Difference Between R Shiny Vs Shiny for Python

In this Python Tutorial we will learn the Difference Between R Shiny Vs Shiny for Python. If you haven’t been living under a rock for the past couple of weeks, you’ve likely noticed some groundbreaking news in the Shiny department. Yes, it’s finally available for Python! But how is the current Shiny for Python version? How does R Shiny compare vs Shiny for Python (PyShiny)? We have the answers, so continue reading.

Shiny for Python is still a work in progress, but you can already build some great PyShiny demos!

Today we’ll go over a detailed R Shiny vs. Shiny for Python comparison in 4 key areas – startup code, UI elements, server code/reactivity, and dashboard styling. Each key area will demonstrate code and end-result differences for both programming languages.

At the time of writing this post, Shiny for Python is still in alpha. The Shiny for Python ecosystem is quite limited at the moment, so if you’re looking for enterprise-grade Shiny apps, skip this article and go straight to R Shiny for your enterprise needs. But with that being said, there’s still quite a lot you can do with PyShiny – let’s find out below.

Boilerplate Code Needed to Run the App

So, what is a boilerplate code? Put simply, it’s the code that every app/dashboard has in common. In Shiny, it usually boils down to library imports, UI and server declaration, and their connection in a Shiny app.

Python and R have different views on best programming practices. In R, you import a package and have all the methods available instantly. In Python, you usually import required modules of a library and then call the methods with <module-name>.<method-name> syntax.

Want a Shiny app fast? Try Appsilon’s Shiny templates and have an R Shiny dashboard in less than 10 minutes.

To be fair, you can make Python work like R by using the from <module-name> import * syntax, but it’s not recommended way. Avoid this approach at all costs.

Let’s now take a look at the amount of code needed to write the most basic R Shiny app that renders one heading element:

library(shiny)


ui <- fluidPage(
  tags$h2("My R Shiny Dashboard")
)

server <- function(input, output, session) { }

shinyApp(ui = ui, server = server)

For reference, here’s what it looks like:

Image 1 - Boilerplate R Shiny app

Image 1 – Boilerplate R Shiny app

And here’s the same Shiny app in Python:

from shiny import App, ui


app_ui = ui.page_fluid(
    ui.tags.h2("My Python Shiny Dashboard")
)

def server(input, output, session): 
    pass


app = App(ui=app_ui, server=server)

It looks more or less identical:

Image 2 - Boilerplate Shiny for Python app

Image 2 – Boilerplate Shiny for Python app

So, what’s the difference? The difference boils down to recommended programming practices of individual languages. Both do the same thing in different ways. Both are great, but neither is better. It’s just preference.

We’ll now take a look at how you can declare UI elements in R Shiny / Shiny for Python.

UI Elements – R vs. Python

The goal of this section is to create a form-based Shiny application in both R Shiny and Shiny for Python. We won’t handle the user input but only deal with the UI instead.

Want to monitor R Shiny user sessions and draw them as a heatmap? Our detailed guide to R shinyheatmap package has you covered.

Let’s start with R Shiny. It’s incredibly easy to add input controls with methods such as selectInput(), radioButtons() and sliderInput(). Here’s the entire code snippet:

library(shiny)

ui <- fluidPage(
  tags$h1("Heading 1"),
  selectInput(
    inputId = "selectLevel",
    label = "Filter by level:",
    choices = c("Junior", "Mid level", "Senior"),
    selected = c("Junior")
  ),
  selectInput(
    inputId = "selectSkills",
    label = "Filter by skills:",
    choices = c("Python", "R", "Machine learning"),
    selected = c("Python", "Machine learning"),
    multiple = TRUE
  ),
  radioButtons(
    inputId = "radioExperience",
    label = "Experience level:",
    choices = c("0-1 years of experience", "2-5 years of experience", "5+ years of experience"),
    selected = c("2-5 years of experience")
  ),
  checkboxGroupInput(
    inputId = "cbxAdditional",
    label = "Additional:",
    choices = c("Married", "Has kids"),
    selected = c("Married")
  ),
  sliderInput(
    inputId = "slider",
    label = "Overall impression:",
    value = 5,
    min = 1,
    max = 10
  ),
  textInput(
    inputId = "textAdditional",
    label = "Anything to add?"
  )
)

server <- function(input, output) { }

shinyApp(ui, server)

The corresponding Shiny app looks like this:

Image 3 - R Shiny app with UI elements

Image 3 – R Shiny app with UI elements

The story is similar in Python. All UI elements have to be imported from shiny.ui, and you can then access individual UI elements by calling ui.<input-name>().

