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Top 10 Python IDEs and Code Editors

Over the years, Python language has evolved enormously with the contribution of developers. Python is one of the most popular programming languages. It was designed primarily for server-side web development, software development, evaluation, scripting and artificial intelligence. For this feature Python encloses certain code editors and IDEs that are used for software development say, Python itself. If you are new to programming, learning Python is highly recommended as it is fast, efficient and easy to learn. Python interpreters are available on various operating systems such as Windows, Linux, Mac OS. This article provides a look into code editors and IDEs along with their features, pros and cons and talks about which are the best suited for writing Python codes. But first let us see what are code editors and IDEs. What is a Code Editor? A code editor is built for editing and modifying source code. A standalone text editor is used for writing and editing computer programs. Excellent ones can execute code as well as control a debugger as well as interact with source control systems. Compared to an IDE, a good dedicated code editor is usually smaller and quicker, but is less functional. Typically they are optimized for programming languages. One major feature of a text editor is that they are designed to modify various files and work with whatever language or framework you choose. What is IDE? IDE (Integrated Development Environment) understands the code significantly better than a text editor. It is a program exclusively built for software development. It is designed with a set of tools that all work together:  Text editor  Compiler Build automation Debugging Libraries, and many more to speed up the work.  These tools integrate: An editor designed to frame codes with text formatting, auto-completionetc., build, execution, debugging tools, file management and source and version control. It reduces manual efforts and combines all the equipment in a typical framework. IDE comes with heavy files. Hence, the downloads and installation is quite tedious. IDE requires expertise along with a lot of patience.  How does an IDE and Code editor differ from each other? An IDE is distinctive from code editors in the following ways: Integrated build process:The user does not have to write his own scripts to build apps in an IDE.  File management: IDE has an integrated file management system and deployment tool. It provides support to other framework as well. On the other hand, a Text editor is a simple editor where source code can be edited and it has no other formatting or compiling options. Development Environment: An IDE is mainly used for development purposes as it provides comparatively better features than a text editor. It allows you to write, compile and debug the entire script.  Syntax Highlighting:The editor displays the text message and puts the source code in different colours to improve its readability. Even error messages are displayed in different colours so that the user understands where he has written the wrong code.  Auto completion:It identifies and inserts a common code for the user instantly. This feature acts as an assistance for the programmer. The code suggestion automatically gets displayed.  Debugger: This tool helps the programmer to test and debug the source code of the main program.  Although IDEs have far better features than a Text editor one major significance of Text editor is that it allows modifying all types of files rather than specifying any definite language or types. Features For a good software development, we need code editors and IDEs which help the developer to automate the process of editing, compiling, testing, debugging and much more. Some of the features of these editors are listed below: Good user interface: They allow users to interact and run programs easily. Incredibly fast: Although these IDEs need to import heavy libraries, compile and debug, they offer fast compilation and run time.  Syntax stylizing: Codes are colorized automatically and syntax is highlighted.    Debugging tool: Itruns the code, set breakpoints, examine the variables. Provides good language syntax: IDEs usually work on a specific language but the others are designed for multi-language support. Code editors are designed with multi-language support.  Good source and version control environment: IDEs come with source control feature to keep a track of changes made in source code and other text files during the development of any software. Intelligent code completion:This feature speeds up the coding process by automatically suggesting for incomplete codes. It reduces typos and other common mistakes. Why do we need a good coding environment? For a good software development one seeks a better coding environment. Although features vary from app to app, a definite set of features is required for one. There are many other things involved such as source code control, extension tools, language support etc. Listed below are the core features which make a good coding environment : Retrieve files: All the codes written in an IDE get saved. Also, the programmer can retrieve his code file at the same state where the work is left off. Run within the environment: It should be able to compile and run within the environment where the codes are written. No external file shall be needed to be downloaded for the execution of the programs.  Good Debugging Tool: An IDE or editor should be able to diagnose and  troubleshoot the programmer’s works and highlight the lines with errors if any. A pop-up window should display the error message. This way the programmer can keep a track of his errands and diagnose them.   Automatic formatting tool: Indentation is done automatically as soon as the programmer moves onto the next line. It keeps the code clean and readable. Quick highlighting: keywords, variables and symbols are highlighted. This feature keeps the code clean and easy to understand. Also, pops up the variables making them easy to spot. This makes it a whole lot easier to pick out portions of code than simply looking at a wall of undifferentiated text. Some of the IDEs and code editors There are various Python IDEs and text editors. Some of the IDEs and text editors along with their features and pros and cons are mentioned below: IDLEKey Features: It is an open source IDE entirely written in Python. It is mainly supported by WINDOWS, LINUX, MAC OS etc.. IDLE is a decent IDE for learning because it is lightweight and quite simple to use. IDLE is installed by default as soon as installation of Python is complete. This makes it easier to get started in Python. IDLE features include the Python shell window(interactive interpreter), auto-completion, syntax highlighting, smart indentation, and a basic integrated debugger. It is however not suitable for the completion of larger projects and best suitable for educational purposes only.  Pros A cross-platform where a developer can search within any window, search through multiple files and replace within the windows editor  Supports syntax highlighting, auto code completion, smart indentation and editable configurations Includes Python shell with highlighter Powerful Integrated Debugger with continuous breakpoints, global view, and local spaces Improves the performance  Call stack visibility Increases the flexibility for developers Cons Used for programming just for beginners Limited to handle normal usage issues. Supports basic design  Large software development cannot be handled  Sublime text Key Features: It is a source code editor, supported on all platforms. It is a very popular cross-platform  and a better text editor. It possesses a built-in support for Python for code editing and packages to extend the syntax and editing features. All Sublime Text packages are written in Python and also a Python API. Installation of the packages often requires you to execute scripts directly in Sublime Text. it is designed to support huge programming and markup languages. Additional functions can be applied by the user with the help of plugins.  Pros More reliable for developers and is cross-platform Supports GOTO anything to access files  Generates wide index of each method, class, and function. AllowsUser interface toolkit Easy navigation to words or symbols Multiple selections to change things at one time Offers command palette to sort, edit and modify the syntax and maintain the indentation.  Offers powerful API and package ecosystem Great performance Highly customizable Allows split editing and instant project switch  Better compatibility with language grammar Custom selection on specific projects Cons Not free Installation of extensions is quite tricky Does not support for direct executing or debugging code from within the editor Less active GIT plugin AtomKey Features: It is an open source code editor developed by Github. It is supported on all platforms. It has features similar to that of Python. It has a framework based on atom shells which help to achieve cross platform functionality. With a sleek interface, file system browser, and marketplace for extensions, it offers a framework for creating desktop applications using JavaScript, HTML, CSS . Extensions can be installed when Atom is running.It enables support for third party packages. Its major feature is that although it is a code editor,it can also be used as an IDE. It is also used for educational purposes. Atom is being improvised day by day, striving to make the user experience rewarding and not remain confined to beginners use only.  Pros Cross-platform  Smooth editing Improves performance of its users Offers built-in package manager and file system browser Faster scripting  Offers smart auto-completion  Smart and flexible Supports multiple pane features Easy navigation across an application Simple to use Allows user interface customization Full support from GitHub Quick access to data and information Cons For beginners only Tedious for sorting configurations and plugins Clumsy tabs reduce performance  Slow loading Runs on JavaScript process  Built on Electron, does not run as a native application VimKey Features: Categorized as a stable open source code editor, VI and VIM are modal editors. As it is supported on almost every platform such as: Windows, LINUX, MAC OS, IOS, Android, UNIX, AmigaOS, MorphOS etc. it is highly configurable. Because of its modal mode of operation, it differs from most other text editors. It possesses three basic modes: insert mode, normal or command mode and command line mode. It is easily customized by the addition of extensions and configuration which makes it easily adaptable for Python development.  