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TOP 10 HIDDEN PYTHON FEATURES TO LEARN AS A BEGINNER IN 2023

As a programmer in the cutting edge tech industry, you need to get the hang of programming dialects that have magnificent in-fabricated includes and can assist with fostering your applications rapidly. Not just that, the language must be not difficult to learn and ought to likewise uphold the designer local area. 

Thus, inferable from these offices most software engineers, coders, and designers have begun utilising the Python programming language. Python is one of the most broadly utilized and pursued programming dialects in the business. 

There are numerous Python highlights one requirement to figure out how to turn into a decent designer. Here are the main 10 secret python elements to advance as a novice in 2023.

Pandas_ml

Pandas is one of the most eminent AI libraries that is written in Python. This library is intended for information investigation and control. Besides, it is helpful as it consolidates probably awesome and most dependable libraries of Python into one entire bundle. Hence, making it simple to utilize and carry out.

Contention Unpacking

List unloading fizzles when utilized on a capability; Python doesn't locally unload a rundown or a tuple when it is passed to a capability. This is on the grounds that it might cause uncertainty: it really depends on the designer to determine when this must be finished.

Arranging of code utilising Black

Imagine a scenario in which you have an opportunity to survey your code quicker and in an effective manner. That is when Black - a programmed code formatter for Python, steps in. One of the coolest Python obscure stunts is the formatters wherein you compose code according to your style. Also, Black configurations it, in this manner assisting the entire course of code audit.

Simple Value Swapping

Developers utilize trading of upsides of two factors in their ordinary programming lives. Trading of upsides of two factors is typically finished with the help of a third factor, a brief variable. This third factor permits the trading of the other two factors. In the interim, Python empowers software engineers to direct trading with practically no temp variable.

Python debugger

A few times, we stall out on certain blunders and apply broad time tackling them. Further, turning every one of our endeavors pointless. The arrangement is obscure, however our endeavors are completely squandered. What's more, that is the point at which you should utilize The Python debugger to simplify your errand. The Python Debugger assists with surveying the code line by line with next to no problem. In this way, making it one of the most amazing elements of Python.

Send off Web Server

Secret highlights of Python are pretty much as valuable as the help of a site. For instance, we should envision that you plan to send off your web server utilizing which you will actually want to share the records from your PC. For this, utilizing a basic order would be convenient. It will send off a web server on any port. For all that to pass effectively, you ought to set the port from range 0 to 65353. There are different boundaries that you can look at also.

Negative ordering

One of the secret Python highlights is that it upholds negative ordering. We as a whole know that files are utilized in clusters in all programming dialects. Also, the components in an exhibit are gotten to utilizing these files. Be that as it may, all programming dialects discourage from utilizing negative files like '- 2.' But Python is a special case as it upholds the negative ordering of exhibits. Thus, - 1 ought to be the last component, and - 2 the second last one.

Short Module Names

Could it be said that you are fed up with using long library names again and again? Then, at that point, the accompanying hack will be useful for you without a doubt. Python gives engineers the amazing chance to use the watchword "as" to make any library name as indicated by them.

Utilization of emoticons in code

Did you had at least some idea that Python has a bundle wherein you can add emoticons to your strings? Indeed, you read it right! You may either utilize the emoticon module or Unicode to incorporate emoticons.

Rundown of Easter Eggs

Programming might seem exhausting as composing contentious expositions and different scholastic tasks for certain individuals. In any case, imagine a scenario where we express that there are Easter eggs in Python. This might sound astonishing in light of the fact that Easter eggs, generally speaking, can be found in computer games, films, kid's shows, and so forth.

Conclusion

If you want to be a successful Python developer in 2023, make sure you learn these 10 hidden features of the language. While some of them may seem simple, they will help make your code more efficient and error-free. And if you’re looking for a comprehensive guide on how to learn Python, check out our best python course in bangalore. With our expert instructors and hands-on exercises, you’ll be able to master this powerful programming language in no time!

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TIPS TO WRITE BEST PYTHON CODE AS A DATA SCIENTIST

Coding in Python is amusing due to the fact there are numerous extraordinary approaches to perform the equal functionalities

Programmers and builders can faucet into a number of languages to construct applications, websites, and different programs.

Ultimately, their desired language will turn out to be being the only with which they may be the maximum cushty and the only that receives the task performed maximum effectively.

But one unique language rising on the pinnacle, amongst all of the new programming languages, is Python.

The listing of programming languages is so big that it's miles herbal for human beings to have a huge sort of evaluation about which one is probably the most pleasant.

Yet, Python stays on the pinnacle.

It is absolutely taken into consideration as one of the pinnacle programming languages on the equal stage as JavaScript or C++, and it's also one of the maximum-used languages through agencies and enterprises.

Here are the pinnacle 10 suggestions with a purpose to write the pleasant Python code as a Data Scientist.

Code Everyday

Consistency could be very vital whilst you are mastering a brand new language.

It is usually recommended to make a dedication to code each day.

It can be tough to believe, however muscle reminiscence performs a massive element in programming.

Committing to coding each day will simply assist broaden that muscle reminiscence.

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Write It Out

As you development to your adventure as a brand new programmer, you could surprise in case you ought to be taking notes.

Yes, you ought to!

In fact, studies shows that taking notes through hand is maximum useful for long-time period retention.

This may be particularly useful for the ones operating closer to the purpose of turning into a full-time developer, as many interviews will contain writing code on a whiteboard.

Go Interactive!

Whether you're mastering approximately fundamental Python statistics structures (strings, lists, dictionaries, etc.) for the primary time, or you're debugging an application, the interactive Python shell may be one in every of your pleasant mastering tools.

Take Breaks

Breaks are particularly vital whilst you are debugging.

If you hit a trojan horse and can`t pretty discern out what goes wrong, take a ruin.

Step farfar from your computer, cross for a walk, or chat with a friend.

In programming, your code have to comply with the guidelines of a language and good judgment exactly, so even lacking a citation mark will ruin everything.

Fresh eyes make a huge difference.

Become a Bug Bounty Hunter

When debugging, it's miles vital to have a methodological technique that will help you discover in which matters are breaking down.

Going via your code withinside the order wherein it's miles accomplished and ensuring every element works is a terrific manner to do this.

Work Together

Though coding might also additionally appear to be a solitary activity, it truly works pleasant whilst you paintings together.

It is extraordinarily vital whilst you are mastering to code in Python which you surround your self with different those who are mastering as well.

This will let you percentage the suggestions and hints you examine alongside the manner.

Teach

There are many approaches to do this: whiteboarding with different Python lovers, writing weblog posts explaining newly discovered concepts, recording films in that you give an explanation for some thing you discovered, or sincerely speaking to your self at your computer.

