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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. 
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: 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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Xcode vs Swift

Xcode and Swift are two different products developed by Apple for macOS, iOS, iPadOS, watchOS, and tvOS. While Xcode is an integrated development environment (IDE) for macOS containing a suite of software development tools to develop software for macOS, iOS, iPadOS, watchOS, and tvOS, Swift is a general-purpose, multi-paradigm, compiled programming language developed iOS, macOS, watchOS, tvOS, Linux, and z/OS. So it is clear that they can not be compared with each other. On the contrary, Swift is compatible with Xcode as Swift v 5.1, the default version of Swift is included in Xcode v 11. In this article, we will go through what Xcode and Swift are in general and cover their features strengths and weaknesses followed by how Swift is compatible with Xcode. XcodeIt was first released in 2003 as version 1 with the latest stable one being version 10.2.1 released on 17 April 2019. It can be downloaded from the Mac App Store and is free to use for macOS Mojave users. Registered developers may download the preview releases and previous versions of the suite using via the Apple Developer website.  Overview of the major featuresSupport: Programming languages such as C, C++, Objective-C, Objective-C++, Java, AppleScript, Python, Ruby, ResEdit (Rez), and Swift are supported by Xcode with source code along with support for a variety of programming models including Cocoa, Carbo, and Java. Not only that, there is additional support via third parties for GNU Pascal, Free Pascal, Ada, C#, Perl, and D Capability: Xcode can build fat binary files that include the code for various architectures in the Mach-O executable format. Known as universal binary files, these allow the application to run on both PowerPC and Intel-based (x86) platforms including both 32-bit and 64-bit codes Compiling and debugging: Xcode uses the iOS SDK to compile and debug applications for iOS that run on ARM architecture processors GUI tool: Xcode comprises of the GUI tool, Instruments that runs dynamic tracing framework on the top of DTrace, a dynamic tracing framework designed by Sun Microsystems and released as a part of OpenSolaris. Advantages and disadvantages of Xcode: Xcode is designed by Apple and will only work with Apple operating systems: macOS, iOS, iPadOS, watchOS, and tvOS. Since its release in 2003, Xcode has made significant improvements and the latest version, Xcode 10.2.1 has all the features that are needed to perform continuous integration. Let us have a look at the pros of using Xcode: Equipped with a well designed and easy to use UI creator Excellent for code completion Using Xcode, a developer can learn profiling and heap analysis in a natural way Xcode’s simulator lets you easily test your app while you build it in an environment that simulates your iPhone The app store has a wide range of audience who are willing to pay for apps. Now, the cons: Clunky and outdated Objective C makes it more frustrating if you are habituated to use a modern language No support for tabbed work environments makes it difficult to work with multiple windows Hardly any information can be found online to solve problems due to a previous Apple NDA on Xcode development It is a complicated process to export your app onto a device Will only work with Apple operating systems The App Store approval process can be annoyingly lengthy.SwiftSwift was launched at Apple's 2014 Worldwide Developers Conference as a general-purpose, multi-paradigm, compiled programming language for iOS, macOS, watchOS, tvOS, Linux, and z/OS Being a new entry these operating systems, Swift accelerates on the best parts of C and Objective C without being held back by its compatibility. It utilises safe patterns for programming, adding more features to it, thus making programming easier and more flexible. By developing their existing debugger, compiler and framework infrastructure, it took quite some time to create the base for Swift. Furthermore, Automatic Reference Counting was used to simplify the memory management part. The framework stack which was once built upon a solid framework of Cocoa and Foundation has undergone significant changes and is now completely regulated and refurbished. Developers who have worked with Objective-C do find Swift quite similar. Objective-C’s dynamic object model and its comprehensively named parameters provide a lot of control to Swift.  Developers can use Swift to have access to the existing Cocoa framework in addition to the mix and match interoperability with an objective C code. Swift uses this common rule to offer multiple new features in combination with object-oriented and procedural portions of the language. The idea is to create the best possible language for a wide range of uses, varying from desktop and mobile apps, systems programming, and scaling up to cloud services. The designing of Swift was done to make sure that developers find it easy to maintain and write correct programs. Coding done in Xcode is safe, fast and expressive. Swift offers a host of features that give developers the control needed to make the code easy to read and write. Furthermore, Apple made Swift to be easily understandable to help developers avoid making mistakes while