Pros Free and easily accessible Customizable and persistent  Has a multi-level undo tree  Extensions are added manually Configuration file is modified Multi-buffers support simultaneous file editing Automated indentation  Good user interface Recognition and conversion of file formats Exclusive libraries including wide range of languages Comes with own scripting language with powerful integration, search and replace functionality Extensive system of plugins Allows debugging and refactoring  Provides two different modes to work: normal and editing mode Strings in VIM can be saved and reused  Cons Used as a text editor only No different color for the pop-up option Not good for beginners PyDev Key Features: It is also categorized as an open source IDE mainly written with JAVA.Since it is an eclipse plugin, the Java IDE is transformed into Python IDE. Its integration with Django gives a Python framework. It also has keyword auto-completion, good debugging tool, syntax highlighting and indentation. Pros Free open source Robust IDE feature set Auto-completion of codes and analysis Smart indentation Interactive console shortcuts Integrated with Django configuration  Platform independent Cons: User interface is not great  Visual studioKey Features: It is categorized as an IDE, is a full-featured IDE developed by Microsoft. It is compatible with Windows and Mac OS only and comes with free as well as paid versions. It has its own marketplace for extensions. PTVS(Python Tools for Visual Studio) offers various features as in coding for Python development, IntelliSense, debugging, refactoring etc. Pros Easy and less tedious installation for development purposes Cons Spacious files  Not supported on Linux Visual studio code Key Features: VS code is a code editor and is way more different from VS. It is a free open source code editor developed by Microsoft can be run on platforms such as Windows, Linux and Mac OS X.  It has a full-featured editor that is highly configurable with Python compatibility for software development. Python tools can be added to enable coding in Python.VS code is integrated with Git which promotes it to perform operations like push, commit directly from the editor itself. It also has electron framework for Node JS applications running on the Blink browser engine. It is enclosed with smart code completion with function definition, imported modules and variable types. Apart from these, VS code also comes with syntax highlighting, a debugging console and proprietary IntelliSense code auto completion. After installing Python, VS code recognizes Python files and libraries immediately.  Pros Free and available on every platform  Small, light-weight but highly extensible Huge compatibility Has a powerful code management system Enables debugging from the editor Multi-language support  Extensive libraries Smart user interface and an acceptable layout Cons Slow search engine Tedious launch time Not a native app just like Atom WingKey Features: Wing is also one of the powerful IDEs today and comes with a lot of good features. It is an open source IDE used commercially. It also is constituted with a strong framework and has a strong debugger and smart editor for Python development making it fast, accurate and fun to perform. It comes with a 30 day trial version. It supports text driven development with unit test, PyTest and Django testing framework.  Pros Open source Find and go-to definition Customizable and extensible Auto-code completion Quick Troubleshoot  Source browser shows all the variables used in the script Powerful debugger  Good refactoring  Cons Not capable of supporting dark themes Wing interface is quite intimidating Commercial version is expensive Python-specific IDEs and Editors Anaconda - Jupyter NotebooksKey Features: It is also an open source IDE with a server-client structure, used to create and edit the codes of a Python. Once it is saved, you can share live code equations, visualizations and text. It has anaconda distribution i.e., libraries are preinstalled so downloading the anaconda itself does the task. It supports Python and R language which are installed by default at installation.  This IDE is again used for data science learning. Quite easy to use, it is not just used as an editor but also as an educational tool or presentation. It supports numerical simulation, machine  learning visualization and statistical modelling. Pros Free Open source  Good user interface Server-client structure Educational tool- Data science, Machine learning  Supports numerical simulation  Enables to create, write, edit and insert images Combines code, text and images Integrated libraries - Matplotlib, NumPy, Pandas Multi-language support Auto code completion Cons Sometimes slow loading is experienced Google Colaboratory Key Features: It is the simplest web IDE used for Python. It gives a free GPU access. Instead of downloading heavy files and tedious launch time, one can directly update the files from Colab to the drive. All you need to do is log in to your google account and open Colab. There is no need for extra setup. Unlike other IDEs no files are required to download. Google provides free computation resources with Colaboratory. It is designed for creating machine learning models. For compilation and execution, all you need to do is to update Python package and get started.   Pros Available to all Code can be run without any interruption Highly user interactive No heavy file downloads Integrated libraries Multi-language support Updated in google drive Update the Python package for execution  Runs on cloud Comments can be added in cells Can import Jupiter or IPython notebooks Cons  All colaboratory files are to be stored in google drive Install all specific libraries No access to unsaved files once the session is over Pycharm Key Features: Developed by Jet Brains and one of the widely used full-featured Python IDE, this is a cross-platform IDE for Python programming and  is well-integrated with Python console and IPython Notebook. It is supported by Windows, Linux, Mac OS and other platforms as well. It has massive productivity and saves ample amount of time. It comes with smart code navigation, code editor, good debugging tool, quick refactoring etc. and supports Python web development frameworks such as Angular JS, JavaScript, CSS, HTML  and live editing functions. The paid version offers advanced features such as full database management and a multitude Framework than the community version such as Django, Flask, Google App, Engine, Pyramid and web2py. Pros Great supportive community Brilliant performance. Amazing editing tools Robust debugging tool Smart code navigation Quick and safe refactoring  Built in developer tools Error detection and fix up suggestions Customizable interface Available in free and paid version Cons Slow loading  Installation is quite difficult and may hang up in between SpyderKey Features: It is an open source IDE supported on all platforms. Ranked as one of the best Python compilers, it supports syntax highlighting, auto completion of codes just like Pycharm. It offers an advanced level of editing, debugging, quick diagnose, troubleshoot and many data exploration features. To get started with Spyder, one needs to install anaconda distribution which is basically used in data science and machine learning. Just like Pycharm it has IntelliSense auto-completion of code. Spyder is built on a structured and powerful framework which makes it one of the best IDE used so far. It is most commonly used for scientific development. Pros Free open source IDE Quick troubleshoot Active framework Smart editing and debugging Syntax is automatically highlighted Auto completion of codes Good for data science and machine learning Structured framework Integrates common Python data science libraries like SciPy, NumPy, and Matplotlib Finds and eliminates bottlenecks Explores and edits variables directly from GUI  Performs well in multi-language editor and auto completion mode Cons Spyder is not capable to configure a specific warning Too many plugins degrades its performance ThonnyKey Features: Thonny is another IDE best suited for beginners for Python development and provides a good virtual environment. It is supported on all platforms. It gives a simple debugger with F5, F6 and F7 keys for debugging. Also, Thonny supports highlighting errors, good representation of function calls, auto code completion and smart indentation. It even allows the developers to configure their code and shell commands. by default,  in Thonny Python is pre-installed as it downloads with its own version of Python.  Pros Simple Graphical user interface.  Free open source IDE Best for beginners Simple debugger with F5, F6, F7 Keys Tackles issues with Python interpreters Highlights syntax error Auto-completion of code Good representation of function calls User can change reference mode easily Step through expression evaluation Reply and resolve to comments Cons Interface is not that good for developers Confined to text editing No template support Slow plugin creation Too basic IDE for software development Which Python IDE is right for you? Requirements vary from programmer to programmer. It is one’s own choice to pick the right tool that is best suited for the task at hand. Beginners need to use a simple tool with few customizations whereas experts require tools with advanced features to bring new updates. Few suggestions are listed below:- Beginners should start with IDLE and Thonny as they do not have complex features and are pretty easy to learn. For data science learners Jupyter Notebooks and Google Colaboratory is preferred. Generally, large scale enterprises prefer the paid versions of IDEs like PyCharm, Atom, Sublime Text etc. in order to get extensive service support from the company. Also, they provide easy finance options and manpower. On the other hand, middle and small scale enterprises tend to look for open source tools which provides them with excellent features. Some of such IDEs are Spyder, Pydev, IDLE and Visual Studio. Conclusion Today, Python stands out as one of the most popular programming languages worldwide. IDE being a program dedicated to software development has made it easier for developers to build, execute, and debug their codes. Code editors can only be used for editing codes whereas an IDE is a feature rich editor which has inbuilt text editor, compiler, debugging tool and libraries. Different IDEs and code editors are detailed in this article along with their merits and demerits. Some are suitable for beginners because of their lightweight nature and simplicity like IDLE, Thonny whereas experts require advance featured ones for building software.  For learning purposes say data science, machine learning Jupyter and Google Colaboratory are strongly recommended. Again there are large scale enterprises who prefer PyCharm, Atom, Sublime Text for software development. On the other hand, small scale enterprises prefer Spyder, Pydev, IDLE and Visual Studio. Hence,the type of IDE or code editor that should be used completely depends upon the requirement of the programmer . To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course. 
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Top 10 Python IDEs and Code Editors