Each of those techniques will solidify your information as

Pair Program

Pair programming has many benefits: it offers you a threat to now no longer most effective have a person assessment your code, however additionally see how a person else is probably considering a problem.

Being uncovered to a couple of thoughts and approaches of questioning will assist you in problem-fixing whilst you bought again to coding for your own.

Build Something, Anything

What you construct isn't as crucial as the way you construct it.

The adventure of the constructing is sincerely what's going to educate you the most.

You can most effective analyze a lot from studying Real Python articles and courses.

Most of your studying will come from the use of Python to construct something.

The troubles you may resolve will educate you a lot.

Contribute to Open Source In the open-supply model, software program supply code is to be had publicly, and everybody can collaborate.

There are many Python libraries which can be open-supply initiatives and make contributions.

Additionally, many corporations post open-supply initiatives.

This manner you may paint with code written and produced via way of means of the engineers operating in those corporations.

Conclusion

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Top 10 VS Code Extensions for Python Developers to Learn In 2023

VS code does now no longer at once assist Python. Therefore, we want VS code extensions for python

VS code (Visual Studio Code) is an open-supply and cross-platform supply-code editor. It became ranked the maximum famous improvement device withinside the Stack Overflow 2021 Developer Survey. 

Python is one of the pinnacle programming languages utilised by builders global for growing quite a few programs, from easy programs to clinical programs. But VS code does now no longer at once assist Python. 

Therefore, we want VS code extensions for python. Fear now no longer- there are numerous alternatives available. However, the largest undertaking is to discover the maximum entire and appropriate ones to your requirements. This article functions as the pinnacle 10 VS Code Extensions for Python Developers to study in 2023.

Kite AutoComplete AI Code

Kite is a development withinside the route of ease of writing code. It is primarily based totally on AI which permits writing the code fast in VS Code. When you name a function, Kite will display the arguments required to name it. 

If you hover on any symbol, it's going to display a precis of it. It offers brief auto-crowning glory and suggests the proper component at a time. It may be used for special programming languages aside from Python consisting of JavaScript, Go, etc. Best VS Code Extensions for Python.

Python

If you figure with Python, you want this extension. VS Code helps Python out of the box, however this extension takes it to an entire new level, a lot in order that VS Code will propose you put in the extension whilst you open a Python file. 

It is one of the pinnacle 10 VS Code Extensions for Python Developers to study in 2023.

Pylance

Pylance is an exceedingly rapid static kind checker and provides complete feature-wealthy language assist for Python. Basically, that is the authentic successor to the Pyright extension that were given launched lower back in 2019. Best VS Code Extensions for Python.

Lightrun

Ligtrun is a real-time debugging platform that helps programs written in numerous languages, together with Python. It includes an intuitive interface in order to upload logs, traces, and metrics in real-time for debugging the code in production. 

You can upload Lightrun snapshots in real-time and on-call so you can debug via means of exploring your stack hint and variables. It is one of the pinnacle 10 VS Code Extensions for Python Developers to study in 2023.

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AREPL

It offers code assessment in real-time. As you begin typing, the code will hold on strolling which allows you to test if the code is proper or no longer in the course of the time of writing itself. 

If a mistake is found, it will likely be contemplated withinside the editor right away with logs. It may be configured for this reason to enjoy the settings. One doesn`t want to run the code, AREPL mechanically evaluates it.

Python Docstring Generator

Documenting is remarkably essential, however it's far a tedious task, and every so often we take shortcuts due to the fact we lack the equipment to make it simpler or extra efficient. 

Python Docstring Generator diminishes builders` enterprise via means of auto-growing docstrings, and aleven though it is able to sound trivial, it's far a time-saver. It

Better Comments

Comments are important for any code as they assist the builders recognize the code higher. The Better Comments extension is barely exclusive than the others. It focuses totally on making extra human-pleasant and readable feedback on your Python code. 

With this extension, you may arrange your annotation and enhance the code clarity. You can use numerous classes and shades to categorize your annotations—for example, Alerts, Queries, TODOs, and Highlights.

Visual Studio IntelliCode

This is a standalone extension that offers AI-assisted IntelliSense. As you would possibly have noticed, the primary extension in this listing additionally includes IntelliSense. 

But in my experience, Python builders locate that this extension works higher as it scans your code the use of AI after which does a higher process with autocomplete than the Python extension from Microsoft. It is one of the pinnacle 10 VS Code Extensions for Python Developers to examine in 2023.

Black Formatter

Black Formatter permits you to layout your code using one of the maximum used Python formatters “Black”. Having a formatter in location improves productivity whilst operating on Python tasks with a couple of builders. 

That`s due to the fact the formatter is PEP8 compliant and makes the code fashion remains regular via the tasks and saves you time with the aid of using now no longer having discussions in pull requests approximately how the code must be formatted.

Bracket Pair Colorizer 2

Bracket Pair Colorizer 2 is some other extension that we builders quickly become aware of which brackets belong to every different and makes it less difficult to study the code. You can specify the matching bracket with shades and set the tokens and shades you need to use. 

This loose extension may be helpful, mainly in case your code has nested situations and loops. It is one of the pinnacle 10 VS Code Extensions for Python Developers to examine in 2023.

Conclusion

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HOW TO BECOME A SELF-TAUGHT PYTHON BACKEND DEVELOPER?

Software improvement basically entails parts: the front-cease improvement, and back-cease improvement, that have their very own demanding situations.

But backend improvement, through and large, is taken into consideration as an experts` paradise that freshers worry to tread.

Back-cease improvement entails a great deal extra than simply growing code.

It calls for right mathematical acumen and a radical expertise of software program and software program improvement.

It entails expertise in how servers paintings and the way they talk with character systems.

The back-cease builders ought to have a wide information of databases, applications, protection architecture, and web website hosting environments, and an expertise of automation and frontend processes.

As backend improvement is feasible in numerous languages, the bulk of builders select Python for its versatility, easy code, and compatibility with statistics technology and gadget gaining knowledge of techniques.

Python has end up the go-to language for backend improvement for fundamental systems like Quora, Netflix, Google, Facebook, and many others commencing doorways for python backend builders.

As in line with the data from the United States Bureau of Labor and Statistics, backend improvement will develop through 13% through 2028 with the growing utilization of cell telephones and the e-Commerce enterprise and subsequently the inevitable boom in call for for Python backend builders.

But on the subject of python backend improvement, the very motive that it entails having a deeper and wider information, it's far taken into consideration a confined zone, extra so while you are new to programming and haven't any plans to have a traditional degree.

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The course to turning into a self-taught python developer is dotted with demanding situations however now no longer when you have a path map.

Building Basics:

Learning the fundamentals of Python is step one you ought to take as a beginner.

Learning to jot down syntax calls for putting in IDE so that you can edit the code or you may visit on line editors for this task.

