Python Interview Questions You Must Prepare In 2023
One of the most popular programming languages currently in use was Python, created by Guido van Rossum and launched in February 1991. Python is utilized by a variety of specialists, including machine learning engineers, data scientists, data analysts, and software developers, because of its easy and clean syntax. It is an open-source language that is free to use and can be picked up quickly, plus it allows for the creation of objects. Web development, graphical user interfaces, web scraping, automation, data science, machine learning, and other fields all make use of Python.
Python programming allows for multiple functions to be accomplished with a little number of lines of code, and it also offers robust computations by way of robust libraries. These reasons contribute to the rising need for Python programmers in the workforce. Take a look at our python training program if you're interested.
If you're looking to obtain a fantastic job, this blog will help you prepare for the most typical Python interview questions.
Interview Questions in Python
We've compiled the most frequently asked interview questions for Python developers. While you may be unfamiliar with the interview process as a beginner, preparing for this interview by studying the answers to these questions will give you the confidence you need to give convincing responses and land your dream job.
1. What exactly is Python?
Guido van Rossum developed Python and offered it to the public for the first time in 1991. It's a high-level language for a wide variety of uses that places a premium on making code comprehensible and provides a straightforward syntax. Because of its ease of use, Python is a popular choice among developers and programmers. Van Rossum left his position as a community leader in 2018 after 30 years.
For a wide variety of OSes, Python interpreters are readily available. As with practically all variants of Python, CPython, the reference version, is open-source software and has a community-based development process. The Python Software Foundation is responsible for maintaining both Python and CPython. Enroll in the Python course in Chennai at SLA and you will be taught all you need to know about Python.
2. What's so great about Python?
As a high-level language, Python can be used for a wide variety of projects. Python is a general-purpose programming language that may be used to make graphical user interface (GUI) programs for the computer, web pages, and web-based software. As a high-level language, Python frees you from repetitive coding tasks so that you can focus on the core functionality of your application. As a result of the programming language's strict grammatical requirements, it is much simpler to keep the codebase readable and the program under control.
3. Can you list some of Python's applications?
Python is well-known for its versatility, as it can be applied to nearly any area of software development. Almost any cutting-edge industry now likely uses Python. Since it is the most widely used programming language, you can use it to make any kind of program you want. Even final-year students are going for Python-related projects as Python projects for final year students are available in huge counts.
Web Apps
Web application development can be accomplished with Python. In addition to HTML and XML, it also supports JSON, email, requests, lovely soup, Feedparser, and other internet protocols. Instagram was developed using the Python web framework Django.
Programming Interfaces for Desktops
One such user interface is the Graphical User Interface (GUI), which simplifies communication with computer programs. The Python programming language includes the Tk graphical user interface framework.
Console Application
Console applications are typically run from the command line, sometimes known as a shell. Instructions are translated into action by means of these computer programs. In earlier computer generations, it was more common to find this kind of program. For command-line programs, its well-known REPL, or Read-Eval-Print Loop, is a must-have.
Numerous open-source tools and modules exist for Python that facilitate the development of CLI programs. The proper IO libraries are called upon for reading and writing. Parameter processing and automatic console help generation are also included. More complex libraries are available for use in developing console apps that can run independently.
Software Engineering
In the world of computer programming, Python is a handy tool. It's a helpful language for setting up management and control, conducting tests, and other tasks.
Constructing reliable controls requires SCons.
Automating continuous compilation and testing with Buildbot and Apache Gumps. - Quantitative and ScientificWe live in the age of artificial intelligence, where machines can perform human-level jobs with little to no human input. Python is a great language to use when developing software that uses AI or machine learning. Numerous scientific and mathematical libraries are included, making even the most complex calculations straightforward to do.
Lots of math is needed for implementing machine learning algorithms. Python's scientific and numerical communities have access to Numpy, Pandas, Scipy, Scikit-learn, and other libraries. Python experts will be able to build upon this code by importing additional libraries. Below are some examples of popular frameworks for machine library libraries.
- SciPy
- Science + Knowledge +
- Understanding
- NumPy
- Pandas
- Matplotlib
- Business Software
Typical apps and enterprise software are two different things. Python's flexibility and clarity are ideal for this kind of program.
Oddo is a Python-based universal program that provides numerous useful features for running a business. Python's Tryton platform is used to create the commercial app.
Video or Audio-Based Applications
Python is a flexible language that may be used to create a wide variety of multimedia programs. Multimedia players like TimPlayer and cplay were developed in Python.