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Top 10 Python IDEs and Code Editors

Over the years, Python language has evolved enormously with the contribution of developers. Python is one of the most popular programming languages. It was designed primarily for server-side web development, software development, evaluation, scripting and artificial intelligence. For this feature Python encloses certain code editors and IDEs that are used for software development say, Python itself. If you are new to programming, learning Python is highly recommended as it is fast, efficient and easy to learn. Python interpreters are available on various operating systems such as Windows, Linux, Mac OS. This article provides a look into code editors and IDEs along with their features, pros and cons and talks about which are the best suited for writing Python codes. But first let us see what are code editors and IDEs. 

What is a Code Editor? 

A code editor is built for editing and modifying source code. A standalone text editor is used for writing and editing computer programs. Excellent ones can execute code as well as control a debugger as well as interact with source control systems. Compared to an IDE, a good dedicated code editor is usually smaller and quicker, but is less functional. Typically they are optimized for programming languages. One major feature of a text editor is that they are designed to modify various files and work with whatever language or framework you choose. 

What is IDE? 

IDE (Integrated Development Environment) understands the code significantly better than a text editor. It is a program exclusively built for software development. It is designed with a set of tools that all work together:  

  • Text editor  
  • Compiler 
  • Build automation 
  • Debugging 
  • Libraries, and many more to speed up the work.  

These tools integrate: An editor designed to frame codes with text formatting, auto-completionetc., build, execution, debugging tools, file management and source and version control. It reduces manual efforts and combines all the equipment in a typical framework. IDE comes with heavy files. Hence, the downloads and installation is quite tedious. IDE requires expertise along with a lot of patience.  

How does an IDE and Code editor differ from each other? 

An IDE is distinctive from code editors in the following ways: 

  • Integrated build process:The user does not have to write his own scripts to build apps in an IDE.  
  • File management: IDE has an integrated file management system and deployment tool. It provides support to other framework as well. On the other hand, a Text editor is a simple editor where source code can be edited and it has no other formatting or compiling options. 
  • Development Environment: An IDE is mainly used for development purposes as it provides comparatively better features than a text editor. It allows you to write, compile and debug the entire script.  
  • Syntax Highlighting:The editor displays the text message and puts the source code in different colours to improve its readability. Even error messages are displayed in different colours so that the user understands where he has written the wrong code.  
  • Auto completion:It identifies and inserts a common code for the user instantly. This feature acts as an assistance for the programmer. The code suggestion automatically gets displayed.  
  • Debugger: This tool helps the programmer to test and debug the source code of the main program.  

Although IDEs have far better features than a Text editor one major significance of Text editor is that it allows modifying all types of files rather than specifying any definite language or types. 

Features 

For a good software development, we need code editors and IDEs which help the developer to automate the process of editing, compiling, testing, debugging and much more. Some of the features of these editors are listed below: 

  • Good user interface: They allow users to interact and run programs easily. 
  • Incredibly fast: Although these IDEs need to import heavy libraries, compile and debug, they offer fast compilation and run time.  
  • Syntax stylizing: Codes are colorized automatically and syntax is highlighted.    
  • Debugging tool: Itruns the code, set breakpoints, examine the variables. 
  • Provides good language syntax: IDEs usually work on a specific language but the others are designed for multi-language support. Code editors are designed with multi-language support.  
  • Good source and version control environment: IDEs come with source control feature to keep a track of changes made in source code and other text files during the development of any software. 
  • Intelligent code completion:This feature speeds up the coding process by automatically suggesting for incomplete codes. It reduces typos and other common mistakes. 

Why do we need a good coding environment? 

For a good software development one seeks a better coding environment. Although features vary from app to app, a definite set of features is required for one. There are many other things involved such as source code control, extension tools, language support etc. Listed below are the core features which make a good coding environment : 

  • Retrieve files: All the codes written in an IDE get saved. Also, the programmer can retrieve his code file at the same state where the work is left off. 
  • Run within the environment: It should be able to compile and run within the environment where the codes are written. No external file shall be needed to be downloaded for the execution of the programs.  
  • Good Debugging Tool: An IDE or editor should be able to diagnose and  troubleshoot the programmer’s works and highlight the lines with errors if any. A pop-up window should display the error message. This way the programmer can keep a track of his errands and diagnose them.   
  • Automatic formatting tool: Indentation is done automatically as soon as the programmer moves onto the next line. It keeps the code clean and readable. 
  • Quick highlighting: keywords, variables and symbols are highlighted. This feature keeps the code clean and easy to understand. Also, pops up the variables making them easy to spot. This makes it a whole lot easier to pick out portions of code than simply looking at a wall of undifferentiated text. 

Some of the IDEs and code editors 

There are various Python IDEs and text editors. Some of the IDEs and text editors along with their features and pros and cons are mentioned below: 

IDLE

IDLEKey Features: It is an open source IDE entirely written in Python. It is mainly supported by WINDOWS, LINUX, MAC OS etc.. IDLE is a decent IDE for learning because it is lightweight and quite simple to use. IDLE is installed by default as soon as installation of Python is complete. This makes it easier to get started in Python. IDLE features include the Python shell window(interactive interpreter), auto-completion, syntax highlighting, smart indentation, and a basic integrated debugger. It is however not suitable for the completion of larger projects and best suitable for educational purposes only.  

Pros 

  • A cross-platform where a developer can search within any window, search through multiple files and replace within the windows editor  
  • Supports syntax highlighting, auto code completion, smart indentation and editable configurations 
  • Includes Python shell with highlighter 
  • Powerful Integrated Debugger with continuous breakpoints, global view, and local spaces 
  • Improves the performance  
  • Call stack visibility 
  • Increases the flexibility for developers 

Cons 

  • Used for programming just for beginners 
  • Limited to handle normal usage issues. 
  • Supports basic design  
  • Large software development cannot be handled  

Sublime text 

Sublime text

Key Features: It is a source code editor, supported on all platforms. It is a very popular cross-platform  and a better text editor. It possesses a built-in support for Python for code editing and packages to extend the syntax and editing features. All Sublime Text packages are written in Python and also a Python API. Installation of the packages often requires you to execute scripts directly in Sublime Text. it is designed to support huge programming and markup languages. Additional functions can be applied by the user with the help of plugins.  