Next, you'll need to visit on line tutorials to discover ways to honestly write code.

Most of the tutorials are pretty beneficial in giving the simple concept of what the Python code looks as if and this is the handiest issue they do.

If you don`t need to kill your preference to research Python at the start itself, it's far higher to get an amazing draw close to theoretical principles.

Get to realize approximately variables, statistics types, assessment loops, arrays, and functions.

Start with easy examples shifting directly to complicated ones as you develop with the principles and consider not anything beats experimentation in programming.

Bridge with Front-End Frameworks:

To be hired as a backend developer, you'll now no longer want the front-cease information.

If you need to keep away from useless hassle for the duration of synchronizing with the front-cease improvement, peeping into it's going to assist like a spell.

Be it speaking cease-points, figuring out protection flaws in code, or correcting in a roundabout way written vain code, understanding the hows and whys of the front cease will assist.

So, ensure you get a flavor of HTML, CSS, and JavaScript for cooking withinside the backend well.

That being said, it isn't always vital to look thru the pipeline of the front-cease talents to its cease, due to the fact it's far overwhelming except you're aiming to end up

I hope my blog was relevant for you. To get in-depth knowledge on Python along with its various applications, check out our interactive python course in bangalore.

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Python Decorators: A Practical Guide

Python Decorators: A Practical Guide

Python decorators are a powerful feature that allows programmers to modify the behaviour of a function or class without having to change its source code. This article aims to provide a practical guide to understanding and using decorators in your Python code.

What are Decorators?

Decorators are functions that take another function as input and return a modified version of that function. In other words, decorators are a way to wrap a function and modify its behavior without changing its source code.

The syntax for using decorators is as follows:

ruby

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@decorator_function

def my_function():

    # function body

Here, decorator_function is the name of the function that will modify my_function. The @ symbol is used to indicate that decorator_function is a decorator for my_function.

Decorators can be chained together, which means that multiple decorators can be applied to a single function:

less

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@decorator_function1

@decorator_function2

def my_function():

    # function body

In this case, decorator_function2 will modify the function returned by decorator_function1.

Creating Decorators

Creating a decorator in Python is relatively simple. A decorator is just a regular Python function that takes another function as an argument and returns a modified version of that function.

Here's an example of a decorator that adds logging to a function:

python

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def logger(func):

    def wrapper(*args, **kwargs):

        print(f"Entering {func.__name__}")

        result = func(*args, **kwargs)

        print(f"Exiting {func.__name__}")

        return result

    return wrapper

In this example, the logger function takes another function func as an argument and returns a new function wrapper. wrapper logs the entry and exit of func and returns the result of calling func. If you're looking for training in react native, then you can check out our react native course in Bangalore.

Using Decorators

Decorators can be used to modify the behavior of a function in a variety of ways. Here are some examples of how decorators can be used:

Timing function execution:

python

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import time

def timer(func):

    def wrapper(*args, **kwargs):

        start_time = time.time()

        result = func(*args, **kwargs)

        end_time = time.time()

        print(f"Elapsed time: {end_time - start_time:.2f} seconds")

        return result

    return wrapper

@timer

def my_function():

    # function body

Caching function results:

python

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def memoize(func):

    cache = {}

    def wrapper(*args):

        if args in cache:

            return cache[args]

        else:

            result = func(*args)

            cache[args] = result

            return result

    return wrapper

@memoize

def fib(n):

    if n < 2:

        return n

    return fib(n - 1) + fib(n - 2)

Adding security checks:

python

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import functools

import getpass

def requires_password(func):

    @functools.wraps(func)

    def wrapper(*args, **kwargs):

        password = getpass.getpass("Enter password:")

        if password == "password123":

            return func(*args, **kwargs)

        else:

            raise Exception("Incorrect password")

    return wrapper

@requires_password

def my_function():

    # function body 

Conclusion

Python decorators are a powerful feature that can be used to modify the behaviour of functions and classes without having to change their source code. Decorators are easy to create and use, and they can be applied in a variety of situations to add functionality to your Python code. If you're looking for training in python, then you can check out our Python course in Bangalore.

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Why is Python Guiltlessly Called a Failed Programming Language?

Python is probably famous, however it truly isn't a futuristic programming language

Unarguably, Python is one of the pinnacle programming languages this is substantially utilised in tech organisations for numerous improvement and coding purposes.

The language is notably famous amongst novices due to the fact it's miles pretty clean to examine and may be deployed below numerous improvement circumstances.

Python is largely a programming language that is free, open-source, object-oriented, and taken into consideration high-degree.

The language has in-constructed statistics systems and dynamic semantics.

In a nutshell, the language is famous amongst programmers because it gives high-cease productiveness and its clarity renders this system low maintenance.

But the tech enterprise is split approximately into the destiny possibilities of Python.

Some say Python will maintain to dominate the tech improvement space, meanwhile, others suppose Python is truly now no longer the language of destiny.

Python truly gives numerous blessings that trap tech professionals, however at the flipside, the language has numerous disadvantages.

Initially, it took the programming network to comprehend the blessings supplied through Python.

But because the early 2010s, the language has been booming and ultimately has handed the dominance of C, C#, Java, and JavaScript in popularity.

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Hence, setting an expiration date at the language`s skills could be a piece of a stretch, nevertheless, you can by no means deny the drawbacks that it carries.

Disadvantages of Using Python

Currently, Python is most famous amongst novices and current tech professionals.

But the language is pretty vintage and had masses of time to grow, however it didn`t, instead, it most effective were given famous and bought help from a big and supportive network.

Python`s sluggish runtime is a chief challenge for programmers once they write large applications with plenty of traces of code, besides, it isn't towards hardware due to the fact it's miles a high-degree programming language, unlike C or C++.

Python is particularly sluggish due to the fact its execution takes location with the assistance of an interpreter, in place of a compiler.

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The interpreter reasons the coding govt to sluggish down dynamically.

Furthermore, builders declare the language is likewise now no longer green for cellular utility improvement, Android and iOS utility improvement are pretty tough for Python.

Libraries like Kivy and Beware had been clearly evolved to decorate the overall performance of Python in order that it may be used to create cellular applications.

The libraries are pretty useful, however the language can not feature without them.

Meanwhile, different programming languages like Java, Kotlin, and Swift are actually revolutionising the cellular app improvement space.

Will Python Stand Out Among its Competitors?

Python is largely an easy programming language, that's certainly considered one among its best disadvantages.

The language is truly now no longer a failed one, however it absolutely isn't a futuristic one.

Languages like Rust, Go, and Julia are more difficult and patch up the susceptible spots of Python.

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A guide to writing a perfect Python resume

What makes a good Python resume?

Here are the steps to write a Python resume for both beginners and experienced programmers:

1. Keep It Brief

Python resume should be as short and sweet as possible! 