Here’s the code snippet which creates the same application as in R Shiny:

from shiny import App, ui


app_ui = ui.page_fluid(
    ui.tags.h1("Heading 1"),
    ui.input_select(
        id="selectLevel",
        label="Filter by level:",
        choices=["Junior", "Mid level", "Senior"],
        selected="Junior"
    ),
    ui.input_select(
        id="selectSkills",
        label="Filter by skills:",
        choices=["Python", "R", "Machine learning"],
        selected=["Python", "Machine learning"],
        multiple=True
    ),
    ui.input_radio_buttons(
        id="radioExperience",
        label="Experience level:",
        choices=["0-1 years of experience", "2-5 years of experience", "5+ years of experience"],
        selected="2-5 years of experience"
    ),
    ui.input_checkbox_group(
        id="cbxAdditional",
        label="Additional:",
        choices=["Married", "Has kids"],
        selected="Married"
    ),
    ui.input_slider(
        id="slider",
        label="Overall impression:",
        value=5,
        min=1,
        max=5
    ),
    ui.input_text(
        id="textAdditional",
        label="Anything to add?"
    )
)

def server(input, output, session): 
    pass


app = App(ui=app_ui, server=server)

Let’s see what it looks like:

Image 4 - Python for Shiny app with UI elements

Image 4 – Python for Shiny app with UI elements

The UI elements are ever so slightly different between the languages. Other than that, Python’s naming convention is much more consistent. For example, all input elements start with the input_ keyword. That’s not the case in R, where the same keyword is put at the end of the function name.

As before, it’s just personal preference, but it might be faster for beginners to create user inputs in Python, as code completion will work much better.

Server Logic – Is R Shiny Better than Shiny for Python?

An application without interactive components isn’t an application – it’s just a collection of UI elements. It’s essential for any Shiny developer to learn how to make their apps reactive by working with data, rendering text, plots, tables, and so on.

In this section, we’ll show you how to draw a histogram of 200 data points drawn from a Normal distribution with a mean of 100, and a standard deviation of 15. The number of histogram bins is controlled by the user via a slider.

But what is a Histogram? Here’a complete guide to Histograms in R and ggplot2.

Long story short, this is the code you need to make an interactive R Shiny dashboard:

library(shiny)
library(ggplot2)

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      sliderInput(inputId = "n", label = "N", min = 0, max = 100, value = 20)
    ),
    mainPanel(
      textOutput(outputId = "text_n"),
      plotOutput(outputId = "histogram")
    )
  )
)

server <- function(input, output, session) {
  output$text_n <- renderText({ 
    paste0("Number of bins: ", input$n)
  })
  
  output$histogram <- renderPlot({
    df <- data.frame(x = (100 + 15 * rnorm(200)))
    ggplot(df, aes(x)) + 
      geom_histogram(color = "#000000", fill = "#0099F8", bins = input$n)
  })
}

shinyApp(ui = ui, server = server)

Let’s see what it looks like:

Image 5 - R Shiny dashboard rendering a histogram

Image 5 – R Shiny dashboard rendering a histogram

The procedure is quite simple. You have to call a corresponding rendering function – e.g., renderText() to render text, or renderPlot() to show a chart, and then write an expression inside it. For ggplot2, it’s best to have the data in form of a data frame.

Let’s see how Python compares. We’re using the matplotlib library to render charts, and the entire UI portion is pretty much identical to R.

The difference becomes visible in the server portion. Python uses function decorators to render elements. Here are some rules you’ll have to remember:

  • Use @output decorator any time you want to control what’s rendered in a single element.
  • Use @render.<element-name> decorator to denote which element type you will render.
  • Add a function call below – the function has to be called identically to an ID of the output UI element, so keep that in mind.

That’s the main difference between Python to R, and you’ll quickly get the hang of it. Other than that, you can put any Python code inside the function and return what you want to display.