Pros 

  • More reliable for developers and is cross-platform 
  • Supports GOTO anything to access files  
  • Generates wide index of each method, class, and function. 
  • AllowsUser interface toolkit 
  • Easy navigation to words or symbols 
  • Multiple selections to change things at one time 
  • Offers command palette to sort, edit and modify the syntax and maintain the indentation.  
  • Offers powerful API and package ecosystem 
  • Great performance 
  • Highly customizable 
  • Allows split editing and instant project switch  
  • Better compatibility with language grammar 
  • Custom selection on specific projects 

Cons 

  • Not free 
  • Installation of extensions is quite tricky 
  • Does not support for direct executing or debugging code from within the editor 
  • Less active GIT plugin 

Atom

Atom

Key Features: It is an open source code editor developed by Github. It is supported on all platforms. It has features similar to that of Python. It has a framework based on atom shells which help to achieve cross platform functionality. With a sleek interface, file system browser, and marketplace for extensions, it offers a framework for creating desktop applications using JavaScript, HTML, CSS . Extensions can be installed when Atom is running.It enables support for third party packages. Its major feature is that although it is a code editor,it can also be used as an IDE. It is also used for educational purposes. Atom is being improvised day by day, striving to make the user experience rewarding and not remain confined to beginners use only.  

Pros 

  • Cross-platform  
  • Smooth editing 
  • Improves performance of its users 
  • Offers built-in package manager and file system browser 
  • Faster scripting  
  • Offers smart auto-completion  
  • Smart and flexible 
  • Supports multiple pane features 
  • Easy navigation across an application 
  • Simple to use 
  • Allows user interface customization 
  • Full support from GitHub 
  • Quick access to data and information 

Cons 

  • For beginners only 
  • Tedious for sorting configurations and plugins 
  • Clumsy tabs reduce performance  
  • Slow loading 
  • Runs on JavaScript process  
  • Built on Electron, does not run as a native application 

Vim

Vim

Key Features: Categorized as a stable open source code editor, VI and VIM are modal editors. As it is supported on almost every platform such as: Windows, LINUX, MAC OS, IOS, Android, UNIX, AmigaOS, MorphOS etc. it is highly configurable. Because of its modal mode of operation, it differs from most other text editors. It possesses three basic modes: insert mode, normal or command mode and command line mode. It is easily customized by the addition of extensions and configuration which makes it easily adaptable for Python development.  

Pros 

  • Free and easily accessible 
  • Customizable and persistent  
  • Has a multi-level undo tree  
  • Extensions are added manually 
  • Configuration file is modified 
  • Multi-buffers support simultaneous file editing 
  • Automated indentation  
  • Good user interface 
  • Recognition and conversion of file formats 
  • Exclusive libraries including wide range of languages 
  • Comes with own scripting language with powerful integration, search and replace functionality 
  • Extensive system of plugins 
  • Allows debugging and refactoring  
  • Provides two different modes to work: normal and editing mode 
  • Strings in VIM can be saved and reused  

Cons 

  • Used as a text editor only 
  • No different color for the pop-up option 
  • Not good for beginners 

PyDev 

PyDev

Key Features: It is also categorized as an open source IDE mainly written with JAVA.Since it is an eclipse plugin, the Java IDE is transformed into Python IDE. Its integration with Django gives a Python framework. It also has keyword auto-completion, good debugging tool, syntax highlighting and indentation. 

Pros 

  • Free open source 
  • Robust IDE feature set 
  • Auto-completion of codes and analysis 
  • Smart indentation 
  • Interactive console shortcuts 
  • Integrated with Django configuration  
  • Platform independent 

Cons

  • User interface is not great  

Visual studio

Visual studioKey Features: It is categorized as an IDE, is a full-featured IDE developed by Microsoft. It is compatible with Windows and Mac OS only and comes with free as well as paid versions. It has its own marketplace for extensions. PTVS(Python Tools for Visual Studio) offers various features as in coding for Python development, IntelliSense, debugging, refactoring etc. 

Pros 

  • Easy and less tedious installation for development purposes 

Cons 

  • Spacious files  
  • Not supported on Linux 

Visual studio code 

Visual studio code Key Features: VS code is a code editor and is way more different from VS. It is a free open source code editor developed by Microsoft can be run on platforms such as Windows, Linux and Mac OS X.  It has a full-featured editor that is highly configurable with Python compatibility for software development. Python tools can be added to enable coding in Python.VS code is integrated with Git which promotes it to perform operations like push, commit directly from the editor itself. It also has electron framework for Node JS applications running on the Blink browser engine. It is enclosed with smart code completion with function definition, imported modules and variable types. Apart from these, VS code also comes with syntax highlighting, a debugging console and proprietary IntelliSense code auto completion. After installing Python, VS code recognizes Python files and libraries immediately.  

Pros 

  • Free and available on every platform  
  • Small, light-weight but highly extensible 
  • Huge compatibility 
  • Has a powerful code management system 
  • Enables debugging from the editor 
  • Multi-language support  
  • Extensive libraries 
  • Smart user interface and an acceptable layout 

Cons 

  • Slow search engine 
  • Tedious launch time 
  • Not a native app just like Atom 

Wing

Wing

Key Features: Wing is also one of the powerful IDEs today and comes with a lot of good features. It is an open source IDE used commercially. It also is constituted with a strong framework and has a strong debugger and smart editor for Python development making it fast, accurate and fun to perform. It comes with a 30 day trial version. It supports text driven development with unit test, PyTest and Django testing framework.  

Pros 

  • Open source 
  • Find and go-to definition 
  • Customizable and extensible 
  • Auto-code completion 
  • Quick Troubleshoot  
  • Source browser shows all the variables used in the script 
  • Powerful debugger  
  • Good refactoring  

Cons 

  • Not capable of supporting dark themes 
  • Wing interface is quite intimidating 
  • Commercial version is expensive 

Python-specific IDEs and Editors 

Anaconda - Jupyter Notebooks

Anaconda - Jupyter Notebooks

Key Features: It is also an open source IDE with a server-client structure, used to create and edit the codes of a Python. Once it is saved, you can share live code equations, visualizations and text. It has anaconda distribution i.e., libraries are preinstalled so downloading the anaconda itself does the task. It supports Python and R language which are installed by default at installation.  This IDE is again used for data science learning. Quite easy to use, it is not just used as an editor but also as an educational tool or presentation. It supports numerical simulation, machine  learning visualization and statistical modelling. 

Pros 

  • Free Open source  
  • Good user interface 
  • Server-client structure 
  • Educational tool- Data science, Machine learning  
  • Supports numerical simulation  
  • Enables to create, write, edit and insert images 
  • Combines code, text and images 
  • Integrated libraries - Matplotlib, NumPy, Pandas 
  • Multi-language support 
  • Auto code completion 

Cons 

  • Sometimes slow loading is experienced 

Google Colaboratory 

Google Colaboratory

Key Features: It is the simplest web IDE used for Python. It gives a free GPU access. Instead of downloading heavy files and tedious launch time, one can directly update the files from Colab to the drive. All you need to do is log in to your google account and open Colab. There is no need for extra setup. Unlike other IDEs no files are required to download. Google provides free computation resources with Colaboratory. It is designed for creating machine learning models. For compilation and execution, all you need to do is to update Python package and get started.   

