the numbers 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9, as needed.
One consequence of this difference is that xrange() can be used for generating very large ranges since it doesn't require a lot of memory. In contrast, using range() with a large range could lead to running out of memory.
Another difference between range() and xrange() is that xrange() can only be used in loops, while range() can be used in loops and also in other contexts where a list is needed. For example, if you want to create a list of numbers, use range().
In Python 3. x, the xrange() function has been removed, and the range() function now behaves like xrange() in Python 2. x. This means that range() now returns a generator object instead of a list, making it more memory efficient and suitable for generating large ranges.
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What is a generator in Python, and how is it different from a function?
A generator in Python is a unique function that may be used to instantly generate a sequence of values. Generators can be halted and resumed, allowing them to generate a series of values over time, in contrast to normal functions that run, return, and terminate.
Instead of the "return" keyword, the "yield" keyword defines generators. When a generator function is used, it returns a generator object, which can then be used with the "next()" function to produce values. The generator function runs each time "next()" is called until it comes across a "yield" statement, at which point it pauses and returns the value indicated in the "yield" statement. The generator function continues to run until it encounters another "yield" statement or reaches the end of the function when "next()" is invoked once more.
As generators generate values on the fly rather than holding them in memory, memory efficiency is one of their key advantages. This is very helpful for processing large data sets or when creating long sequences of numbers.
The ability to implement iterators, objects that may be used to iterate through a sequence of values, is another advantage of generators. Iterators are very helpful when working with huge data sets as they enable you to process the data one value at a time rather than loading the entire data set into memory simultaneously.
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Python Interview Questions For 2 Years Of Experience
Here are python interview questions for candidates with 2 years of experience or more:
What is a lambda function in Python, and how is it used?
In Python, a lambda function is a small, anonymous function that can be defined on a single line. Unlike regular functions, which are defined using the 'def' keyword and have multiple lines of code, lambda functions are defined using the 'lambda' keyword and can only contain a single expression.
Here is an example of a simple lambda function that adds two numbers:
In this example, the 'lambda' keyword defines a new function that takes two arguments ('x' and 'y') and returns their sum. The resulting lambda function is then assigned to the add variable and can be called just like a regular function.
Lambda functions are often used when a small, simple function is needed, such as when passing a function as an argument to another function or defining a function inline. Here is an example of using a lambda function as an argument to the built-in 'map' function:
In this example, the 'map' function is used to apply a lambda function that calculates the square of a number to each element of the 'numbers' list. The resulting squares are then collected into a new list using the built-in 'list' function.
Lambda functions can also be used with Python features, such as closures and higher-order functions, to create more complex behavior. However, because lambda functions are limited to a single expression, they are unsuitable for more complex tasks requiring multiple lines of code or complex logic.
What is the difference between a local and a global variable in Python?
In Python, a local variable is a variable that is defined inside a function and can only be accessed within that function. Once the function has completed execution, the local variable is no longer accessible.
On the other hand, a global variable is a variable that is defined outside of any function and can be accessed from anywhere in the program. Global variables remain in memory for the entire lifetime of the program, so they can be accessed and modified from any function or module.
Using global and local variables appropriately is important to ensure that your code is organised and easy to understand. Overusing global variables can make it difficult to track changes to the variable's value, while overusing local variables can lead to unnecessary code duplication.
How can you handle errors and exceptions in Python?
In Python, errors and exceptions are handled using try-except blocks. The try block contains the code that may raise an exception, and the except block handles the exception that is raised.
Here's an example of a try-except block
In this example, ExceptionType is the type of exception that you want to handle. If an exception of that type is raised in the try block, the code in the except block will be executed.
You can also include an else block after the except block, which will be executed if no exceptions are raised in the try block. Finally, you can include a final block, which will be executed regardless of whether an exception was raised.
By handling errors and exceptions in your Python code, you can prevent your program from crashing and make it more robust and reliable.
What is the use of the init method in Python?
In Python, the __init__ method is a special method called when a class instance is created. It is used to initialize the object's attributes and set their initial values. The __init__ method is commonly used to define instance variables and assign them values based on the arguments passed to the class constructor. Using the __init__ method, you can ensure that the object is properly initialized when it is created, preventing errors and making your code more robust.
This is one of the important Python Interview Questions For Freshers that you should be sure to practice before your interview.
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What is the difference between deep copy and shallow copy in Python?
In Python, a deep copy and a shallow copy are two ways to create a copy of an object. A shallow copy creates a new object that points to the same memory locations as the original object, while a deep copy creates a new object with its memory locations that are not shared with the original object.
A shallow copy is created using the copy method or the slice operator ([:]). It is useful when you want to create a copy of an object but don't want to duplicate the memory the object uses. However, modifying the original object will also reflect the changes in the shallow copy.
A deep copy is created using the deep copy method from the copy module. It creates a completely new object that is independent of the original object. This is useful when you want to create a copy of an object that you can modify without affecting the original object. However, deep copying can be slower and more memory-intensive than shallow copying.
What is the difference between Python's map() and filter() functions?
The map() and filter() functions are built-in functions in Python that apply a function to a sequence of elements. The main difference between the two functions is that map() applies a function to each element of a sequence and returns a new sequence containing the results, while filter() applies a function to each element of a sequence and returns a new sequence containing only the elements for which the function returns True.
What is the purpose of the dir() function in Python?
The dir() function in Python is used to get an object's list of attributes and methods. When called without arguments, dir() returns a list of the names in the current local scope. When called with an object as an argument, dir() returns a list of the attributes and methods of that object. This can be useful for exploring the properties and capabilities of an object in Python.
What is the difference between the is and == operators in Python?
The operator in Python tests if two variables refer to the same object in memory, while the == operator tests if two variables have the same value. In other words, it checks for object identity, while == checks for object equality. For example:
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