Here’s the entire code snippet for the app:

from shiny import App, render, ui
import numpy as np
import matplotlib.pyplot as plt


app_ui = ui.page_fluid(
    ui.layout_sidebar(
        ui.panel_sidebar(
            ui.input_slider(id="n", label="N", min=0, max=100, value=20)
        ),
        ui.panel_main(
            ui.output_text(id="text_n"),
            ui.output_plot(id="histogram")
        )
    )
)

def server(input, output, session):
    @output
    @render.text
    def text_n():
        return f"Number of bins: {input.n()}"

    @output
    @render.plot
    def histogram():
        x = 100 + 15 * np.random.randn(200)
        fig, ax = plt.subplots()
        ax.hist(x, bins=input.n(), ec="#000000", color="#0099F8")
        return fig


app = App(ui=app_ui, server=server)

It should look almost identical to the R Shiny version:

Image 6 - Shiny for Python dashboard rendering a histogram

Image 6 – Shiny for Python dashboard rendering a histogram

And it does – with marginal differences between matplotlib and ggplot2, but we won’t get into these.

As the last area of this R Shiny vs. Shiny for Python comparison, we’ll take a look at stylings with CSS.

Styling Shiny Dashboards – Which Language Does it Better?

R Shiny has been around the block for longer, hence it currently has more ways (at least documented ones) to add custom CSS to your dashboards. This doesn’t mean you can’t add CSS to Shiny for Python apps, but you’re a bit limited at this point in time.

For R Shiny, we’ll create a www folder and create the following main.css file inside it:

@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;700&display=swap');

* {
    margin: 0;
    padding: 0;
    box-sizing: border-box;
    font-family: 'Poppins', sans-serif;
    font-size: 2rem;
    background-color: #EEEEEE;
}

.container-fluid > .row {
    display: flex;
    flex-direction: column;
    align-items: center;
}

The CSS file resets the padding/margins, changes the default font, and centers every element. To change the theme of the R Shiny app, simply specify the theme parameter in the fluidPage() method:

ui <- fluidPage(
  theme = "main.css",
  sidebarLayout(
    sidebarPanel(
      sliderInput(inputId = "n", label = "N", min = 0, max = 100, value = 20)
    ),
    mainPanel(
      textOutput(outputId = "text_n"),
      plotOutput(outputId = "histogram")
    )
  )
)

The styled R Shiny app looks like this:

Image 7 - Styled R Shiny app

Image 7 – Styled R Shiny app

It’s a bit different story with Shiny for Python. As of now, there’s no theme parameter for page_fluid() function, nor does adding a link to the file work.

The only currently viable option is to add CSS styles directly to the app UI:

app_ui = ui.page_fluid(
    ui.tags.head(
        ui.tags.style("""
            @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;700&display=swap');

            * {
                margin: 0;
                padding: 0;
                box-sizing: border-box;
                font-family: 'Poppins', sans-serif;
                font-size: 2rem;
                background-color: #EEEEEE;
            }

            .container-fluid > .row {
                display: flex;
                flex-direction: column;
                align-items: center;
            }
        """)
    ),
    ui.layout_sidebar(
        ui.panel_sidebar(
            ui.input_slider(id="n", label="N", min=0, max=100, value=20)
        ),
        ui.panel_main(
            ui.output_text(id="text_n"),
            ui.output_plot(id="histogram")
        )
    ),
)

Here’s the corresponding app:

Image 8 - Styled Shiny for Python app

Image 8 – Styled Shiny for Python app

It’s not the cleanest solution, especially because CSS files tend to have thousands of lines, but it’s something we’ll have to deal with in these alpha releases.


Summary of R Shiny vs. Shiny for Python

And that does it for our R Shiny vs. Shiny for Python comparison. We’ve compared the two in four areas, and can conclude they’re pretty much identical, except for the following:

  • Shiny for Python packs a much more consistent naming convention for specifying inputs.
  • R Shiny is currently easier to style with CSS.
  • Server/reactive functionality involves a bit more code in Python due to function decorators.

Other than that, the two are nearly identical, at least in this alpha release of Shiny for Python.

What are your thoughts on Shiny for Python? Have you encountered any other shortcomings when compared to R? Please let us know in the comment section below.