Pros 

  • Available to all 
  • Code can be run without any interruption 
  • Highly user interactive 
  • No heavy file downloads 
  • Integrated libraries 
  • Multi-language support 
  • Updated in google drive 
  • Update the Python package for execution  
  • Runs on cloud 
  • Comments can be added in cells 
  • Can import Jupiter or IPython notebooks 

Cons  

  • All colaboratory files are to be stored in google drive 
  • Install all specific libraries 
  • No access to unsaved files once the session is over 

Pycharm 

Pycharm

Key Features: Developed by Jet Brains and one of the widely used full-featured Python IDE, this is a cross-platform IDE for Python programming and  is well-integrated with Python console and IPython Notebook. It is supported by Windows, Linux, Mac OS and other platforms as well. It has massive productivity and saves ample amount of time. It comes with smart code navigation, code editor, good debugging tool, quick refactoring etc. and supports Python web development frameworks such as Angular JS, JavaScript, CSS, HTML  and live editing functions. The paid version offers advanced features such as full database management and a multitude Framework than the community version such as Django, Flask, Google App, Engine, Pyramid and web2py. 

Pros 

  • Great supportive community 
  • Brilliant performance. 
  • Amazing editing tools 
  • Robust debugging tool 
  • Smart code navigation 
  • Quick and safe refactoring  
  • Built in developer tools 
  • Error detection and fix up suggestions 
  • Customizable interface 
  • Available in free and paid version 

Cons 

  • Slow loading  
  • Installation is quite difficult and may hang up in between 

Spyder

Spyder

Key Features: It is an open source IDE supported on all platforms. Ranked as one of the best Python compilers, it supports syntax highlighting, auto completion of codes just like Pycharm. It offers an advanced level of editing, debugging, quick diagnose, troubleshoot and many data exploration features. To get started with Spyder, one needs to install anaconda distribution which is basically used in data science and machine learning. Just like Pycharm it has IntelliSense auto-completion of code. Spyder is built on a structured and powerful framework which makes it one of the best IDE used so far. It is most commonly used for scientific development. 

Pros 

  • Free open source IDE 
  • Quick troubleshoot 
  • Active framework 
  • Smart editing and debugging 
  • Syntax is automatically highlighted 
  • Auto completion of codes 
  • Good for data science and machine learning 
  • Structured framework 
  • Integrates common Python data science libraries like SciPy, NumPy, and Matplotlib 
  • Finds and eliminates bottlenecks 
  • Explores and edits variables directly from GUI  
  • Performs well in multi-language editor and auto completion mode 

Cons 

  • Spyder is not capable to configure a specific warning 
  • Too many plugins degrades its performance 

Thonny

Thonny

Key Features: Thonny is another IDE best suited for beginners for Python development and provides a good virtual environment. It is supported on all platforms. It gives a simple debugger with F5, F6 and F7 keys for debugging. Also, Thonny supports highlighting errors, good representation of function calls, auto code completion and smart indentation. It even allows the developers to configure their code and shell commands. by default,  in Thonny Python is pre-installed as it downloads with its own version of Python.  

Pros 

  • Simple Graphical user interface.  
  • Free open source IDE 
  • Best for beginners 
  • Simple debugger with F5, F6, F7 Keys 
  • Tackles issues with Python interpreters 
  • Highlights syntax error 
  • Auto-completion of code 
  • Good representation of function calls 
  • User can change reference mode easily 
  • Step through expression evaluation 
  • Reply and resolve to comments 

Cons 

  • Interface is not that good for developers 
  • Confined to text editing 
  • No template support 
  • Slow plugin creation 
  • Too basic IDE for software development 

Which Python IDE is right for you? 

Requirements vary from programmer to programmer. It is one’s own choice to pick the right tool that is best suited for the task at hand. Beginners need to use a simple tool with few customizations whereas experts require tools with advanced features to bring new updates. Few suggestions are listed below:- 

  • Beginners should start with IDLE and Thonny as they do not have complex features and are pretty easy to learn. 
  • For data science learners Jupyter Notebooks and Google Colaboratory is preferred. 

Generally, large scale enterprises prefer the paid versions of IDEs like PyCharm, Atom, Sublime Text etc. in order to get extensive service support from the company. Also, they provide easy finance options and manpower. On the other hand, middle and small scale enterprises tend to look for open source tools which provides them with excellent features. Some of such IDEs are Spyder, Pydev, IDLE and Visual Studio. 

Conclusion 

Today, Python stands out as one of the most popular programming languages worldwide. IDE being a program dedicated to software development has made it easier for developers to build, execute, and debug their codes. Code editors can only be used for editing codes whereas an IDE is a feature rich editor which has inbuilt text editor, compiler, debugging tool and libraries. Different IDEs and code editors are detailed in this article along with their merits and demerits. Some are suitable for beginners because of their lightweight nature and simplicity like IDLE, Thonny whereas experts require advance featured ones for building software.  

For learning purposes say data science, machine learning Jupyter and Google Colaboratory are strongly recommended. Again there are large scale enterprises who prefer PyCharm, Atom, Sublime Text for software development. On the other hand, small scale enterprises prefer Spyder, Pydev, IDLE and Visual Studio. Hence,the type of IDE or code editor that should be used completely depends upon the requirement of the programmer . To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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Scala Vs Python Vs R Vs Java - Which language is better for Spark & Why?