coding and make the code look organised, along with the modules that give namespaces and eliminate headers. Since Swift uses some features present in other languages, one of them being named parameters written with clean syntax that makes the APIs much easier to maintain and read. Here are some of the additional features of Swift: Multiple return values and Tuples Generics Short and quick iterations over a collection or range Structs that support extensions, methods and protocols Functional programming patterns Advanced control flow Powerful error handling. These features are systematically designed to make them work together resulting in creating a powerful but fun-to-use language. Advantages and disadvantages of Swift: Pros of using the Swift Programming language: Easy to read and maintain: The Swift program codes are based on natural English as it has borrowed syntaxes from other programming languages. This makes the language more expressive Scalable: Users can add more features to Swift, making it a scalable programming language. In the future, Swift is what Apple is relying on and not Objective C Concise: Swift does not include long lines of code and that favours the developers who want a concise syntax, thus increasing the development and testing rate of the program Safety and improved performance: It is almost 40% better than the Objective-C when speed and performance are taken into consideration as it is easy to tackle the bugs which lead to safer programming Cross-device support: This language is capable of handling a wide range of Apple platforms such as iOS, iOS X, macOS, tvOS, and watchOS. Automatic Memory Management: This feature present in Swift prevents memory leaks and helps in optimizing the application’s performance that is done by using Automatic Reference Counting. Cons of Swift: Compatibility issues: The updated versions Swift is found to a bit unstable with the newer versions of Apple leading to a few issues. Switching to a newer version of Swift is the fix but that is costly Speed Issues: This is relevant to the earlier versions of the Swift programming language Less in number: The number of Swift developers is limited as Swift is a new programming language Delay in uploading apps: Developers will be facing delays over their apps written in Swift to be uploaded to the App Store only after iOS 8 and Xcode 6 are released. The estimated time for release is reported to be September-October, 2014. Conclusion So as we discussed both Xcode and Swift, it is clear that they cannot be compared to each other. In fact, they both complement each other to deliver impressive results without any headaches. Apple relies on both quite a lot and it is certain to have Swift and Xcode the perfect combination of a robust application and a user-friendly programming language.
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Xcode vs Swift

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Scala Vs Kotlin

Ever-changing requirements in coding have always been happening, ones that cause programmers to change their minds about using the appropriate programming language and tools to code. Java has been there for a long time, a really long time, 24 years ago. It is relatively easy to use, write, compile, debug, and learn than other programming languages. However, its certain inhibitions like slow performance, unavailability of any support for low-level programming, possessing poor features in GUI 4, and having no control over garbage collection is putting Java developers in a dilemma on choosing an alternative to Java, such as JetBrains’ programming language, Kotlin, presently an officially supported language for Android development or Scala, an all-purpose programming language supporting functional programming and a strong static type system. Today, we will discuss how developers can decide to choose Scala or Kotlin as an alternative to Java. We will briefly talk about Scala and Kotlin separately and talk about their application before moving forward to looking at the differences, advantages, and disadvantages of both and finally have you decide which one of these two suits your requirements. User’s requirement Before we begin, here is a question for the readers, ‘What are you looking for in the next programming language that you will use?’ It is an obvious question because the programming purposes drive the actual basis and need of developing a language. Do you need a language that strives to better Java or use a language that lets you do things that aren’t possible in Java? If it is the first reason, then Scala might be the best one for you, otherwise, it is a simplified programming language like Kotlin. Now let us first briefly discuss Scala and Kotlin individually. ScalaDeveloped by Martin Odersky, the first version of Scala was launched in the year 2003 and is a classic example of a  general-purpose, object-oriented computer language, offering a wide range of functional programming language features and a strong static type system. Inspired from Java itself, Scala, as the name suggests, is highly scalable and this very feature sets Scala apart from other programming languages. When we say that Scala is inspired from Java, that means developers can code Scala in the same way they do for Java. Additionally, Scala makes it possible to use numerous Java and libraries within itself as well. It is designed to be able to use an elegant, concise and type-safe method to express common programming