Original article sourced at: https://appsilon.com

#python 

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Buddha Community

Difference Between R Shiny Vs Shiny for Python

CSharp REPL: A Command Line C# REPL with Syntax Highlighting

C# REPL

A cross-platform command line REPL for the rapid experimentation and exploration of C#. It supports intellisense, installing NuGet packages, and referencing local .NET projects and assemblies.

C# REPL screenshot 

(click to view animation)

C# REPL provides the following features:

  • Syntax highlighting via ANSI escape sequences
  • Intellisense with fly-out documentation
  • Nuget package installation
  • Reference local assemblies, solutions, and projects
  • Navigate to source via Source Link
  • IL disassembly (both Debug and Release mode)
  • Fast and flicker-free rendering. A "diff" algorithm is used to only render what's changed.

Installation

C# REPL is a .NET 6 global tool, and runs on Windows 10, Mac OS, and Linux. It can be installed via:

dotnet tool install -g csharprepl

If you're running on Mac OS Catalina (10.15) or later, make sure you follow any additional directions printed to the screen. You may need to update your PATH variable in order to use .NET global tools.

After installation is complete, run csharprepl to begin. C# REPL can be updated via dotnet tool update -g csharprepl.

Usage:

Run csharprepl from the command line to begin an interactive session. The default colorscheme uses the color palette defined by your terminal, but these colors can be changed using a theme.json file provided as a command line argument.

Evaluating Code

Type some C# into the prompt and press Enter to run it. The result, if any, will be printed:

> Console.WriteLine("Hello World")
Hello World

> DateTime.Now.AddDays(8)
[6/7/2021 5:13:00 PM]

To evaluate multiple lines of code, use Shift+Enter to insert a newline:

> var x = 5;
  var y = 8;
  x * y
40

Additionally, if the statement is not a "complete statement" a newline will automatically be inserted when Enter is pressed. For example, in the below code, the first line is not a syntactically complete statement, so when we press enter we'll go down to a new line:

> if (x == 5)
  | // caret position, after we press Enter on Line 1

Finally, pressing Ctrl+Enter will show a "detailed view" of the result. For example, for the DateTime.Now expression below, on the first line we pressed Enter, and on the second line we pressed Ctrl+Enter to view more detailed output:

> DateTime.Now // Pressing Enter shows a reasonable representation
[5/30/2021 5:13:00 PM]

> DateTime.Now // Pressing Ctrl+Enter shows a detailed representation
[5/30/2021 5:13:00 PM] {
  Date: [5/30/2021 12:00:00 AM],
  Day: 30,
  DayOfWeek: Sunday,
  DayOfYear: 150,
  Hour: 17,
  InternalKind: 9223372036854775808,
  InternalTicks: 637579915804530992,
  Kind: Local,
  Millisecond: 453,
  Minute: 13,
  Month: 5,
  Second: 0,
  Ticks: 637579915804530992,
  TimeOfDay: [17:13:00.4530992],
  Year: 2021,
  _dateData: 9860951952659306800
}

A note on semicolons: C# expressions do not require semicolons, but statements do. If a statement is missing a required semicolon, a newline will be added instead of trying to run the syntatically incomplete statement; simply type the semicolon to complete the statement.

> var now = DateTime.Now; // assignment statement, semicolon required

> DateTime.Now.AddDays(8) // expression, we don't need a semicolon
[6/7/2021 5:03:05 PM]

Keyboard Shortcuts

  • Basic Usage
    • Ctrl+C - Cancel current line
    • Ctrl+L - Clear screen
    • Enter - Evaluate the current line if it's a syntactically complete statement; otherwise add a newline
    • Control+Enter - Evaluate the current line, and return a more detailed representation of the result
    • Shift+Enter - Insert a new line (this does not currently work on Linux or Mac OS; Hopefully this will work in .NET 7)
    • Ctrl+Shift+C - Copy current line to clipboard
    • Ctrl+V, Shift+Insert, and Ctrl+Shift+V - Paste text to prompt. Automatically trims leading indent
  • Code Actions
    • F1 - Opens the MSDN documentation for the class/method under the caret (example)
    • F9 - Shows the IL (intermediate language) for the current statement in Debug mode.
    • Ctrl+F9 - Shows the IL for the current statement with Release mode optimizations.
    • F12 - Opens the source code in the browser for the class/method under the caret, if the assembly supports Source Link.
  • Autocompletion
    • Ctrl+Space - Open autocomplete menu. If there's a single option, pressing Ctrl+Space again will select the option
    • Enter, Right Arrow, Tab - Select active autocompletion option
    • Escape - closes autocomplete menu
  • Text Navigation
    • Home and End - Navigate to beginning of a single line and end of a single line, respectively
    • Ctrl+Home and Ctrl+End - Navigate to beginning of line and end across multiple lines in a multiline prompt, respectively
    • Arrows - Navigate characters within text
    • Ctrl+Arrows - Navigate words within text
    • Ctrl+Backspace - Delete previous word
    • Ctrl+Delete - Delete next word