Recruiters usually have about 30 seconds to read a resume and decide, so it should be clear. 

Make your experience as readable as possible by concentrating on the most important points.

2. Create a resume according to the Python job description

Instead of sending out a single resume for every job, alter the details of your resume for each application you submit. 

Check out the company's website to see what style, tone, and writing adjustments they prefer, and adjust your Python resume accordingly.

3. Choose the right templates

The information on your resume should be unique to you, even if it contains information such as your work experience, skills, and contact information. 

A resume can also be modified by changing its appearance or design

4. Contact Information

It should be placed in an accessible area so that it can be accessed by anyone. 

You do not want the recruiter to search through your entire resume if they decide to contact you after reviewing your Python resume. 

Ensure that all the information is accurate by double-checking it. 

If you want a new job, your headline should reflect your career aspirations, not your current position. 

If you want to become a Python developer, even if you are a marketing intern, your heading should be "Python developer".

5. Previous work experience

You should list the most recent job at the top, followed by an older job, and so on. 

You should include the job title, the company, your tenure in the position, and your achievements in this section. 

Not just what you were assigned, employers want to see what you actually accomplished. 

Describe your Python accomplishments in the context of business metrics to demonstrate your understanding of the bigger picture and ability to translate your work results into real-world outcomes.

6. Education

You should include your post-secondary degrees (i.e. community college, college, and graduate degrees). 

List your degree even if it is not relevant to the job you are applying for. 

Rather than having a computer science background, Python developers come from a wide range of fields. 

You shouldn't list micro-degrees like certifications or professional training here.

7. Qualifications and certifications

Technical positions require skills sections, which are not optional. 

It is important to highlight terms such as Python, Machine Learning, and Data Science. 

Here you can also list your soft skills, such as communication or leadership. Your skills should not be ranked. If you do, you may overpromise or undersell. 

You may list your strengths first and your weaknesses later, but do not overwhelm this section with unnecessary details.

Overview

I hope you found this Python resume creation tutorial helpful. Near Learn is the best place for Python training if you are looking for it.

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What is the Best Language for Machine Learning?

If you're simply getting began out withinside the subject of device studying (ML), or in case you are trying to refresh your skills, you would possibly surprise that is the satisfactory language to use. Choosing the proper device studying language may be difficult, specifically in view that there are such a lot of incredible options. 

There are an splendid 700+ programming languages in sizeable use, and every has its personal professionals and cons. If you're simply starting your profession as a device studying engineer, with time you’ll find out that are the satisfactory programming languages for the particular commercial enterprise issues you are attempting to solve. 

Before diving into the satisfactory device studying languages, let’s discover the concept. 

What is Machine Learning? 

Without going into an excessive amount of detail, device studying is a subset of synthetic intelligence that offers pc structures with the capacity to mechanically examine and make predictions primarily based totally on facts. These predictions can range significantly relying at the particular use case. 

Most Popular Machine Learning Language: Python

Before diving into the distinctive device studying languages, it’s essential to apprehend that there isn't always definitely one “satisfactory” language. Each one has its personal professionals, cons, and particular abilties. It in large part relies upon on what you’re looking to construct and your background. 

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With that said, the maximum famous device studying language, with out a doubt, is Python. Around 57% of facts scientists and device studying builders rely upon Python, and 33% prioritize it for improvement. 

Python’s frameworks have significantly advanced over the last few years, which has improved its abilties with deep studying. There has been the discharge of pinnacle libraries like TensorFlow and numerous others. 

Over 8.2 million builders throughout the globe rely upon Python for coding, and there’s an awesome cause for that. It is a fave desire for facts analytics, facts science, device studying, and AI. Its tremendous library environment permits device studying practitioners to access, handle, transform, and manner facts with ease. It additionally gives platform independence, much less complexity, and higher readability. 

The integrated libraries and programs offer base-degree code, because of this that device studying engineers don’t ought to begin writing from scratch. And in view that device studying calls for non-stop facts processing, Python’s in-construct libraries and programs help with nearly each task. All of this ends in a discounted improvement time and an development in productiveness while running with complicated device studying applications. 

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Some of the world’s largest tech giants like Google, Instagram, Facebook, Dropbox, Netflix, Walt Disney, YouTube, Uber, and Amazon decide upon Python as their programming language. 

While Python certainly could be the maximum famous language, there are numerous others that have to be taken into consideration. The jogging 5 are Python, R, C/C++, Java, and JavaScript. The remote 2nd to Python is generally taken into consideration to be C/C++. Java is near behind, and even as Python is frequently as compared to R, they definitely don’t compete in phrases of popularity. In surveys regarding facts scientists, R has frequently finished the bottom prioritization-to-utilization ratio the various 5 languages. Javascript is frequently positioned at the decrease stop of the list.

While nowhere close to as famous because the pinnacle 5, there are numerous different languages that device studying practitioners use and are really well worth consideration, together with Julia, Scala, Ruby, MATLAB, Octave, and SAS. 

Conclusion

As you could see from this article, there is lots that is going into selecting the satisfactory language for device studying. It’s now no longer as easy as one being the “satisfactory.” It all relies upon in your experience, expert background, and applications. But famous languages like Python, C++, Java, and R ought to continually be taken into consideration first.

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Python 3.11 vs Python 3.10: Why Developers Love the Latest Version More?

Python is one of the most popular programming languages. But the only problem with this programming language is speed, because it's no secret that Python is not the fastest programming language. But that will change in Python 3.11, but what makes Python 3.11 better than its version 3.10? We will find out in this article. The Python Software Foundation (PSF) maintains Python, develops the language, and is constantly working on new ways to improve it. On average, Python 3.11 is 14% faster than Python 3.10. The new version is slower in some benchmarks, but up to 64% faster in others.

Python Features 3.10

Better error messages

Python has always been praised as a user-friendly programming language. While this is true, there are some Python features that may be friendlier. Python 3.10 comes with much more accurate and useful error messages. One of the most influential improvements in Python 3.10 is better and more accurate bug fixes for many common issues.

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Pairing of structural patterns

Structural pattern comparison is one of the biggest features of Python 3.10, perhaps in terms of controversy and potential impact, is structural pattern comparison. This implementation is sometimes called the case of a switch, which is part of Python, but you can see that structural pattern comparison is much more powerful.

Here are three different examples that highlight why this feature is called structural pattern matching and show you how to use this new feature:

Detect and deconstruct various structures in your data

Use different types of patterns

Compare literal patterns

The structural pattern agreement is a comprehensive complement to Python. To try out how you can use this in your own projects, the following three sections delve into some details. You can also find some links to help you explore deeper than you want.

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Deconstruction of data structures

At its core, comparing structural patterns identifies patterns that your data structures can match. In this section, you will study a practical example of working with data in a different order, even if they have the same meaning. You define multiple patterns, and depending on which pattern matches your data, you will process your data correctly.