"- 3D CAD Programs"CAD is used for the design of buildings that require an engineering background (Computer-aided design). An element of the system can be visualized in three dimensions using this method.
A 3D computer-aided design program can make use of the following Python capabilities :- Fandango (Popular)
- (Popular)
- CAMVOX
- HeeksCNC
- AnyCAD
- RCAM
Business Software
Creating apps for internal company use is a viable use for Python. A few examples of real-time applications include OpenERP, Tryton, and Piccolo.
Application for Processing Images
Python's library support for image manipulation is extensive. This image can be customized to fit our needs. Some of the image-processing libraries available in Python are OpenCV, Pillow, and SimpleITK. We've covered a lot of ground here, including a wide variety of uses where Python is indispensable. In the subsequent session, we'll go deeper into Python's foundational concepts.
4. How is Python installed?
Click the "Download Anaconda" button on Anaconda.org to begin the installation of Python. Current stable releases of Python are available for download here. The technique is simple after Python is installed. This is followed by firing up an integrated development environment (IDE) and diving into some Python programming. Check out this Python Tutorial if you're interested in learning more about the procedure. Join the Python training in Chennai at SLA which is offered on weekdays and weekends.
5. To what extent does Python's core functionality lie?
Python is widely utilized in the fields of data science, artificial intelligence, and machine learning. Python in data science is inevitable.
Reasons, why Python has become so popular, include :- Python's readable and straightforward syntax makes it a breeze to pick up.
- Since Python's syntax is intuitive, debugging is a breeze.
- Python is an open-source, free programming language. It's useful in a variety of languages.
- Classes and other object-oriented notions can be written in this language.
- It's compatible with a wide variety of languages and can be used in tandem with C++, Java, and more.
7. Exactly what do you mean by "literals" in Python?
An uncomplicated and straightforward way to state a numerical value is through the use of a literal. The primitive type possibilities of a language are reflected in its literals. Common literal types include integers, floating-point numbers, Booleans, and character strings. Python accepts the following literals:
Python literals represent a variable or constant. Python supports a wide variety of literals.
String literals are character strings encapsulated in a code. Single-quote, double-quote, and multi-quote strings are all possible. Character literals are single characters surrounded by single or double quotation marks.
Numeric Literals are fixed values that can be categorized as either integers, floating-point numbers, or complex numbers.
They can be assigned the values True or False, which are the Boolean literals for "1" and "0," respectively.
Special Literals: Classify ungenerated fields with this notation. The value "None" is used to signify it.
- “Halo” and ''12345” are both string literals.
- Calculated values: 0, 1, 2, -1, 2
- Word count: 89675L
- 3.14 in floating literals
- Syntactically complex literals: 12j
- True or False are the boolean literals.
- In terms of literals, there are none that stand out.
- U"hello" is a Unicode literal.
- [], [5, 6, 7] are examples of list literals.
- The tuple literals are (), (9,), (8, 9, 0)
- Definition literals:, 'x':1
- Floating-point literals: "8, 9, 10"
8. To what category does Python belong?
Python is a popular programming language because it is interpreted, interactive, and object-oriented. These include classes, modules, exceptions, dynamic typing, and very advanced dynamic data types.
As a dynamically typed interpreted language, Python is highly flexible. These languages are often referred to as "scripting" languages because the code is not binary. By "dynamically typed," I mean that types are inferred by the interpreter rather than explicitly expressed in the source code.
Python's succinct, easy-to-learn syntax is prioritized for readability, which reduces the cost of software maintenance. Python's module and package systems make it possible to write modular programs and repurpose existing code. Both the source code and binaries of the Python interpreter and its extensive standard library can be obtained for no cost and shared among users of any platform
9. Is Python an interpreted language and how?
To put it simply, an interpreter is a piece of software that reads your program's source code, runs the instructions you give it, creates the variables you tell it to create, and does a lot of other work in the background to make sure everything runs smoothly or alerts you to any problems it encounters.
There is no interpreter or compiler for Python. Whether the implementation is interpreted or compiled is a characteristic of the implementation. Python's bytecode (a set of instructions readable by an interpreter) can be translated in several ways.
A.py file contains the program's source code.
Python takes the source code and turns it into a collection of instructions that can be executed by a virtual machine. Bytecode is the intermediate format generated when.py source code is compiled into.pyc. Then, either the default CPython interpreter or PyPy's JIT can read and understand this bytecode (Just in Time compiler).