Adding References

Use the #r command to add assembly or nuget references.

  • For assembly references, run #r "AssemblyName" or #r "path/to/assembly.dll"
  • For project references, run #r "path/to/project.csproj". Solution files (.sln) can also be referenced.
  • For nuget references, run #r "nuget: PackageName" to install the latest version of a package, or #r "nuget: PackageName, 13.0.5" to install a specific version (13.0.5 in this case).

Installing nuget packages

To run ASP.NET applications inside the REPL, start the csharprepl application with the --framework parameter, specifying the Microsoft.AspNetCore.App shared framework. Then, use the above #r command to reference the application DLL. See the Command Line Configuration section below for more details.

csharprepl --framework  Microsoft.AspNetCore.App

Command Line Configuration

The C# REPL supports multiple configuration flags to control startup, behavior, and appearance:

csharprepl [OPTIONS] [response-file.rsp] [script-file.csx] [-- <additional-arguments>]

Supported options are:

  • OPTIONS:
    • -r <dll> or --reference <dll>: Reference an assembly, project file, or nuget package. Can be specified multiple times. Uses the same syntax as #r statements inside the REPL. For example, csharprepl -r "nuget:Newtonsoft.Json" "path/to/myproj.csproj"
      • When an assembly or project is referenced, assemblies in the containing directory will be added to the assembly search path. This means that you don't need to manually add references to all of your assembly's dependencies (e.g. other references and nuget packages). Referencing the main entry assembly is enough.
    • -u <namespace> or --using <namespace>: Add a using statement. Can be specified multiple times.
    • -f <framework> or --framework <framework>: Reference a shared framework. The available shared frameworks depends on the local .NET installation, and can be useful when running an ASP.NET application from the REPL. Example frameworks are:
      • Microsoft.NETCore.App (default)
      • Microsoft.AspNetCore.All
      • Microsoft.AspNetCore.App
      • Microsoft.WindowsDesktop.App
    • -t <theme.json> or --theme <theme.json>: Read a theme file for syntax highlighting. This theme file associates C# syntax classifications with colors. The color values can be full RGB, or ANSI color names (defined in your terminal's theme). The NO_COLOR standard is supported.
    • --trace: Produce a trace file in the current directory that logs CSharpRepl internals. Useful for CSharpRepl bug reports.
    • -v or --version: Show version number and exit.
    • -h or --help: Show help and exit.
  • response-file.rsp: A filepath of an .rsp file, containing any of the above command line options.
  • script-file.csx: A filepath of a .csx file, containing lines of C# to evaluate before starting the REPL. Arguments to this script can be passed as <additional-arguments>, after a double hyphen (--), and will be available in a global args variable.

If you have dotnet-suggest enabled, all options can be tab-completed, including values provided to --framework and .NET namespaces provided to --using.

Integrating with other software

C# REPL is a standalone software application, but it can be useful to integrate it with other developer tools:

Windows Terminal

To add C# REPL as a menu entry in Windows Terminal, add the following profile to Windows Terminal's settings.json configuration file (under the JSON property profiles.list):

{
    "name": "C# REPL",
    "commandline": "csharprepl"
},

To get the exact colors shown in the screenshots in this README, install the Windows Terminal Dracula theme.

Visual Studio Code

To use the C# REPL with Visual Studio Code, simply run the csharprepl command in the Visual Studio Code terminal. To send commands to the REPL, use the built-in Terminal: Run Selected Text In Active Terminal command from the Command Palette (workbench.action.terminal.runSelectedText).