This section will shed some light on the explanation of possible patterns. Instead, it will try to give you an idea of ​​the possibilities. In the next section, we will go back and explain the patterns in more detail.

Python Features 3.11

CPython optimization

CPython is a reference implementation of the Python programming language. CPython, written in C and Python, is the standard and most widely used implementation of Python. In version 3.11, the CPython compiler is much more optimized and faster than in version 3.10. CPython 3.11 is on average 1.22x faster than CPython 3.10, measured by a set of performance benchmarks, and is integrated with GCC on Ubuntu Linux. Depending on your working pressure, acceleration can be up to 10-60% faster. In Python 3.11, developers usually focus on running faster and running faster, as outlined in the documentation.

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Python 3.11:  Speed

Each new version has many improvements and is similar to Python 3.11. One of the features (speed) that every developer has been waiting for is finally here. Because the object type rarely changes, the compiler now tries to parse the running code and replace the general byte codes with specific ones. Binary operations (addition, subtraction, etc.) can be replaced, for example, in special versions for integers, floating numbers and strings.

Python function calls also require a bit of overhead in Python 3.11. Function call storage frames now use less memory and are designed more efficiently. Although recursive calls are not optimized for endpoints (which is probably not possible in Python), they are more efficient than earlier versions. The Python interpreter itself is also beginning to speed up, and the kernel modules needed for the Python runtime are stored and loaded more efficiently.

According to the official set of Python benchmarks, Python 3.11 runs about 1.25 times faster than version 3.10. Keep in mind that this acceleration is an overall measure: some objects are faster, but many others are somewhat faster or similar. The best thing about these upgrades, however, is that they are free. You do not need to make any code changes for Python programs to take advantage of 3.11 acceleration. 

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Automating repetitive tasks with Python's "automation" libraries such as pyautogui and selenium

Automating Repetitive Tasks with Python's "Automation" Libraries such as PyAutoGUI and Selenium

As the amount of data we need to process and analyze increases, so does the amount of repetitive tasks we need to perform. These tasks can be time-consuming and prone to human error, making automation a great solution to streamline workflows and increase efficiency. In this article, we will explore two popular "automation" libraries in Python, PyAutoGUI and Selenium, and how they can be used to automate repetitive tasks.

What is PyAutoGUI?

PyAutoGUI is a Python library that allows you to automate tasks by controlling your computer's keyboard and mouse. PyAutoGUI can perform tasks like moving the mouse cursor, clicking the mouse, typing keys on the keyboard, taking screenshots, and more.

How to Install PyAutoGUI?

To install PyAutoGUI, you can use pip, the package installer for Python. Open your terminal or command prompt and type:

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pip install pyautogui

How to Use PyAutoGUI?

Once PyAutoGUI is installed, you can start automating tasks. Let's take an example where we want to open a file using the Windows file explorer. If you're looking for training in react native, then you can check out our react native course in Bangalore. Here is a sample code that does this:

python

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import pyautogui as pg

# Press the Windows key to open the Start menu

pg.press('win')

# Type "file explorer" and hit Enter to open it

pg.typewrite('file explorer')

pg.press('enter')

# Wait for File Explorer to open

pg.sleep(2)

# Type the path of the file we want to open and hit Enter

pg.typewrite('C:\\Users\\user\\Documents\\example.txt')

pg.press('enter')

This code will automate the process of opening the file explorer, typing the file path, and opening the file. You can modify this code to suit your needs. If you're looking for training in python, then you can check out our Python course in Bangalore.

What is Selenium?

Selenium is a Python library that allows you to automate web browsers. Selenium can perform tasks like clicking links, filling out forms, navigating web pages, and more.

How to Install Selenium?

To install Selenium, you can use pip, the package installer for Python. Open your terminal or command prompt and type:

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pip install selenium

How to Use Selenium?

Once Selenium is installed, you can start automating tasks. Let's take an example where we want to automate the process of logging into a website. Here is a sample code that does this:

python

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from selenium import webdriver

# Open the web browser and navigate to the login page

browser = webdriver.Chrome()

browser.get('https://example.com/login')

# Enter the login credentials and click the login button

username = browser.find_element_by_id('username')

password = browser.find_element_by_id('password')

login_button = browser.find_element_by_id('login-button')

username.send_keys('myusername')

password.send_keys('mypassword')

login_button.click()

This code will automate the process of logging into a website by opening the web browser, navigating to the login page, entering the login credentials, and clicking the login button. If you're looking for training in react JS, then you can check out our React JS course in Bangalore.

Conclusion

Automating repetitive tasks can save time and increase efficiency. Python's "automation" libraries like PyAutoGUI and Selenium provide a simple way to automate tasks that involve keyboard and mouse inputs and web browsing. By using these libraries, you can easily automate tasks like data entry, web scraping, and more.

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Creating a Full-Stack Web Application with React JS and Python Flask

React Native is a popular framework for building cross-platform mobile apps using JavaScript and React. It allows developers to create high-quality, performant mobile apps that work on both iOS and Android platforms. Python Django is a powerful web framework that can be used to build RESTful APIs that can be used by mobile apps.

In this article, we will walk through the process of building a React Native mobile app that communicates with a Python Django API.

Step 1: Set up the Django API

The first step is to create the Django API that our mobile app will communicate with. To do this, we'll need to create a new Django project and add a new app to it.

First, install Django by running the following command:

pip install django

Next, create a new Django project by running the following command:

django-admin startproject my project

This will create a new Django project called myproject. Next, create a new app called api by running the following command:

bash

cd myproject

python manage.py startapp api

This will create a new app called api inside the myproject project.

Next, open the settings.py file in the myproject folder and add the api app to the INSTALLED_APPS list:

css

INSTALLED_APPS = [    ...    'api',]

Now, open the urls.py file in the myproject folder and add the following code:

php

from django.urls import include, path

urlpatterns = [

    path('api/', include('api.urls')),

]

This code creates a new URL pattern that maps to the api app.

Now, create a new file called urls.py in the api folder and add the following code:

python

from django.urls import path

from . import views

urlpatterns = [

    path('hello/', views.hello),

]

This code creates a new URL pattern that maps to a new view called hello.

Finally, create a new file called views.py in the api folder and add the following code:

python

from django.http import JsonResponse

def hello(request):

    return JsonResponse({'message': 'Hello from Django!'})

This code creates a new view that returns a JSON response with the message "Hello from Django!".

Now, start the Django development server by running the following command:

python manage.py runserver

This will start the development server on http://localhost:8000. If you navigate to http://localhost:8000/api/hello/ in your web browser, you should see the message "Hello from Django!". If you're looking for training in react native, then you can check out our react native course in Bangalore.