Python is considered an interpreted language due to the fact that an interpreter is required in order for your computer to understand the code you've written. Later, when working on a project, you'll need to write Python code and run it on your own computer, so you'll need to download and use the Python interpreter.
10. How do you define pep 8?
Best practices and guidelines for composing Python code are outlined in PEP 8, also known as PEP8 or PEP-8. Authors Guido van Rossum, Barry Warsaw, and Nick Coghlan put it together back in 2001. The primary goal of PEP 8 is to improve the readability and uniformity of Python code.
There are several proposals for improving Python, collectively known as Python Enhancement Proposals (PEP). A Python Enhancement Proposal (PEP) describes proposed changes to Python and provides information to the community about important aspects of the language, such as its design and style.
11. What is the PYTHON PATH?
PYTHONPATH is an environment variable that enables users to add new directories to Python's sys.path directory list. In essence, it is an environment variable that is set prior to the Python interpreter's execution.
12. What in Python is a namespace?
In Python, a namespace is a mechanism that provides each object with a unique name. A variable or method may qualify as an object. The Python namespace is maintained in the form of a Python dictionary. Consider as an illustration the structure of a computer's directory-file system. It goes without saying that a file with the same name may exist in many folders. However, if the file's absolute path is provided, one can be directed to it.
A namespace is a method for assuring that all names in a program are separate and interchangeable. You may already be aware that in Python, everything, including texts, lists, and functions, is an object. In addition, Python employs dictionaries to implement namespaces, which is remarkable. There is a mapping between names and objects, with names serving as keys and objects serving as values. Multiple namespaces may use the same name, each translating it to a separate object. Here are some examples of namespaces:
Local Namespace : This namespace contains local function names. This namespace is formed when a function is called, and it exists only until the function returns.
Global Namespace : This namespace stores names from multiple imported modules that you are utilizing in a project. It begins when the module is introduced to the project and continues until the script is finished.
Built-in Namespace : This namespace holds the names of functions and exceptions that are built into the operating system.
13. What is Flask and what are its benefits?
The open-source web framework Flask. Flask is a collection of tools, frameworks, and technologies enabling the development of web applications. This web application is constructed utilizing a web page, a wiki, a massive web-based calendar application, or a commercial website. Flask is a micro-framework, therefore it does not heavily rely on other libraries.
Benefits:
There are a number of compelling arguments for using Flask as a web application framework. Like :
- Support for unit testing that is incorporated
- There is an integrated development server and a quick debugger.
- Restful request transmission with a Unicode foundation
- The usage of cookies is allowed.
- WSGI 1.0 compliant jinja2 template
- In addition, the flask grants you entire control over the project's development.
- HTTP request handling functionality
- Flask is a flexible and lightweight web framework that can be quickly integrated with a few add-ons.
- You may use your favorite gadget to connect. The primary API for ORM Basic is orderly and well-designed.
- Extremely adaptable
- The construction of the flask is straightforward.
14. What is the difference between local and global variables in Python?
Local variables are declared within a function and have a limited scope, whereas global variables are defined outside of any function and have a global scope. In other words, local variables are only accessible within the function where they were generated, whereas global variables are accessible throughout the entire program and each function.
Local Variables
Local variables are variables that are created and used exclusively within a function. Outside of the function, it is inaccessible.
Global Variables
Global variables are variables that are defined outside of each function and are accessible throughout the entire program, inside and outside of each function.
15. What is a Python module?
A Python module is a single file containing a collection of Python instructions and definitions. You can provide functions, classes, and variables within a module. A module can also include executable code. It is simpler to comprehend and use code that is structured into modules. It also organizes the code logically.
16. Is Django better than Flask?
Django is more popular because it provides abundant functionality out of the box, making the development of complex applications easier. Django is ideally suited for large, feature-rich projects. The capabilities may be excessive for some applications.
If you are new to web programming, Flask is an excellent starting point. Flask-based websites generate substantial traffic, although not as much as Django-based websites. If you desire precise control, you should utilize flask, whereas Django developers rely on a broad community to create unique websites.
17. Describe the distinctions between Django, Pyramid, and Flask.
Flask is a "micro framework" meant for smaller, less demanding applications. Pyramid and Django are both designed for large-scale applications, but their approaches to extensibility and flexibility are distinct.
A pyramid is designed to be adaptable, allowing the developer to utilize the most appropriate tools for the project. This indicates that the developer has the ability to select the database, URL structure, templating style, and other settings. Django aims to provide all of the components that a web application would need, so programmers can just open the box and begin working, incorporating Django's various components as they go.