One of the most important decisions for the Big data learners or beginners is choosing the best programming language for big data manipulation and analysis. Just understanding business problems and choosing the right model is not enough but implementing them perfectly is equally important and choosing the right language (or languages) for solving the problem goes a long way. If you search top and highly effective programming languages for Big Data on Google, you will find the following top 4 programming languages: JavaScalaPythonRJavaJava is one of the oldest languages of all 4 programming languages listed here. Traditional Frameworks of Big data like Apache Hadoop and all the tools within its ecosystem are Java-based and hence using java opens up the possibility of utilizing large ecosystem of tools in the big data world.  ScalaA beautiful crossover between object-oriented and functional programming language is Scala. Scala is a highly Scalable Language. Scala was invented by the German Computer Scientist, Martin Odersky and the first version was launched in the year 2003.PythonPython was originally conceptualized by Guido van Rossum in the late 1980s. Initially, it was designed as a response to the ABC programming language and later gained its popularity as a functional language in a big data world. Python has been declared as one of the fastest-growing programming languages in 2018 as per the recently held Stack Overflow Developer Survey. Many data analysis, manipulation, machine learning, deep learning libraries are written in Python and hence it has gained its popularity in the big data ecosystem. It’s a very user-friendly language and it is its biggest advantage.  Fun factPython is not named after the snake. It’s named after the British TV show Monty Python.RR is the language of statistics. R is a language and environment for statistical computing and graphics. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is named partly after the first names of the first two R authors and partly as a play on the name of S*. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.*SS is a statistical programming language developed primarily by John Chambers and R is an implementation of the S programming language combined with lexical scoping semantics, inspired by Scheme.Every framework is implemented in the underlying programming language for its implementation. Ex Zend uses PHP, Panda Framework uses python similarly Hadoop framework uses Java and Spark uses Scala.However, Spark officially supports Java, Scala, Python and R, all 4 languages. If one browses through Apache Spark’s official website documentation, he/she would find many other languages utilized by the open-source community for Spark implementation.    When any developer wants to start learning Spark, the first question he stumbles upon is, out of these pools of languages, which one to use and which one to master? Solution Architects would have a tough time choosing the right language for spark framework and Organizations will always be wondering, which skill sets are relevant for my problem if one doesn’t have the right knowledge about these languages in the context of Spark.    This article will try to answer all these queries.so let’s start-JavaOldest of all and popular, widely adopted programming language of all. There is a number offeatures/advantages due to which Java is favorite for Big data developers and tool creators:Java is platform-agnostic language and hence it can run on almost any system. Java is portable due to something called Java Virtual Machine – JVM. JVM is a foundation of Hadoop ecosystem tools like Map Reduce, Storm, Spark, etc. These tools are written in Java and run on JVM.Java provides various communities support like GitHub and stack overflow etc.Java is scalable, backward compatible, stable and production-ready language. Also, supports a large variety of tried and tested libraries.It is statically typed language (We would see details of this functionality in later sections, in comparison with others)Java is mostly the choice for most of the big data projects but for the Spark framework, one has to ponder upon, whether Java would be the best fit.One major drawback of Java is its verbosity. One has to write long code (number of lines of code) to achieve simple functionality in Java.Java does not support Read-Evaluate-Print-Loop (REPL) which is a major deal-breaker when choosing a programming language for big data processing.ScalaScala is comparatively new to the programming scene but has become popular very quickly. Above are a few quotes from bigger names in the industry for Scala. From the Spark context, many experts prefer Scala over other programming languages as Spark is written in Scala. Scala is the native language of Spark. It means any new API always first be available in Scala.Scala is a hybrid functional programming language because It has both the features of object-oriented programming and functional programming. As an OO Programming Language, it considers every value as an object and all OOPS concepts apply. As a functional programming language, it defines and supports functions. All operations are done as functions. No variable stands by itself. Scala is a machine-compiled language.Scala and Java are popular programming languages that run over JVM. JVM makes these languages framework friendly. One can say, Scala is an advanced level of Java.Features/Advantages of Scala:It’s general-purpose object-oriented language with functional language properties too. It’s less verbose than Java.It can work with JVM and hence is portable.It can support Java APIs comfortably.It's fast and robust in Spark context as its Spark native.It is a statically typed language.Scala supports Read-Evaluate-Print-Loop (REPL)Drawbacks / Downsides of Scala:Scala is complex to learn due to the functional nature of language.Steep learning curve.Lack of matured machine learning languages.PythonPython is one of the de-facto languages of Data Science. It is a simple, open-source, general-purpose language and is very easy to learn. It has a rich set of libraries, utilities, ready-to-use features and support to a number of mature machine learning, big data processing, visualization libraries.Advantages of Python:It is interpreted language (i.e. support to REPL, Read, Evaluate, Print, Loop.) If you type a command into a command-line interpreter and it responds immediately. Java lacks this feature.Easy to learn, easy debugging, fewer lines of code.It is dynamically typed. i.e. can dynamically defined variable types. i.e. Python as a language is type-safe.Python is platform agnostic and scalable.Drawbacks/Disadvantages:Python is slow. Big data professionals find projects built in Java / Scala are faster and robust than the once with python.Whilst using user-defined functions or third party libraries in Python with Spark, processing would be slower as increased processing is involved as Python does not have equivalent Java/Scala native language API for these functionalities.Python does not support heavy weight processing fork() using uWSGI but it does not support true multithreading.R LanguageR is the favourite language of statisticians. R is fondly called a language of statisticians.  It’s popular for research, plotting, and data analysis. Together with RStudio, it makes a killer statistic, plotting, and data analytics application.R is majorly used for building data models to be used for data analysis.Advantages/Features of R:Strong statistical modeling and visualization capabilities.Support for ‘data science’ related work.It can be integrated with Apache Hadoop and Spark easily.Drawbacks/Disadvantages of R:R is not a general-purpose language.The code written in R cannot be directly deployed into production. It needs conversion into Java or Python.Not as fast as Java / Scala.Comparison of four languages for Apache SparkWith the introduction of these 4 languages, let’s now compare these languages for the Spark framework:These languages can be categorized into 2 buckets basis high-level spark architecture support, broadly:JVM Languages: Java and ScalaNon-JVM Languages: Python and RDue to these categorizations, performance may vary. Let’s understand architecture in little depth to understand the performance implications of using these languages. This would also help us to understand the question of when to use which language.Spark Framework High-level architecture An application written in any one of the languages is submitted on the driver node and further driver node distributes the workload by dividing the execution on multiple worker nodes.JVM compatible Application Execution Flow Consider the applications written are JVM compatible (Java/Scala). Now, Spark is also written in native JVM compatible Scala language, hence there is no explicit conversion required at any point of time to execute JVM compatible applications on Spark. Also, this makes the native language applications faster to perform on the Spark framework.There are multiple scenarios for Python/R written applications:Python/R driver talk to JVM driver by socket-based API. On the driver node, both the driver processes are invoked when the application language is non-JVM language.Scenario 1: Applications for which Equivalent Java/Scala Driver API exists - This scenario executes the same way as JVM compatible applications by invoking Java API on the driver node itself. The cost for inter-process communication through sockets is negligible and hence performance is comparable. This is with the assumption that processed data over worker nodes are not to be sent back to the Driver again.Scenario 1(b): If the assumption taken is void in scenario 1 i.e. processed data on worker nodes is to be sent back to driver then there is significant overhead and serialization required. This adds to processing time and hence performance in this scenario deteriorates.Scenario 2: Applications for which Equivalent Java/Scala Driver API do not exist – Ex. UDF (User-defined functions) / Third party python libraries. In such cases equivalent Java API doesn’t exist and hence, additional executor sessions are initiated on worker node and python API is serialized on worker node and executed. This python worker processes in addition to JVM and coordination between them is overhead. Processes also compete for resources which adds to memory contention.In addition, if the data is to send back to the driver node then processing takes a lot of time and problem scales up as volume increases and hence performance is bigger problem.As we have seen a performance, Let’s see the tabular comparison between these languages.Comparison PointsJavaScalaPythonRPerformanceFasterFaster (about 10x faster than Python)SlowerSlowerLearning CurveEasier than JavaTougher than PythonSteep learning curve than Java & PythonEasiestModerateUser GroupsWeb/Hadoop programmersBig Data ProgrammersBeginners & Data EngineersData Scientists/ StatisticiansUsageWeb development and Hadoop NativeSpark NativeData Engineering/ Machine Learning/ Data VisualizationVisualization/ Data Analysis/ Statistics use casesType of LanguageObject-Oriented, General PurposeObject-Oriented & Functional General PurposeGeneral PurposeSpecifically for Data Scientists.Needs conversion into Scala/Python before productizingConcurrencySupport ConcurrencySupport ConcurrencyDoes not Support ConcurrencyNAEase of UseVerboseLesser Verbose than ScalaLeast VerboseNAType SafetyStatically typedStatically typed (except for Spark 2.0 Data frames)Dynamically TypedDynamically TypedInterpreted Language (REPL)NoNoYesYesMaturated machine learning libraries availability/ SupportLimitedLimitedExcellentExcellentVisualization LibrariesLimitedLimitedExcellentExcellentWeb Notebooks SupportIjava Kernel in Jupyter NotebookApache Zeppelin Notebook SupportJupyter Notebook SupportR NotebookWhich language is better for Spark and Why?With the info we gathered for the languages, let's move to the main question i.e. which language to choose for Spark? My answer is not a straightforward single language for this question. I will state my point of view for choosing the proper language: If you are a beginner and want to choose a language from learning Spark perspective. If you are organization/ self employed or looking to answer a question for solutioning a project perspective. I. If you are beginner:If you are a beginner and have no prior education of programming language then Python is the language for you, as it’s easy to pick up. Simple to understand and very user-friendly. It would prove a good starting point for building Spark knowledge further. Also, If you are looking for getting into roles like ‘data engineering’, knowledge of Python along with supported libraries will go a long way. If you are a beginner but have education in programming languages, then you may find Java very familiar and easy to build upon prior knowledge. After all, it grapevine of all the languages.  If you are a hardcore bigdata programmer and love exploring complexities, Scala is the choice for you. It’s complex but experts say if once you love Scala, you will prefer it over other languages anytime.If you are a data scientist, statistician and looking to work with Spark, R is the language for you. R is more science oriented than Python. II. If you are organization/looking for choice of language for implementations:You need to answer the following important questions before choosing the language:Skills and Proficiency: Which skill-sets and proficiency over language, you already have with you/in your team?Design goals and availability of features/ Capability of language: Which libraries give you better support for the type of problem(s) you are trying to solve.Performance implications Details of these explained below: 1. Skillset: This is very straightforward. Whichever is available skill set within a team, go with that to solve your problem, after evaluating answers of other two questions. If you are self-employed, the one you have proficiency is the most likely suitable choice of language.  2. Library Support:  Following gives high-level capabilities of languages:R: Good for research, plotting, and data analysis.Python: Good for small- or medium-scale projects to build models and analyse data, especially for fast start-ups or small teams.Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance.In my opinion, Scala/Java can be used for larger robust projects to ease maintenance. Also, If one wants the app to scale quickly and needs it to be robust, Scala is the choice.Python and R: Python is more universal language than R, but R is more science oriented. Broadly, one can say Python can be implemented for Data engineering use cases and R for Data science-oriented use cases. On the other hand, if you discover these two languages have about the same library support you need, then pick the one whose syntax you prefer. You may find that you need both depending on the situation. 3. Performance: As seen earlier in the article, Scala/ Java is about 10x faster than Python/R as they are JVM supported languages. However, if you are writing Python/R applications wisely (like without using UDFs/ Not sending data back to the Driver etc) they can perform equally well.ConclusionFor learning, depending upon your prior knowledge, Python is the easiest of all to pick up. For implementations, Choice is in your hands which language to choose for implementations but let me tell you one secret or a tip, you don’t have to stick to one language until you finish your project. You can divide your problem in small buckets and utilize the best language to solve the problem. This way, you can achieve balance between optimum performance, availability, proficiency in a skill, and sub-problem at hand.  Do let us know how your experience was in learning the language comparisons and the language you think is better for Spark. Moreover, which one you think is “the one for you”, through comments below.
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What is Context in React? How to use Context in React?