patterns. Scala is a very popular programming language amongst developers and rising up its ranks in the world of technology. Although Scala comes with a number of plus points, there are some which make it a bit ineffective. Here are the strengths and weaknesses of Scala. Strengths: Full Support for Pattern Matching, Macros, and Higher-Kinded Types Has a very flexible code syntax Gets a bigger Community Support Enables overloading operators Weaknesses: Slow in compilation Challenging Binary Compilation Not so proficient in the Management of Null SafetyKotlin Developed by JetBrains, Kotlin was released on February 2012 as an open-source language. Until now, there have been two released versions with the latest one being Kotlin 1.2, the most stable version that was released on November 28, 2017. Since Kotlin is extremely compatible with Java 6 the latest version of Java on Android, it has gained critical acclaim on Android worldwide and additionally, it offers various key features that are prepared only for Java 8 and not even Java 6 developers have access to that. Kotlin provides seamless and flawless interoperability with Java. That means, developers can easily call Java codes from Kotlin and same goes the other way around. The built-in null safety feature avoids showing the NullPointerException (NPE) that makes developing android apps easy and joyful, something every android programmer wants. Below mentioned are the key pointers on the strengths and weaknesses of Kotlin. Strengths Takes a Functional Programming Approach and Object-Oriented Programming style(OOP) Style  Has Higher-Order Functions Short, Neat, and Verbose-Free Expression  Supported by JetBrains and Google. Weaknesses: More limited Pattern Matching Additional Runtime Size Initial Readability of Code Shortage of Official Support Smaller Support Community. Ease of learning: Scala vs Kotlin Scala is a powerful programming language packed with superior features and possesses a flexible syntax. It is not an easy language to learn and is a nightmare for newcomers. Kotlin, on the other hand, has been reported to have been an easy-to-learn language for many Java developers as getting started with Kotlin is relatively easy and so is writing codes. Even though it is a comparatively easier language to learn and code with, Kotlin lacks the solid set of features that is common in Scala. It might take less time to learn a programming language, but the most important thing to look for is a comprehensive array of features. Scala, even though a very difficult language to learn, is cherished by the developers as it lets them do things that cannot be done in Kotlin Here are the major differences between Scala and Kotlin: ScalaKotlinType inferenceEfficientImmutabilityExtension FunctionsSingleton objectMassive InteroperabilityConcurrency controlLessens Crashes at RuntimeString interpolationSmart Cast FunctionHigher-order functionSafe and ReliableCase classes and Pattern matching Lazy computationLow adoption costRich collection setMaking the appropriate choice of languageNow, whether you may like a programming language or not, if that very language helps you get the best out of your job, then you will have to live with it. These are the facts about getting the best results. The outcome is the main factor in you deciding the appropriate language for your job. Kotlin is the only option for Android development as Android doesn’t use JVM, so any old JVM-compatible language will not work in Android. Kotlin has it all what it takes to compile, debug, and run the software on Android because of which it is in-built into Android Studio. However, Kotlin is not so usable outside Android development. If you are one of the developers who like working with Eclipse for your IDE, then Scala IDE is better than the Kotlin Plugin even if you can make Eclipse work with both the languages with limitations. Scala IDE is more advanced than the Kotlin plugin and is easier to set up. Some developers found it quite difficult to make the Kotlin plugin work. This case is quite the same with NetBeans. Kotlin is still getting there but is already popular amongst Java developers as it offers an easier transition than Scala. Kotlin is still maturing, but many Java people find adopting it is an easier transition than Scala is.  Scala, however, is for developers who are focused more on discovering new ideas while Kotlin is for those who want to get results. Kotlin stresses fast compilation but is more restrictive while Scala gives a lot of flexibility. Go for Scala if you breathe functional programming! It has more appropriate features for this type of programming than Kotlin does. Scala supports currying and partial application, the methods of breaking down functions requiring multiple arguments offering more flexibility. Go for the one that is the most appropriate one for your work, style of working and what you are aiming at. Think before you leap. The Outcome At the end of the day, all that matters is what you want to use the language for. While Scala goes well for the projects that require a combination of functional, OOP style programming languages, and where programmers need to handle lots of data or complex modelling, Kotlin becomes the best choice when you want something less frustrating than Java while developing apps because using Kotlin makes app development less cumbersome and a great thing to work on. It is just like a better-looking version of Java with less lengthy codes. 