17. Describe the distinctions between Django, Pyramid, and Flask.Flask is a "micro framework" meant for smaller, less demanding applications. Pyramid and Django are both designed for large-scale applications, but their approaches to extensibility and flexibility are distinct.
A pyramid is designed to be adaptable, allowing the developer to utilize the most appropriate tools for the project. This indicates that the developer has the ability to select the database, URL structure, templating style, and other settings. Django aims to provide all of the components that a web application would need, so programmers can just open the box and begin working, incorporating Django's various components as they go.
Pyramid and Flask give developers freedom over how (and if) their data is saved, whereas Django includes an ORM by default. SQLAlchemy is the most common ORM for non-Django web applications, but there are several alternatives, including DynamoDB, MongoDB, LevelDB, and SQLite. Pyramid is designed to be compatible with any type of persistence layer, including those that have not yet been invented. At SLA, Enrolled students are given extended support towards their placement with Python interview questions.
18. Discuss the architecture of Django
Django's MVC (Model-View-Controller) architecture is composed of three parts: model, view, and controller.
1. ModelDatabases are physical representations of the Model's logical data structure, which forms the basis of the entire program (generally relational databases such as MySql, Postgres).
2. ViewWhen you go to a website on your web browser, the user interface, often known as The View, is what you see on the screen. They are represented by HTML/CSS/Javascript files.
3. ControllerThe Controller mediates between the view and the model and is in charge of feeding information from the model to the view.
Using MVC, your application will spin around the model, exposing or modifying it.
19. Explain Python's Scope
Consider scope as the patriarch of a family; all objects operate within a scope. A formal definition would be that this is a block of code under which objects stay relevant regardless of how many are declared. Several instances of the same are shown below:
Local Scope : When a variable is created within a function, it belongs to the local scope of that function and can only be utilized within it.
Global Scope: A variable is said to be in the global scope when it is formed within the main body of Python code. The finest aspect of global scope is that it is available from any area of Python code, whether global or local.
Internal Function : This is also known as a function inside a function, as shown in the example above in local scope variable y is not available outside the function but within any function inside another function.
Scope at the Module Level: This refers to the globally available objects of the current module. Outermost Scope: This is a list of all the built-in names that can be accessed within the application.
20. List the most frequent built-in data types in Python.
Below are the most frequently utilized built-in datatypes :
Numbers : Consists of integers, complex numbers, and floating-point numbers. Lists: Technically speaking, a list is an ordered sequence of things that are modifiable, and the elements within lists might belong to various data types.
Illustration : list = [911, “SLA Institute”, 100]
Tuples : Like lists, tuples are organized sequences of elements, but once stated, they cannot be modified.
Illustration : tup_1 = (911, “SLA Institute”, 100)
String : The series of characters enclosed in single or double quotations is known as a string.
Illustration : “Hello, I am a software engineer” ‘Hello, I am a software engineer’
Dictionary : A dictionary is a data structure that stores information as a collection of entries (keys) and associated values (values), where entries are retrieved by their respective keys.
Illustration : [12] Manish = {1:’science’, 2:’Maths’, 3:’English’}
Sets : To simplify, sets are groups of distinct objects whose arrangement does not follow any strict rules.
Illustration : set = {1,2,3}
Boolean : Boolean values can only take on two possible forms: True and False
21. How do you differentiate between global, protected, and private attributes in Python?
Attributes of a class can alternatively be referred to as variables. Python variables can have one of three different kinds of access :
- Variables declared public can be accessed from within or outside the class.
- If a variable is defined as private, it can only be accessed by other members of the same class.
- If a variable is declared as protected, it can only be accessed from inside the same package.
- These additional categories describe attributes:
- Local attributes are those that are defined inside a method or code block and may only be accessed from inside that block or method.
- The definition of a global attribute occurs outside of a method or code block, allowing its use everywhere.
m1 = "Toyota" //Global attributes
def price(self):
m2 = "Luxury cars" //Local attributes
return m2
Toy_c = Car()
print(Toy_c.c1)
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22. In Python, what actually are "keywords"?
Python keywords are special terms that can't be used as literals in the code but are instead assigned to variables, functions, or other identifiers. Their contributions to the language definition are crucial in shaping its syntax and grammar.
| False | class | finally | is | return |
|---|---|---|---|---|
| None | continue | for | lambda | try |
| True | def | from | nonlocal | while |
| and | del | global | not | with |
| as | elif | if | or | yield |
| assert | else | import | pass | |
| break | except |
23. In Python, what are functions?
In Python, a function is a block of code that can be reused to carry out a specific task or a group of related tasks. In order to improve modularity in programs that make extensive use of code reuse, functions are crucial. Many useful operations, such as print, are already implemented in Python (). You can even make your own user-defined functions with it.