What the hack is Context?Have you ever wondered about passing data or using states in between different components without using Props? Or passing a state from Parent to Child component without manually passing a prop at every level?  Let’s understand with an example below:Here we have a parent component app.js where we have defined our states. We want to access the data of state in the last child which is “Child 1.2” in the below chart.app.js Parent ComponentThe ideal or older approach in React is to pass the data from the root component to the last component is via Props. We have to pass props in each intermediary level so as to send in the last level. While this approach also works, the real problems begin if data is needed on a different branch i.e Child 2.1 or Child 2.2 in above chart…In order to solve this problem, we need to pass data from the root/top level of the application through all the intermediate components to the one where we want to pass the data, even though some intermediate components don't even need it.  This mind-numbing process is known as prop drilling,  Prop Drillingwhere you’re passing the state from your root component to the bottom and you end up passing the data via props through components that do not even necessarily need themOne really good solution to solve the above problem is using Context According to the React documentation:  “Context provides a way to pass data through the component tree without having to pass props down manually at every level”Ordinarily, we’d have used any state management library like Redux or have used HOC’s to pass the data in a tedious manner. But what if we don’t want to use it all? Here comes the role of new Context API!In layman words, it gives an approach to make specific data available to all components throughout the React component tree regardless of how deeply nested those components are.Context is just like a global object to the subtree of the React component.When to use the Context APIThe Context API is convenient for sharing data that is either global, such as setting the header and footer theme of a website or logic of user authentication and many more. In cases like these, we can use the Context API without using any extra library or external modules. It can also be used in a multilingual application where we want to implement multiple languages that can be translated into the required text with the help of ContextAPI. It will save prop-drilling   In fact, in any situation where we have to pass a prop through a component so it reaches another component, inside down the tree is where we can use the Context API.Introducing The Context APIThe context API is a way to pass data from top component to bottom ones, without manually passing it to via props. Context is fundamentally utilized when some data needs to be accessible by numerous components at different nesting levels. To create a new Context, we can use the React createContext function like below: const MyContext = React.createContext(defaultValue);In React, data is often passed from a parent to its child component as a property. Here, we can also omit the default value which we have passed to the context, if needed.React data passing from parent to its child Let’s Get Started With ContextThree things are needed to tap into the power of context: 1. The context itselfTo create a context we can use React.createContext method which creates a context object. This is used to ensure that the components at different level can use the same context to fetch the data.   In React.createContext, we can pass an input parameter as an argument which could be anything or it can be null as well.import React from `react';  const ThemeContext = React.createContext('dark');  // Create our context        export default ThemeContext;In this example, a string is passed for the current Context which is “dark”. So we can say, the current theme required for a specific component is Dark.   Also, we have exported the object so that we can use it in other places. In one app, React also allows you to create multiple contexts. We should always try to separate context for different purposes, so as to maintain the code structure and better readability. We will see that later in our reading.   What next?? Now, to utilize the power of Context in our example, we want to provide this type of theme to all the components.  Context exposes a pair of elements which is a Provider Component and a Consumer Component.2. A context providerOkay, so now we have our Context object. And to make the context available to all our components we have to use a Provider.   But, what is Provider? According to the React documentation:"every context object comes with a Provider React component that allows consuming components to subscribe to context changes"In other words, Provider accepts a prop (value) and the data in this prop can be used in all the other child components. This value could be anything from the component state.// myProvider.js import React from 'react'; import Theme from './theme'; const myProvider = () => ( ...   ); export default myProvider;We can say that a provider acts just like a delivery service.prop finding context and deliverling it to consumerWhen a consumer asks for something, it finds it in the context and delivers it to where it's needed.But wait, who or what is the consumer???3.  A context consumer What is Consumer? A consumer is a place to keep the stored information. It can request for the data using the provider and can even manipulate the global store if the provider allows it. In our previous example, let’s grab the theme value and use it in our Header component. // Header.js   import React from 'react'; import Theme from './theme';   const Header = () => (                        {theme => Selected theme is {theme}}             );   export default Header;Dynamic Context:   We can also change the value of the provider by simply providing a dynamic context. One way of achieving it is by placing the Provider inside the component itself and grabbing the value from component state as below:// Footer.js   import React from 'react';   class Footer extends React.Component {    state = {        theme: 'dark'    };      render() {        return (                                                );    } }Simple, no? We can easily change the value of  the Provider to any Consumer.Consuming Context With Class-based ComponentsWe all pretty know that there are two methods to write components in React, which is Class based components and Function based components. We have already seen a demo of how we can use the power of Context in class based components.  One is to use the context from Consumer like “ThemeContext.Consumer” and the other method is by assigning context object from current Context to contextType property of our class.import React, { Component } from "react"; import MyThemeContext from "../Context/MyThemeContext"; import GlobalTheme from "../theme";   class Main extends Component {    constructor() {        super();    }    static contextType = MyThemeContext;  //assign context to component    render() {        const currentTheme = GlobalTheme[this.context];        return (            ...        );    }   }There is always a difference in how we want to use the Context. We can either provide it outside the render() method or use the Context Consumer as a component itself.  Here in the above example, we have used a static property named as contextType which is used to access the context data. It can be utilized by using this.context. This method however, limits you consuming, only one context at a time.Consuming Context With Functional ComponentsContext with Functional based components is quite easy and less tedious. In this we can access the context value through props with the help of useContext method in React. This hook (useContext) can be passed in as the argument along with our Context to consume the data in the functional component.const value = useContext(MyContext);It accepts a context object and returns the current context value. To read more about hooks, read here.  Our previous example looks like:import React, { useContext } from 'react' import MyThemeContext from './theme-context'   const User = props => {    const context = useContext(MyThemeContext)    return ...Now, instead of wrapping our content in a Consumer component we have access to the theme context state through the ‘context’ variable.But we should avoid using context for keeping the states locally. Instead of  conext, we can use local state there.Use of Multiple ContextsIt may be possible that we want to add multiple contexts in our application. Like holding a theme for the entire app, changing the language based on the location, performing some A/B testing, using global parameters for login or user Profile… For instance, let’s say there is a requirement to keep both Theme context and userInfo Context, the code will look like as:       ...   It’s quite possible in React to hold multiple Contexts, but this definitely hampers rendering, serving ‘n’ number of contexts in ‘m’ component and holding the updated value in each rendered component.To avoid this and to make re-rendering faster, it is suggested to make each context consumer in the tree as a separate node or into different contexts.                 And we can perform the nesting in context as:    {theme => (                    {colour => (                Theme: {theme} and colour: {colour}            )}            )} It’s worth noting that when a value of a context changes in the parent component, the child components or the components’ holding that value should be rerendered or changed. Hence, whenever there is a change in the value of provider, it will cause its consumers to re-render.ConclusionDon’t you think this concept is just amazing?? Writing a global context like theme or language or userProfile and using the data of them directly in the child or other components? Implementing these stateful logic by global preferences was never so easy, but Context made this transportation job a lot simple and achievable! Hope you find this article useful. Happy Coding!Having challenge learning to code? Let our experts help you with customized courses!
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How to use sys.argv in Python