Step 2: Set up the React Native app

The next step is to set up the React Native app that will communicate with the Django API. To do this, we'll need to create a new React Native app using the expo-cli command line tool.

First, install the expo-cli tool by running the following command:

npm install -g expo-cli

Next, create a new React Native app by running the following command:

csharp

expo init myapp

This will create a new React Native app called myapp. Choose the "blank" template when prompted.

Next, install the axios library, which we'll use to make API requests, by running the following command:

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npm install axios

Finally, open the App.js file in the myapp folder and replace the existing code with the following:

python

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import React, { useEffect, useState } from 'react';

import {

import { StyleSheet, Text, View } from 'react-native';

import axios from 'axios';

export default function App() {

const [message, setMessage] = useState('');

useEffect(() => {

axios.get('http://localhost:8000/api/hello/')

.then(response => {

setMessage(response.data.message);

})

.catch(error => {

console.log(error);

});

}, []);

return (

<View style={styles.container}>

<Text>{message}</Text>

</View>

);

}

const styles = StyleSheet.create({

container: {

flex: 1,

backgroundColor: '#fff',

alignItems: 'center',

justifyContent: 'center',

},

});

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This code imports the necessary libraries, sets up a state variable called `message`, and makes an API request to the Django server using the `axios` library. The `useEffect` hook is used to make the API request when the component is mounted, and the response is used to update the `message` state variable. Finally, the `message` variable is displayed in the app. If you're looking for training in python, then you can check out our Python course in Bangalore.

Now, start the Expo development server by running the following command:

expo start

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This will start the Expo development server on `http://localhost:19002`. You can then run the app on your mobile device using the Expo app or on an emulator using the options provided on the development server page.

Conclusion

In this article, we walked through the process of building a React Native mobile app that communicates with a Python Django API. We created a new Django project and app, set up a new view to return a JSON response, and used the `axios` library to make API requests from our React Native app. By following these steps, you can build powerful mobile apps that communicate with a Python backend using the Django web framework.

In conclusion, building a React Native mobile app that communicates with a Python Django API is a powerful way to create cross-platform mobile applications. React Native offers a simple and efficient way to build mobile applications that work across multiple platforms, while Python and Django provide a flexible and robust backend that can handle complex data processing and business logic. By combining these two technologies, developers can create high-performance mobile apps with a powerful backend API.  If you're looking for training in react JS, then you can check out our React JS course in Bangalore.

In this article, we covered the basics of setting up a Python Django API, creating a view to return JSON data, and building a React Native mobile app that communicates with the API. By following these steps, developers can build powerful, cross-platform mobile apps that can handle complex data and business logic. With the power of Python and Django on the backend and React Native on the frontend, the possibilities for mobile app development are endless.

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Top 10 Python GUI Frameworks for Developers

Python is a powerful and versatile programming language used by developers to build a wide range of applications. From web applications to data analysis and machine learning, Python has become the go-to language for developers in various domains. Graphical user interfaces (GUIs) are an essential part of many applications, making it easier for users to interact with software. In this article, we will discuss the top 10 Python GUI frameworks that developers can use to create amazing graphical user interfaces.

PyQt

PyQt is a cross-platform GUI toolkit that is built around the popular Qt framework. It is one of the most widely used Python GUI frameworks and offers a range of tools and widgets for building desktop applications. PyQt is compatible with multiple platforms including Windows, Mac OS X, and Linux, and provides a consistent look and feel across all these platforms.

Tkinter

Tkinter is the default GUI framework that comes with Python. It is a powerful toolkit for creating desktop applications and provides a range of widgets such as buttons, labels, and menus. Tkinter is easy to learn and use and is suitable for beginners and experienced developers alike.

wxPython

wxPython is a popular GUI framework that is based on the C++ wxWidgets library. It offers a range of tools and widgets for building cross-platform desktop applications. wxPython provides a native look and feel for each platform and supports many popular GUI development tools. If you're looking for training in python, then you can check out our Python course in Bangalore.

PySide

PySide is an open-source Python GUI framework that is based on the Qt framework. It offers a range of tools and widgets for building desktop applications and provides a native look and feel across multiple platforms. PySide is easy to use and offers excellent performance.

Kivy

Kivy is an open-source Python GUI framework that is designed for creating multi-touch applications. It provides a range of tools and widgets for building cross-platform mobile and desktop applications. Kivy is easy to learn and use and is suitable for beginners and experienced developers alike. If you're looking for training in react native, then you can check out our react native course in Bangalore.

PyGTK

PyGTK is a Python binding for the GTK+ toolkit, which is a popular GUI toolkit used in many Linux applications. It provides a range of tools and widgets for building desktop applications and is compatible with multiple platforms including Windows and Mac OS X.

Pyforms

Pyforms is an open-source Python GUI framework that is designed for creating complex desktop applications. It provides a range of tools and widgets for building cross-platform applications with an intuitive and modern user interface. Pyforms is easy to learn and use and offers excellent performance.

PyGUI

PyGUI is an open-source Python GUI framework that is designed for creating small to medium-sized desktop applications. It provides a range of tools and widgets for building cross-platform applications with a modern user interface. PyGUI is easy to learn and use and offers excellent performance.

PyGObject

PyGObject is a Python binding for the GObject library, which is the foundation of the GNOME desktop environment. It provides a range of tools and widgets for building desktop applications with a modern user interface. PyGObject is compatible with multiple platforms including Windows and Mac OS X.

PyQTgraph

PyQtgraph is an open-source Python GUI framework that is designed for creating scientific and technical applications. It provides a range of tools and widgets for building cross-platform applications with a modern user interface. PyQtgraph is easy to learn and use and offers excellent performance.

Conclusion

Python offers a wide range of GUI frameworks for developers to build amazing graphical user interfaces. Each framework has its own set of advantages and disadvantages, and the choice depends on the specific requirements of the application. PyQt, Tkinter, wxPython, PySide, Kivy, PyGTK, Pyforms, PyGUI, PyGObject, and PyQtgraph are the top 10 Python GUI frameworks 

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10 Best React.js UI Component Libraries

React Bootstrap React.js is a popular JavaScript library used for building modern web applications. Its component-based architecture makes it easy to reuse code and create user interfaces that are both flexible and efficient. One of the biggest advantages of React is its rich ecosystem of UI component libraries. In this article, we will take a look at the top 10 best React.js UI component libraries.

Material UI

Material UI is a React component library that follows the Google Material Design guidelines. It provides a set of pre-built UI components that can be easily customized to fit the needs of your application. Material UI is popular for its clean design and ease of use, making it a great choice for both beginners and advanced developers.

Ant Design

Ant Design is a comprehensive UI component library that includes over 50 reusable components, such as forms, tables, and charts. It also includes a set of icons and a design system that can be used to create consistent user interfaces. Ant Design is known for its flexibility and extensibility, making it a popular choice for large-scale applications.