24. What are the characteristics of Pandas?
For data-intensive tasks, you can rely on the open-source Python package Pandas and its extensive collection of data structures. Pandas, with their unique characteristics, are a perfect fit for any data operation, from research to addressing difficult business challenges. As one of the most useful tools, pandas are versatile and can process many different types of files.
25. What do you mean by data frames?
Among the many pandas data structures, the dataframe is the most flexible and may be modified in various ways. Pandas can handle disparate information that is often presented along two dimensions. (with columns and rows)
26. What does the Pandas Series mean?
The one-dimensional panda data structure is a series, which may store nearly any kind of information. It looks like a column in Excel. It's useful for processing data in a single dimension and supports a variety of operations.
Series generation via data collection:
Code :
import pandas as pd
data=["5",7,"nine",11.0]
series=pd.
Series(data)
print(series)
print(type(series))
27. What understanding do you have of pandas groupby?
Pandas provides a feature called groupby, which may be used to divide an object into subsets. This function, like its SQL, MySQL, and Oracle counterparts, is employed to group data according to classes and entities for the purpose of further aggregation. One or more columns in a dataframe can be used to create groups.
Code :
df=pd.DataFrame({'Gadgets':['SamsungA7','Nokia1100','HP','Asus'],'Type':["Mobile","Mobile","Laptop","Laptop"]})
df To execute groupby, write the below code:
df.groupby('Type').
count()
28. How can you join pandas dataframes together?
Through the use of pandas' concat(), append(), and join() functions, it is possible to horizontally or vertically stack two data frames.
Concat is essentially the vertical stacking of dataframes into a single dataframe and performs better when the data frames contain similar columns and can be employed for concatenation of data having comparable fields.
By calling append(), data frames can be stacked horizontally. The best concatenation function to use when joining two tables (dataframes).
Join is used to combine two or more data frames that share a column or columns. Here, the stacking is laid out on a horizontal plane.
29. What kinds of joins do Pandas provide?
Join operations available in pandas include the left join, inner join, right join, and outer join. Learn Python from SLA via weekdays/weekend classes and improve your skills.
30. In Pandas, how can you merge dataframes?
The fields and datatypes of the input dataframes will determine how they are combined. When merging data, axis 0 is used if there are common fields, while axis 1 is used otherwise.
When working with a dataframe, what is the best way to go to the first five records? The head(5) method allows us to extract the first five records from a dataframe. The first 5 rows are what df.head() returns by default. Use df.head(n) to retrieve the first n rows.
31. How to go to the final five records in a dataframe?
With the tail(5) function, we may extract the five most recent records from a dataframe. In its default implementation, df.tail() only returns the top 5 rows. df.tail(n) will be used to retrieve the final n rows.
32. What is the distinction between tuples and dictionaries?
A tuple is immutable, whereas a dictionary can be changed. If you want to update the definitions in a dictionary, it won't affect the dictionary's identity, but you can't do the same with a tuple.
33. Recursion: what is it and how does it work?
When a function performs a recursion, it makes one or more internal calls to itself. In order to avoid the infinite loop that can occur if a recursive function is utilized in software, it must provide a termination condition.
34. List comprehension in Python: Explain this.
The purpose of list comprehension is to convert one list into another. Each item in the new list can be modified individually and the whole can be rearranged based on preexisting conditions. It's a set of brackets surrounding an expression that evaluates to a for clause. Ace your interview with these Python interview questions and answers.
35. With what resources can static analysis be carried out?
Pychecker and Pylint are two static analysis tools that can be used to detect Python programming errors. Pychecker is a tool for finding programming errors and providing style and complexity warnings in the code. While Pylint verifies that the module adheres to a specific set of rules for programming.
36. In Python, what does pass mean?
Pass is an empty statement that has no effect when executed. A Null statement, to put it another way. The interpreter does not skip over this statement, but it does nothing either. It's used when you need to make a statement but don't want any commands to run.
37. How do you copy an object in Python?
In Python, you can't exactly copy anything, but you can get quite close. The "=" operator allows us to save the value of an object in a variable.
var=copy.copy(obj)38. How is it possible to convert a number to a string?
Numbers can be converted to strings using the built-in str() method.