The sys module is one of the common and frequently used modules in Python. In this article, we will walk you through how to use the sys module. We will learn about what argv[0] and sys.argv[1] are and how they work. We will then go into how to parse Command Line options and arguments, the various ways to use argv and how to pass command line arguments in Python 3.x In simple terms,Command Line arguments are a way of managing the script or program externally by providing the script name and the input parameters from command line options while executing the script. Command line arguments are not specific just to Python. These can be found in other programming languages like C, C# , C++, PHP, Java, Perl, Ruby and Shell scripting. Understanding sys.argv with examples  sys.argv is a list in Python that contains all the command-line arguments passed to the script. It is essential in Python while working with Command Line arguments. Let us take a closer look with a few examples. With the len(sys.argv) function, you can count the number of arguments. import sys print ("Number of arguments:", len(sys.argv), "arguments") print ("Argument List:", str(sys.argv)) $ python test.py arg1 arg2 arg3 Number of arguments: 4 arguments. Argument List: ['test.py', 'arg1', 'arg2', 'arg3']Module name to be used while using sys.argv To use sys.argv, you will first need to the sys module. What is argv[0]? Remember that sys.argv[0] is the name of the script. Here – Script name is sysargv.py import sys print ("This is the name of the script: ", sys.argv[0]) print ("Number of arguments: ", len(sys.argv)) print ("The arguments are: " , str(sys.argv))Output:This is the name of the script:  sysargv.py                                                                               Number of arguments:  1                                                                                                 The arguments are:  ['sysargv.py']What is "sys. argv [1]"? How does it work? When a python script is executed with arguments, it is captured by Python and stored in a list called sys.argv. So, if the below script is executed: python sample.py Hello Python Then inside sample.py, arguments are stored as: sys.argv[0] == ‘sample.py’ sys.argv[1] == ‘Hello’ sys.argv[2] == ‘Python’Here,sys.argv[0] is always the filename/script executed and sys.argv[1] is the first command line argument passed to the script . Parsing Command Line options and arguments  Python provides a module named as getopt which helps to parse command line options and arguments. Itprovides a function – getopt, whichis used for parsing the argument sequence:sys.argv. Below is the syntax: getopt.getopt(argv, shortopts, longopts=[]) argv: argument list to be passed.shortopts: String of short options as list . Options in the arguments should be followed by a colon (:).longopts: String of long options as list. Options in the arguments should be followed by an equal sign (=). import getopt import sys   first ="" last ="" argv = sys.argv[1:] try:     options, args = getopt.getopt(argv, "f:l:",                                ["first =",                                 "last ="]) except:     print("Error Message ")   for name, value in options:     if name in ['-f', '--first']:         first = value     elif name in ['-l', '--last']:         last = value   print(first + " " + last)Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python getopt_ex.py -f Knowledge -l Hut Knowledge Hut (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python getopt_ex.py --first Knowledge –last Hut Knowledge HutWhat are command line arguments? Why do we use them? Command line arguments are parameters passed to a program/script at runtime. They provide additional information to the program so that it can execute. It allows us to provide different inputs at the runtime without changing the code. Here is a script named as argparse_ex.py: import argparse parser = argparse.ArgumentParser() parser.add_argument("-n", "--name", required=True) args = parser.parse_args() print(f'Hi {args.name} , Welcome ')Here we need to import argparse package Then we need to instantiate the ArgumentParser object as parser. Then in the next line , we add the only argument, --name . We must specify either shorthand (-n) or longhand versions (--name)  where either flag could be used in the command line as shown above . This is a required argument as mentioned by required=True Output:  (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py --name Nandan  Hi Nandan , Welcome  (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py -n Nandan  Hi Nandan , Welcome The example above must have the --name or –n option, or else it will fail.(venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py --name   usage: argparse_ex.py [-h] --name NAME argparse_ex.py: error: the following arguments are required: --namePassing command line arguments in Python 3.x argv represents an array having the command line arguments of thescript . Remember that here, counting starts fromzero [0], not one (1). To use it, we first need to import sys module (import sys). The first argument, sys.argv[0], is always the name of the script and sys.argv[1] is the first argument passed to the script. Here, we need to slice the list to access all the actual command line arguments. import sys if __name__ == '__main__':     for idx, arg in enumerate(sys.argv):        print("Argument #{} is {}".format(idx, arg))     print ("No. of arguments passed is ", len(sys.argv))Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv\Scripts>python argv_count.py Knowledge Hut 21 Argument #0 is argv_count.py Argument #1 is Knowledge Argument #2 is Hut Argument #3 is 21 No. of arguments passed is  4Below script - password_gen.py is used to generate a secret password by taking password length as command line argument.import secrets , sys, os , string ''' This script generates a secret password using possible key combinations''' ''' Length of the password is passed as Command line argument as sys.argv[1]''' char = string.ascii_letters+string.punctuation+string.digits length_pwd = int(sys.argv[1])   result = "" for i in range(length_pwd):     next= secrets.SystemRandom().randrange(len(char))     result = result + char[next] print("Secret Password ==" ,result,"\n")Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv\Scripts>python password_gen.py 12 Secret Password == E!MV|,M][i*[Key takeaways Let us summarize what we've learnt so far. We have seen how to use the sys module in Python, we have looked at what areargv[0] and sys.argv[1] are and how they work, what Command Line arguments are and why we use them and how to parse Command Line options and arguments. We also dived into multiple ways to use argv and how to pass command line arguments in Python 3.xHope this mini tutorial has been helpful in explaining the usage of sys.argv and how it works in Python. Be sure to check out the rest of the tutorials on KnowledgeHut’s website and don't forget to practice with your code! 
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How to use sys.argv in Python

The sys module is one of the common and frequently... Read More

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