Semantic UI React

Semantic UI React is a port of the popular Semantic UI library to React. It provides a set of UI components that are based on natural language principles, making them easy to understand and use. Semantic UI React is also highly customizable, allowing developers to easily change the appearance and behavior of components. If you're looking for training in python, then you can check out our Python course in Bangalore.

Blueprint

Blueprint is a UI toolkit for building web applications with React. It includes a set of UI components that are designed to be both functional and beautiful. Blueprint also includes a set of icons and a design system that can be used to create consistent user interfaces. It is known for its clean design and ease of use.

React Bootstrap is a React implementation of the popular Bootstrap framework. It provides a set of UI components that are based on the Bootstrap design language, making it easy to create responsive and mobile-friendly user interfaces. React Bootstrap is also highly customizable, allowing developers to easily modify the appearance and behavior of components.

Chakra UI

Chakra UI is a simple and modular UI component library for React. It provides a set of customizable components that can be easily styled to fit the needs of your application. Chakra UI also includes a design system that can be used to create consistent user interfaces. It is known for its simplicity and ease of use.

Evergreen

Evergreen is a UI library for building web applications with React. It includes a set of customizable components that are designed to be both functional and beautiful. Evergreen also includes a set of icons and a design system that can be used to create consistent user interfaces. It is known for its flexibility and ease of use. If you're looking for training in react native, then you can check out our react native course in Bangalore.

Rebass

Rebass is a lightweight and customizable UI component library for React. It provides a set of functional and responsive components that can be easily styled to fit the needs of your application. Rebass also includes a set of themes and a design system that can be used to create consistent user interfaces. It is known for its simplicity and flexibility.

Grommet

Grommet is a UI component library that is designed to be both functional and beautiful. It includes a set of customizable components that can be easily styled to fit the needs of your application. Grommet also includes a set of icons and a design system that can be used to create consistent user interfaces. It is known for its rich feature set and ease of use.

Carbon

Carbon is a design system and UI component library for building web applications with React. It includes a set of customizable components that are designed to be both functional and beautiful. Carbon also includes a set of icons and a design system that can be used to create consistent user interfaces. It is known for its enterprise-level feature set and ease of use.

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How to Use ChatGPT for Python Programming?

ChatGPT is a powerful language model developed by OpenAI that is capable of generating human-like responses to text-based queries. While it is primarily designed for natural language processing and text generation, it can also be used for Python programming. In this article, we'll discuss how to use ChatGPT for Python programming with the following headings:

Introduction to ChatGPT

ChatGPT is a language model that uses deep learning techniques to generate natural language responses to text-based queries.

It is trained on a vast corpus of text data and is capable of understanding the nuances of human language.

Setting up ChatGPT for Python programming

To use ChatGPT for Python programming, you need to install the OpenAI API package.

You can install the package using pip, a package manager for Python. If you're looking for training in react native, then you can check out our Python course in Bangalore.

Example:

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pip install openai

Getting an OpenAI API key

To access the OpenAI API, you need to sign up for an OpenAI API key.

You can sign up for a key on the OpenAI website.

Example:

python

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import openai_secret_manager

assert "openai" in openai_secret_manager.get_services()

secrets = openai_secret_manager.get_secret("openai")

print(secrets)

Using ChatGPT for Python programming

Once you have installed the OpenAI API package and obtained an API key, you can use ChatGPT for Python programming.

To use ChatGPT, you need to provide it with a prompt, which is a text-based query or command. If you're looking for training in react native, then you can check out our react native course in Bangalore.

ChatGPT will generate a natural language response based on the prompt.

Example:

python

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import openai

openai.api_key = secrets["api_key"]

def get_chatgpt_response(prompt):

    response = openai.Completion.create(

        engine="davinci",

        prompt=prompt,

       ��max_tokens=1024,

        n=1,

        stop=None,

        temperature=0.7,

    )

    message = response.choices[0].text

    return message.strip()

Examples of using ChatGPT for Python programming

ChatGPT can be used for a variety of Python programming tasks, such as generating code snippets, answering programming-related questions, and providing explanations of programming concepts.

Example:

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prompt = "What is a Python list comprehension?"

response = get_chatgpt_response(prompt)

print(response)

Output:

"A list comprehension is a concise way of creating a list in Python. It consists of an expression followed by a for clause and zero or more if clauses. Here is an example: [x**2 for x in range(10) if x % 2 == 0] This will create a list of even squares from 0 to 8."

In conclusion, ChatGPT can be a useful tool for Python programmers looking to get quick answers to programming-related questions or generate code snippets. With a little setup and configuration, you can use ChatGPT to improve your Python programming skills and productivity.

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Deep Diving into the Differences between Python 2 and Python 3

Python is a popular, high-level programming language that is widely used for a variety of tasks, from web development to data analysis. However, there are currently two major versions of Python in use: Python 2 and Python 3. In this article, we will take a deep dive into the differences between these two versions of Python, and discuss the factors that should be considered when choosing which version to use for your next project.

Syntax Differences

One of the most noticeable differences between Python 2 and Python 3 is the syntax. Python 3 introduced several changes to the language that are not backward compatible with Python 2. Here are a few examples of these changes:

The print statement in Python 2 is replaced by the print() function in Python 3.

# Python 2

print "Hello, world!"

# Python 3

print("Hello, world!")

The raise statement in Python 2 is replaced by the raise Exception expression in Python 3.

# Python 2

raise ValueError, "Invalid value"

# Python 3

raise ValueError("Invalid value")

The xrange function in Python 2 is replaced by the range function in Python 3.

# Python 2

for i in xrange(10):

    print(i)

# Python 3

for i in range(10):

    print(i)

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Library Differences

Another major difference between Python 2 and Python 3 is the library. Python 3 introduced several new libraries and modules that are not available in Python 2. Here are a few examples:

The pathlib module was introduced in Python 3.3, which provides an object-oriented approach to working with file and directory paths.

# Python 3

from pathlib import Path

path = Path("/path/to/file.txt")

print(path.name)

The statistics module was introduced in Python 3.4, which provides functions for mathematical statistics.

# Python 3

import statistics

data = [1, 2, 2, 3, 3, 4]

print(statistics.mean(data))

The asyncio module was introduced in Python 3.4, which provides an asynchronous I/O framework for writing concurrent code using coroutines.

# Python 3

import asyncio

async def my_coroutine():

    print("Hello, world!")

loop = asyncio.get_event_loop()

loop.run_until_complete(my_coroutine())

Unicode Handling

Python 2 uses ASCII as the default encoding for the str type, which means that it can only handle a limited set of characters. Python 3, on the other hand, uses Unicode as the default encoding for the str type, which means that it can handle a much larger set of characters. This makes Python 3 more suitable for working with internationalisation and localization. If you're looking for training in react native, then you can check out our Python course in Bangalore.