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Scala Vs Kotlin

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What are List Methods in Python

Sequence is one of the most basic data types in Python. Every element of a sequence is allocated a unique number called its position or index. The first designated index is zero, the second index is one, and so forth. Although Python comes with six types of pre-installed sequences, the most used ones are lists and tuples, and in this article we would be discussing about lists and its methods.Certain tasks can be performed with all types of sequence; these include adding, multiplying, indexing, slicing, and so on. For added convenience, Python provides built-in functions to find the length of a sequence and to find the largest and smallest elements of the sequence. If you are interested to learn more about other functions and features of Python, you may go through our Python tutorial.What is a ListList is the most versatile data-type available in Python that can be written as a collection of comma-separated values or items between square brackets. The items in a list need not necessarily be homogeneous, i.e. of the same type. This property of List makes it one of the most powerful tools in Python eg:[‘HELLO’, 57, “SKY”]. A single list can contain different Data-Types such as integers, strings, as well as Objects. Lists are mutable, and hence can be changed even after their creation.In Python, lists are ordered in a definite manner and have a definite count. Elements of a list are indexed according to a particular sequence and the indexing is done with 0 being the starting index. Even element of a list has its unique place in the list, which allows duplication of the elements in the list, with each element having its own distinct place and credibility. Lists are a useful tool for storing a sequence of data.Creating a list is as simple as putting different types of comma-separated values between square brackets:list1 = ['jack', 'jill', 1998, 2019];[Text Wrapping Break] list2 = [1, 2, 3, 4, 5 ];[Text Wrapping Break] list3 = ["w", "x", "y", "z"]Just like string indices, list indices start with 0, and lists can be sliced, concatenated and so on.Creating a ListPython Lists can be created just by placing the integer, string, or character inside the square brackets[]. Unlike Sets, a list does not require a built-in function for its creation.# Python program to demonstrate the creation of a list [Text Wrapping Break] # Creating a List [Text Wrapping Break] List = [] [Text Wrapping Break] print("Initial blank List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] # Creating a List with  [Text Wrapping Break] # the use of a String [Text Wrapping Break] List = ['PythonListDemo'] [Text Wrapping Break] print("\nList with the use of String: ") [Text Wrapping Break] print(List) [Text Wrapping Break][Text Wrapping Break] # Creating a List with [Text Wrapping Break] # the use of multiple values  [Text Wrapping Break] List = ["Python", "List", "Demo"] [Text Wrapping Break] print("\nList containing multiple values: ") [Text Wrapping Break] print(List[0]) [Text Wrapping Break] print(List[2]) [Text Wrapping Break] [Text Wrapping Break] # Creating a Multi-Dimensional List [Text Wrapping Break] # (By Nesting a list inside a List) [Text Wrapping Break] List = [['Python', 'List'] , ['Demo']] [Text Wrapping Break] print("\nMulti-Dimensional List: ") [Text Wrapping Break] print(List)Initial blank List: [Text Wrapping Break] [] [Text Wrapping Break] [Text Wrapping Break] List with the use of String: [Text Wrapping Break] ['PythonListDemo'] [Text Wrapping Break] [Text Wrapping Break] List containing multiple values: [Text Wrapping Break] Python [Text Wrapping Break] Demo[Text Wrapping Break] [Text Wrapping Break] Multi-Dimensional List:  [Text Wrapping Break] [['Python', 'List'], ['Demo']]Creating a list with multiple distinct or duplicate elements:Multiple distinct or duplicate values can be stored as a sequence during creation of list:# Creating a List with [Text Wrapping Break] # the use of  Numbers [Text Wrapping Break] # (Having duplicate values) [Text Wrapping Break] List = [1, 2, 4, 4, 3, 3, 3, 6, 5] [Text Wrapping Break] print("\nList with the use of Numbers: ")  [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Creating a List with [Text Wrapping Break] # mixed type of values [Text Wrapping Break] #  (Having numbers and strings) [Text Wrapping Break] List = [1, 2, 'Python', 4, 'List', 6, 'Demo'] [Text Wrapping Break] print("\nList with the use of Mixed Values: ") [Text Wrapping Break] print(List)List with the use of Numbers: [Text Wrapping Break][1, 2, 4, 4, 3, 3, 3, 6,  5] [Text Wrapping Break] [Text Wrapping Break] List with the use of Mixed Values: [Text Wrapping Break] [1, 2, 'Python', 4, 'List', 6, 'Demo'] [Text Wrapping Break]Adding Elements to a ListUsing append() methodUsing the built-in append() function elements can be added to the List. At a time only one element can be added to the list by the use of append() method, but for the addition of multiple elements with the method, loops are used. Unlike Sets, a new List can be added to an existing one with the use of the append() method.