Visual Studio Code screenshot

Windows OS

To add the C# REPL to the Windows Start Menu for quick access, you can run the following PowerShell command, which will start C# REPL in Windows Terminal:

$shell = New-Object -ComObject WScript.Shell
$shortcut = $shell.CreateShortcut("$env:appdata\Microsoft\Windows\Start Menu\Programs\csharprepl.lnk")
$shortcut.TargetPath = "wt.exe"
$shortcut.Arguments = "-w 0 nt csharprepl.exe"
$shortcut.Save()

You may also wish to add a shorter alias for C# REPL, which can be done by creating a .cmd file somewhere on your path. For example, put the following contents in C:\Users\username\.dotnet\tools\csr.cmd:

wt -w 0 nt csharprepl

This will allow you to launch C# REPL by running csr from anywhere that accepts Windows commands, like the Window Run dialog.

Comparison with other REPLs

This project is far from being the first REPL for C#. Here are some other projects; if this project doesn't suit you, another one might!

Visual Studio's C# Interactive pane is full-featured (it has syntax highlighting and intellisense) and is part of Visual Studio. This deep integration with Visual Studio is both a benefit from a workflow perspective, and a drawback as it's not cross-platform. As far as I know, the C# Interactive pane does not support NuGet packages or navigating to documentation/source code. Subjectively, it does not follow typical command line keybindings, so can feel a bit foreign.

csi.exe ships with C# and is a command line REPL. It's great because it's a cross platform REPL that comes out of the box, but it doesn't support syntax highlighting or autocompletion.

dotnet script allows you to run C# scripts from the command line. It has a REPL built-in, but the predominant focus seems to be as a script runner. It's a great tool, though, and has a strong community following.

dotnet interactive is a tool from Microsoft that creates a Jupyter notebook for C#, runnable through Visual Studio Code. It also provides a general framework useful for running REPLs.

Download Details:
Author: waf
Source Code: https://github.com/waf/CSharpRepl
License: MPL-2.0 License

#dotnet  #aspdotnet  #csharp 

August  Larson

August Larson

1624422360

R vs Python: What Should Beginners Learn?

Let go of any doubts or confusion, make the right choice and then focus and thrive as a data scientist.

I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.

I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.

Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).

Therefore, the short answer on whether you should learn Python or R is: it depends.

The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.

#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Ananya Gupta

Ananya Gupta

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What Are The Key Differences between Python 2 vs Python 3

Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines “just go with the version your favorite tutorial was written in, and check out the differences later on.”

But what if you are starting a new project and have the choice to pick? I would say there is currently no “right” or “wrong” as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use.

However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project.The its good to join best python training program which help to improve your skills.

What is Python 2?
Python 2 made code development process easier than earlier versions. It implemented technical details of Python Enhancement Proposal (PEP). Python 2.7 (last version in 2.x ) is no longer under development and in 2020 will be discontinued.

What is Python 3?
On December 2008, Python released version 3.0. This version was mainly released to fix problems that exist in Python 2. The nature of these changes is such that Python 3 was incompatible with Python 2.

It is backward incompatible Some features of Python 3 have been backported to Python 2.x versions to make the migration process easy in Python 3.

KEY DIFFERENCE
Python 3 syntax is simpler and easily understandable whereas Python 2 syntax is comparatively difficult to understand.
Python 3 default storing of strings is Unicode whereas Python 2 stores need to define Unicode string value with “u.”
Python 3 value of variables never changes whereas in Python 2 value of the global variable will be changed while using it inside for-loop.
Python 3 exceptions should be enclosed in parenthesis while Python 2 exceptions should be enclosed in notations.
Python 3 rules of ordering comparisons are simplified whereas Python 2 rules of ordering comparison are complex.
Python 3 offers Range() function to perform iterations whereas, In Python 2, the xrange() is used for iterations.

Which Python Version to Use?
When it comes to Python version 2 vs. 3 today, Python 3 is the outright winner. That’s because Python 2 won’t be available after 2020. Mass Python 3 adoption is the clear direction of the future.

After considering declining support for Python 2 programming language and added benefits from upgrades to Python 3, it is always advisable for a new developer to select Python version 3. However, if a job demands Python 2 capabilities, that would be an only compelling reason to use this version.

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