39. Explain Python's module and package structures.
Software should be structured using modules. There are more properties and objects that can be imported into any given Python file because each file is a module. A program's folder can be thought of as a collection of its program modules. Subfolders or modules can be included in a package. Learn to implement Python programming operations effortlessly by enrolling in SLA.
40. Why does Python have an object() function?
The object() method in Python always produces an empty object. There is no way to extend this object with additional features.
41. What distinguishes NumPy from SciPy?
Both NumPy and SciPy refer to the Python programming language, however, the former is more commonly used in the scientific community. SciPy is used for more advanced tasks like numerical integration and optimization, machine learning, and so on, while NumPy is the primary library for defining arrays and elementary mathematical operations.
42. What is the purpose of the len() function?
The length of a string, list, array, etc. can be calculated with the help of the len() function.
43. Describe the various file processing modes that can be used with Python.
Python supports read-only(r), write-only(w), read-write(rw), and adds processing modes for files (a). To illustrate, let's imagine you're reading a text file in read mode. The aforementioned modes are renamed rt for read-only, wt for write, and so on. In a similar vein, the letter "b" followed by one of the file access flags ( "r", "w", "rw", or "a") allows for the opening of a binary file. The role of Python in data science is inseparable. Hence enroll in a Python course in Chennai and emerge successful.
44. Explain the meaning of the terms "pickling" and "unpickling."
Pickling is the procedure of serializing the hierarchy of Python objects into a byte stream for archival purposes. Another term for this is "serialization." In contrast to pickling, unpickling entails removing the pickling process entirely. The object hierarchy is rebuilt from the byte stream.
45. Can you explain how Python handles memory?
One of the most typical python interview questions is this.
Python's approach to memory management relies on a private heap that stores all objects and data structures. The programmer has no control over the heap because it is managed by the interpreter. All memory allocation in Python is handled by the language's built-in memory manager. In addition, a garbage collector is included for freeing up heap space and reusing memory.
46. How does one define a unittest in Python?
Unittest is a Python framework for unit testing. Test setup and teardown code can be shared, test collections can be created, tests can be automated, and tests can be separated from the reporting framework.
47. To delete a file in Python, what steps must be taken?
Python's built-in os.remove (filename) and os.unlink commands can be used to delete files (filename)
48. Is there a way to make an empty class in Python?
After defining the class object, we can use the pass command to generate an empty class. In Python, a statement with no effect is called a pass.
49. Decorators in Python: What Are They?
Decorators are special functions that can change a function's behavior without altering the original function itself, and they do this by accepting the original function as an argument. These are handy when we need to dynamically extend a function's capabilities without altering the original. Learn python from SLA. Enrolled students are given hands-on training by expert trainers in real-time projects.
50. Can you explain what exactly a dynamically typed language is?
When it comes to programming, fewer type mistakes are always better, thus type checking is essential. Both compile-time and run-time type checking occur for variables. Statically typed languages are those in which type checking occurs during compilation, while dynamically typed languages are those in which it occurs at runtime.
- Objects in dynamically typed languages have their types assigned to them at runtime.
- The code produced by dynamically typed languages is less efficient than that of their statically typed counterparts.
- Variable types do not have to be specified before they are used in dynamically typed languages. This allows for dynamic allocation.
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51. In Python, what is slicing?
In Python, "slicing" means obtaining individual slices of a sequence. The sequence may be any object that can be modified and iterated. Python's slice() method is used to cut a sequence into smaller pieces as specified.
The slice function can be used in two distinct ways. Slicing syntax in Python:
slice(start, stop)
silica(start, stop, step)
52. Python Arrays vs. Lists: What's the Difference?
Python arrays and lists are both mutable ordered collections of elements, but their use is distinct.
When data is imported into an array from the array module, the data is heterogeneous; however, when data is imported into an array from the NumPy module, the data is homogeneous. However, lists are capable of storing disparate types of data and do not require any additional modules to be imported in order to be used.
import array as a5
array5 = a5.array('i', [5, 7,9, 1] )
print (array5)Or ,
import NumPy as a7 array7 = a7.array([5, 4, 6, 2]) print(array7)
Unlike lists, arrays require a declaration before they may be used.
As opposed to lists, arrays facilitate numerical operations more efficiently.
53. What's the dissimilarity between the a.py file and the a.pyc file?
The python interpreter reads in.py files, which contain < Python source code.
The python compiler produces compiled files (.pyc) that are bytecodes, but these files are only generated for built-in modules and files.
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