# Python program to demonstrate addition of elements in a List [Text Wrapping Break] [Text Wrapping Break] # Creating a List [Text Wrapping Break] List = [] [Text Wrapping Break] print("Initial blank List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] #  Addition of Elements  [Text Wrapping Break] # in the List [Text Wrapping Break] List.append(1) [Text Wrapping Break] List.append (2) [Text Wrapping Break] List.append(4) [Text Wrapping Break] print("\nList after Addition of Three elements: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break]# Adding elements to the List [Text Wrapping Break] # using Iterator [Text Wrapping Break] for i in range(1, 4): [Text Wrapping Break] List.append(i) [Text Wrapping Break] print("\nList after Addition of elements from 1-3: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] # Addition of List to a List [Text Wrapping Break] List2 = ['Python', 'List'] [Text Wrapping Break] List.append(List2) [Text Wrapping Break] print ("\nList after Addition of a List: ") [Text Wrapping Break] print(List)Initial blank List: [Text Wrapping Break] [] [Text Wrapping Break]  [Text Wrapping Break] List after Addition of Three elements: [Text Wrapping Break] [1,2,4][Text Wrapping Break][Text Wrapping Break]List after Addition of elements from 1-3: [Text Wrapping Break] [1, 2, 4, 1, 2, 3] [Text Wrapping Break]  [Text Wrapping Break] List after Addition of a List:[Text Wrapping Break] [1, 2,  4, 1, 2, 3, ['Python', 'List']]Using insert() methodAppend() method restricts the addition of elements at the end of the List only. Using the insert() method, elements can be added to the list at your desired position. Unlike append() which requires only one argument, insert() method requires two arguments for defining the position and value of the element to be inserted (position, value).# Python program to demonstrate addition of elements in a List [Text Wrapping Break] [Text Wrapping Break] # Creating a List  [Text Wrapping Break] List = [1,2,3,4] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] # Addition of Element at  [Text Wrapping Break] # specific Position [Text Wrapping Break] # (using Insert Method) [Text Wrapping Break] List.insert(3, 12) [Text Wrapping Break] List.insert(0, 'Python') [Text Wrapping Break] print("\nList after performing Insert Operation: ") [Text Wrapping Break] print(List)Initial List: [Text Wrapping Break] [1, 2, 3, 4] [Text Wrapping Break] [Text Wrapping Break] List after performing Insert Operation: [Text Wrapping Break] [ 'Python', 1, 2, 3, 12, 4]Using extend() methodApart from append() and insert() methods, there’s another method to add elements by the use of extend() method. This method is used for adding multiple elements to the end of the list at once.# Python program to demonstrate  [Text Wrapping Break] # Addition of elements in a List [Text Wrapping Break] [Text Wrapping Break] # Creating a List  [Text Wrapping Break] List = [1,2,3,4] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] #  Addition of multiple elements [Text Wrapping Break] # to the List at the end [Text Wrapping Break] # (using Extend Method)  [Text Wrapping Break] List.extend ( [8,  'Python', 'Program']) [Text Wrapping Break] print( "\nList after performing  Extend Operation: " ) [Text Wrapping Break] print(List)Initial List: [Text Wrapping Break] [1, 2, 3, 4] [Text Wrapping Break]  [Text Wrapping Break] List after performing Extend Operation:  [Text Wrapping Break] [1, 2, 3, 4, 8, 'Python', 'Program']Accessing elements from the ListIn order to access the items in a List, the index number is used as reference. The index operator [ ] is used to access the items of a list. The index should be an integer and nested lists are accessed by using nested indexing.# Python program to demonstrate  [Text Wrapping Break] # accessing of element from list [Text Wrapping Break][Text Wrapping Break] # Creating a List with [Text Wrapping Break]# the use of multiple values [Text Wrapping Break] List = ["Access", "List", "Elements"] [Text Wrapping Break]  [Text Wrapping Break]# accessing an element from the  [Text Wrapping Break] # list using index number [Text Wrapping Break] print("Accessing an element from the list") [Text Wrapping Break] print(List[0])  [Text Wrapping Break] print(List[2]) [Text Wrapping Break]  [Text Wrapping Break] # Creating a Multi-Dimensional List [Text Wrapping Break] # (By Nesting a list inside a List) [Text Wrapping Break] List = [['Access', 'List'] , ['Elements']] [Text Wrapping Break]  [Text Wrapping Break] # accessing an element from the [Text Wrapping Break] # Multi-Dimensional List using [Text Wrapping Break] # index number [Text Wrapping Break] print("Accessing an element from a Multi -  Dimensional list") [Text Wrapping Break] print(List[0][1]) [Text Wrapping Break] print(List[1][0])Accessing an element from the list [Text Wrapping Break] Access [Text Wrapping Break] Elements [Text Wrapping Break] [Text Wrapping Break] Accessing an element from a Multi-Dimensional  list [Text Wrapping Break] List [Text Wrapping Break] ElementsNegative indexingIn Python, negative sequence indexing means the representation of positions of the array from the end. Rather than calculating the offset like List[len(List)-3], we can just write it like List[-3]. Here, -1 refers to the last item, -2 refers to the second last item etc. i.e. beginning from the end.List = [1, 2, 'Python', 4, 'Negative', 6, 'Index'] [Text Wrapping Break] [Text Wrapping Break] # Accessing an element using negative indexing [Text Wrapping Break] print("Accessing element using negative indexing") [Text Wrapping Break]  [Text Wrapping Break] # print the last element of list [Text Wrapping Break] print(List[-1]) [Text Wrapping Break] [Text Wrapping Break]# print the third last element of list  [Text Wrapping Break] print(List[-3])Accessing element using negative indexing [Text Wrapping Break] Index [Text Wrapping Break] NegativeRemoving Elements from the ListUsing remove() methodIn Python, using the built-in remove() function, elements can be removed from the List but an Error will arise if the element is not present in the set. Remove() method is only capable of removing one element at a time, to remove a range of elements, an iterator is used. A limitation of this method is that it will only remove the first occurrence of the searched element and would not work if there are multiple occurrences of the searched element.