# Python 2

string = "नमस्ते"

print(string)

# Python 3

string = "नमस्ते"

print(string)

Conclusion

Python 2 and Python 3 are both popular versions of Python, but they have several important differences. Python 3 introduced several new features and improvements to the language, such as improved Unicode handling, new libraries and modules, and a new syntax. However, Python 2 is still widely used in many projects and has a large ecosystem of libraries and tools. Near Learn, a technology courses institute in Bangalore, offers courses on both Python 2 and Python 3, which can help developers to understand the differences between the two versions and make an informed decision when choosing which version to use for their next project. It's important to consider the specific requirements of your project and weigh the pros and cons before making a final decision.

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How to Run Your Python Code Concurrently Using Threads

Python is a powerful language that allows developers to create a wide range of applications, from web development to data analysis. However, when it comes to running code concurrently, Python can be a bit limited. One solution to this problem is to use threads. In this article, we will discuss what threads are, how to use them, and some best practices for using them effectively in Python.

What are Threads?

Threads are a way to run multiple tasks concurrently within a single program. A thread is a separate flow of execution within a program, which means that multiple threads can be executed simultaneously. Each thread runs independently and can perform its own tasks, but they share the same memory space.

Threads can be used to improve the performance of a program by allowing it to execute multiple tasks at the same time. This is particularly useful for tasks that are I/O-bound, meaning that they spend a lot of time waiting for input/output operations to complete, such as reading or writing files, making HTTP requests, or querying a database.  If you're looking for training in python, then you can check out our Python course in Bangalore.

Using Threads in Python

Python has a built-in module called threading that allows developers to create and manage threads. To use threads in Python, you need to import the threading module and create a new Thread object for each task you want to run concurrently. Here is an example:

ruby

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import threading

def task1():

    # code for task1

def task2():

    # code for task2

# create threads

thread1 = threading.Thread(target=task1)

thread2 = threading.Thread(target=task2)

# start threads

thread1.start()

thread2.start()

# wait for threads to finish

thread1.join()

thread2.join()

In this example, we define two functions task1 and task2, which represent the tasks we want to run concurrently. We then create two Thread objects, one for each task, and start them using the start method. Finally, we use the join method to wait for both threads to finish before the program exits.

Best Practices for Using Threads

When using threads, there are a few best practices you should follow to ensure that your program runs smoothly and efficiently:

Avoid shared data: Since threads share the same memory space, it's important to avoid accessing shared data concurrently to prevent race conditions and other synchronization issues. If you need to share data between threads, use thread-safe data structures such as locks, queues, or semaphores.

Use the right number of threads: Creating too many threads can actually slow down your program, as the overhead of managing multiple threads can become significant. Try to find the right balance between the number of threads and the tasks you want to run concurrently.

Prioritise tasks: If you have tasks that are more important than others, you can use thread priorities to ensure that they are executed first. However, be careful not to starve other threads of resources. If you're looking for training in react native, then you can check out our react native course in Bangalore.

Use a thread pool: Creating and managing threads can be expensive, especially if you have many short-lived tasks. Consider using a thread pool to reuse threads and reduce overhead.

Conclusion

Using threads is a powerful way to run multiple tasks concurrently in Python. It can improve the performance of your program, especially for I/O-bound tasks. However, using threads requires careful consideration of shared data, thread priorities, and the number of threads. By following these best practices, you can use threads effectively and avoid common pitfalls.

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How to write Python extensions in Rust with PyO3

Python is a powerful programming language with a rich ecosystem of libraries and tools. However, sometimes you need to write extensions in other languages to improve performance or access system-level functionality. Rust is a modern systems programming language that provides the safety and performance of a low-level language while still being easy to use. PyO3 is a Rust library that enables you to write Python extensions in Rust. In this article, we'll cover the basics of how to write Python extensions in Rust with PyO3.

Introduction to PyO3

PyO3 is a Rust library that allows you to write Python extensions in Rust. It provides a simple and safe way to interface with Python, allowing you to call Python functions from Rust and vice versa. PyO3 handles all the details of converting between Rust and Python types, making it easy to write high-performance Python extensions in Rust.

Setting up your development environment

Before you can start writing Python extensions in Rust with PyO3, you'll need to set up your development environment. You'll need to install Rust and Python, as well as PyO3 itself. You can install Rust and Python using your operating system's package manager, or download them from their respective websites. To install PyO3, add it to your Rust project's dependencies in the Cargo.toml file.

Writing a simple Python extension in Rust

Once you have your development environment set up, you can start writing your first Python extension in Rust. To do this, you'll need to create a Rust library that exports a C-compatible interface. You can use PyO3 to generate the necessary C code for you, which you can then compile into a shared library that can be loaded by Python. If you're looking for training in python, then you can check out our Python course in Bangalore.

Here's a simple example of a Rust function that adds two numbers:

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#[pyfunction]

fn add(a: i32, b: i32) -> i32 {

   a + b

}

This function uses the pyfunction attribute to tell PyO3 that it should be exported as a Python function. The a and b arguments are passed as i32 types, which PyO3 automatically converts to Python integers. The result is also an i32, which PyO3 converts back to a Python integer.

To compile this function into a Python extension, you'll need to create a lib.rs file that exports it:

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use pyo3::prelude::*;

#[pymodule]

fn my_module(_py: Python, m: &PyModule) -> PyResult<()> {

   m.add_function(wrap_pyfunction!(add, m)?)?;

   Ok(())

}

This code uses the pymodule attribute to tell PyO3 that this is a Python module. The add_function method adds our add function to the module, and the wrap_pyfunction macro generates the necessary C code for Python to call the Rust function.

Compiling and using the Python extension

To compile our Python extension, we can use Cargo, Rust's package manager:

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cargo build --release

This will generate a shared library in the target/release directory, which we can load in Python using the ctypes module:

python

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import ctypes

lib = ctypes.CDLL('target/release/libmy_module.so')

result = lib.add(1, 2)

print(result)  # prints 3

This code loads our Rust library and calls the add function with the arguments 1 and 2. The result is returned as a Python integer, which we can print to the console. If you're looking for training in react native, then you can check out our react native course in Bangalore.

Conclusion

In conclusion, PyO3 is a powerful tool that allows developers to write Python extensions in Rust. With its easy-to-use interface and comprehensive documentation, PyO3 offers a simple and efficient way to integrate Rust into your Python code. By using Rust to build your Python extensions, you can take advantage of Rust's performance, safety, and stability while still enjoying the benefits of Python's dynamic nature. Whether you're a seasoned Rust developer or a Python enthusiast looking to optimize your code, PyO3 is an excellent choice for building high-performance Python extensions in Rust.