# Python program to demonstrate removal of elements in a List [Text Wrapping Break][Text Wrapping Break] # Creating a List [Text Wrapping Break] List = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Removing elements from List [Text Wrapping Break] # using Remove() method [Text Wrapping Break] List.remove(5) [Text Wrapping Break] List.remove(6) [Text Wrapping Break] print("\nList after removal of two elements: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Removing elements from List [Text Wrapping Break] # using iterator method [Text Wrapping Break] for i in range(1, 5): [Text Wrapping Break]    List.remove(i) [Text Wrapping Break] print("\nList after removing a range of elements: ") [Text Wrapping Break] print(List)Initial List: [Text Wrapping Break][1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] [Text Wrapping Break][Text Wrapping Break]List after removal of two elements: [Text Wrapping Break][1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12][Text Wrapping Break] [Text Wrapping Break] List after removing a range of elements: [Text Wrapping Break] [7, 8, 9, 10, 11, 12]Using pop() methodIn Python,  we can also remove and return an element from the set using the Pop() function, but it removes  the last element of the set only by default. To remove a specific element from a position of the List, index of the element is passed as an argument to the pop() function.List = [1,2,3,4,5] [Text Wrapping Break] [Text Wrapping Break] # Removing element from the  [Text Wrapping Break] # Set using the pop() method [Text Wrapping Break] List.pop() [Text Wrapping Break] print("\nList after popping an element: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Removing element at a  [Text Wrapping Break] # specific location from the  [Text Wrapping Break] # Set using the pop() method [Text Wrapping Break] List.pop(2) [Text Wrapping Break] print("\nList after popping a specific element: ") [Text Wrapping Break] print(List)List after popping an element: [Text Wrapping Break] [1, 2, 3, 4]  [Text Wrapping Break][Text Wrapping Break]List after popping a specific  element: [Text Wrapping Break][1, 2, 4]Slicing of a ListAlthough there are several ways to print the whole List with all the elements in Python, there is only one way to print a specific range of elements from the list: by the use of Slice operation. Slice operation is performed on Lists by the use of colon(:). For printing elements from the beginning of the range use [:Index], for printing elements from end use [:-Index], to print elements from a specific index till the end use [Index:], for printing elements within a specific range, use [Start Index: End Index] and to print the entire List by the use of slicing operation, use [:]. Moreover, in order to print entire List in reverse order, use [::-1]. For printing the elements of List from rear end, negative indexes are used.# Python program to demonstrate removal of elements in a List [Text Wrapping Break][Text Wrapping Break]# Creating a List [Text Wrapping Break] List = ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Print elements of a range [Text Wrapping Break] # using Slice operation [Text Wrapping Break] Sliced_List = List[3:10] [Text Wrapping Break] print("\nSlicing elements in a range 3-10: ") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break]  [Text Wrapping Break] # Print elements from a  [Text Wrapping Break] # pre-defined point to end [Text Wrapping Break] Sliced_List = List[6:] [Text Wrapping Break] print("\nElements sliced from 6th ""element till the end: ") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break] [Text Wrapping Break]# Printing elements from [Text Wrapping Break] # beginning till end [Text Wrapping Break] Sliced_List = List[:] [Text Wrapping Break] print("\nPrinting all elements using slice operation: ") [Text Wrapping Break] print(Sliced_List) Initial List: [Text Wrapping Break] ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'] [Text Wrapping Break] [Text Wrapping Break] Slicing elements in a range 3-10: [Text Wrapping Break]['H', 'O', 'N', 'P', 'R','O','G'][Text Wrapping Break][Text Wrapping Break]Elements sliced from 6th element till the end: [Text Wrapping Break]['P', 'R', 'O', 'G', 'R', 'A', 'M'][Text Wrapping Break][Text Wrapping Break]Printing all elements using slice operation: [Text Wrapping Break] ['P', 'Y', 'T', 'H', 'O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M']Negative index List Slicing# Creating a List [Text Wrapping Break] List = ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Print elements from beginning [Text Wrapping Break] # to a pre-defined point using Slice [Text Wrapping Break] Sliced_List = List[:-7] [Text Wrapping Break] print("\nElements sliced till 7th element from last: ") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break]  [Text Wrapping Break] #  Print elements of a range [Text Wrapping Break] # using negative index List slicing [Text Wrapping Break] Sliced_List = List[-6:-1] [Text Wrapping Break] print("\nElements sliced from index -6 to -1") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break]  [Text Wrapping Break] #  Printing elements in reverse [Text Wrapping Break] # using Slice operation [Text Wrapping Break] Sliced_List = List[::-1] [Text Wrapping Break] print("\nPrinting List in reverse: ") [Text Wrapping Break] print(Sliced_List)Initial List: [Text Wrapping Break] ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'][Text Wrapping Break] [Text Wrapping Break]Elements sliced till 7th element from last: [Text Wrapping Break]['P', 'Y', 'T', 'H', 'O', 'N'][Text Wrapping Break] [Text Wrapping Break]Elements sliced from index -6 to -1[Text Wrapping Break] ['R', 'O', 'G', 'R', 'A'][Text Wrapping Break] [Text Wrapping Break]Printing List in reverse:[Text Wrapping Break]['M', 'A', 'R', 'G', 'O', 'R', 'P', 'N', 'O', 'H', 'T', 'Y', 'P']Updating ListsYou can update single or multiple elements of lists by reassigning the values on the lists individually.# Python program to update elements of a list[Text Wrapping Break]list = ['physics', 'chemistry', 1998, 2019];[Text Wrapping Break] print "Value available at index 2 : "[Text Wrapping Break] print list[2] [Text Wrapping Break] list[2] = 2000;[Text Wrapping Break] print "New value available at index 2 : "[Text Wrapping Break] print list[2]Value available at index 2 : [Text Wrapping Break] 1998 [Text Wrapping Break] New value available at index 2 : [Text Wrapping Break] 2000Built-in functionsFUNCTION DESCRIPTIONsum() Addsall numbers.ord() Used for returning an integer which represents the unique Unicode code point of the given Unicode character.cmp() If the first list is “greater” than the second list, the function returns 1.max() It returns the largest element in the list.min() It returns the smallest element in the list.all() It returns true if all elements are true or false if any element in the list is empty.any() It returns true if even one of the elements of the list is true. If one list is empty, it returns false.len() It returns length the list.enumerate() It adds a counter to an enumerate object that can be used directly for loops.accumulate() It makes an iterator that gives the result of a function. It takes a function as an argument.filter() It can individually check if every element is true or not.map() It applies a particular function to each item of an iterable and shows a list of the results.lambda() It is an anonymous function that behaves like a normal function in regard to arguments. While normal functions are defined with def keyword, anonymous functions are defined using lambda keyword.List MethodsFUNCTION DESCRIPTIONAppend() Adds an element at the end of the listExtend() Adds all elements of one list to another listInsert() Inserts an item at a desired indexRemove() Removes an item from the listPop() Removes and returns an element at a desired indexClear() Removes all elements from the listIndex() Returns the index of the first identical itemCount() Returns the number of items passed as argumentSort() Sort items of a list in ascending orderReverse() Reverses the listcopy() Returns a copy of the listSummaryIn this article, we have covered the concept of Lists in Python. You have learned the basics of creating a List, adding value to it, accessing its elements, removing the elements, and various other operations. We have also covered some basic built-in functions of Python and several other methods along with their functions. 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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What are List Methods in Python

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How to Work with Excel Spreadsheets using Python

Excel is considered as one of the most popular and widely used spreadsheet applications developed by Microsoft. You can organize, analyze and store your data into tabular sheets with the help of Excel. From analysts and sales managers, to CEOs, professionals from every field use Excel for creating quick statistics and for data crunching.Spreadsheets are commonly used in the present world because of their intuitive nature and the ability to handle large datasets. Most importantly, they can work without any prior technical background.Finding different ways to work with Excel using code is essential since working with data and in Python has some serious advantages in comparison with Excel’s UI. Developers of Python have implemented ways to read, write and manipulate Excel documents.You can check the quality of your spreadsheet application by going over the checklist below:Is the spreadsheet able to represent static data?Is the spreadsheet able to mix data, calculations, and reports?Is the data in your spreadsheet complete and consistent in nature?Does the spreadsheet have an organized worksheet structure?This checklist will help you in verifying the qualitative nature of the spreadsheet application you’re going to work on.Practical Applications  In this article, we would be using openpyxl to work on data. With the help of this module, you can extract data from a database into an Excel spreadsheet or you can also convert an Excel spreadsheet into a programmatic format. There can be a lot of possible situations where you might feel the need to use a package like openpyxl. Let us discuss a few of them to get a comprehensive overview of it.Importing New Products Into a Database Consider yourself working in an online store company. When they want to add new products to the online store, they make an Excel spreadsheet with a few hundred rows along with the name of the product, description, price and a few more basic information and then they give it to you. Now, if you want to import this particular data, you need to iterate over each row of the spreadsheet and then add each of the products into the database of the online store.[Text Wrapping Break] Exporting Database Data Into a SpreadsheetConsider you have a Database table. In this particular table, you have collected information of all your users which includes their name, contact number, email address, and so forth. Now, the Marketing Team is willing to collectively contact all the users and promote a new product of the company. However, neither do they have access to the Database nor they have any idea about using SQL to extract the information. In this situation, openpyxl comes to play. You can use it effectively to iterate over each User record and transform the required information into an Excel spreadsheet.    Appending Information to an Existing SpreadsheetConsider the same online store example we discussed above. You have an Excel spreadsheet with a list of users and your job is to append to each row the total amount they have spent in your store.In order to perform this, you have to read the spreadsheet first and then iterate through each row and fetch the total amount spent from the Database. Finally, you need to write it back to the spreadsheet.Starting openpyxlYou can install the openpyxl package using pip. Open your terminal and write the following command: $ pip install openpyxlAfter you have installed the spreadsheet, you can make up your own simple spreadsheet: from openpyxl import Workbook workbook = Workbook() spreadsheet = workbook.active spreadsheet["A1"] = "Hello" spreadsheet["B1"] = "World!" workbook.save(filename="HelloWorld.xlsx")How to Read Excel Spreadsheets with openpyxl Let us start with the most important thing that you can do with a spreadsheet,i.e. read it. We will be using a Watch Sample Dataset which contains a list of 100 watches with information like product name, product ID, review and so forth.  A Simple Way to Read an Excel Spreadsheet Let us start with opening our sample spreadsheet:>>> from openpyxl import load_workbook >>> workbook = load_workbook(filename="sample.xlsx") >>> workbook.sheetnames ['Sheet 1'] >>> spreadsheet = workbook.active >>> spreadsheet >>> spreadsheet.titleIn the example code above, we open the spreadsheet using load_workbook and then we check all the sheets that are available to work with using workbook.sheetnames. Then Sheet 1 is automatically selected using workbook.active since it is the first sheet available. This is the most common way of opening a spreadsheet.  Now, let us see the code to retrieve data from the spreadsheet: >>> spreadsheet["A1"] >>> spreadsheet["A1"].value 'marketplace' >>> spreadsheet["F10"].value "G-Shock Men's Grey Sport Watch"You can retrieve the actual value and the cell value  both. To get the actual value, use .value and to get the cell, you can use .cell():>>> spreadsheet.cell(row=10, column=6) >>> spreadsheet.cell(row=10, column=6).value "G-Shock Men's Grey Sport Watch"Importing Data from a Spreadsheet In this section, we will discuss how to iterate through the data, and about conversion into a more useful format using Python.Let us first start with iterating through the data. There are a number of iterating methods that depend solely on the user.You can slice the data with a combination of rows and columns:>>> spreadsheet["A1:C2"] ((, , ),  (, , )) You can also iterate through the dataset by ranging between rows and columns: >>> # Get all cells from column A  >>> spreadsheet["A"] (,  ,   ...   ,   ) >>> # Get all cells for a range of columns >>> spreadsheet["A:B"]  ((,    ,    ...    ,    ),   (,    ,    ...    ,    ))  >>> # Get all cells from row 5 >>> spreadsheet[5] (,  ,  ...   ,  ) >>> # Get all cells for a range of rows >>> spreadsheet[5:6] ((,   ,    ...    ,    ),   (,    ,    ...    ,    )) Python offers arguments by which you can set limits to the iteration with the help of Python generators like .iter_rows() and .iter_cols(): >>> for row in spreadsheet.iter_rows(min_row=1, ... max_row=2, ... min_col=1, ... max_col=3): ... print(row) (, , ) (, , ) >>> for column in spreadsheet.iter_cols(min_row=1,  ... max_row=2, ... min_col=1, ... max_col=3): ... print(column) (, ) (, )  (, ) You can also add Boolean values_only in the above example and set it to True to get the values of cell: >>> for value in spreadsheet.iter_rows(min_row=1,  ... max_row=2,  ... min_col=1,  ... max_col=3,  ... values_only=True): ... print(value) ('marketplace', 'customer_id', 'review_id') ('US', 3653882, 'R3O9SGZBVQBV76')Since we are now done with iterating the data, let us now manipulate data using Python’s primitive data structures. Consider a situation where you want to extract information of a product from the sample spreadsheet and then store it into the dictionary. The key to the dictionary would be the product ID.   Convert Data into Python classesTo convert data into Python data classes, let us first decide what we want to store and how to store it.  The two essential elements that can be extracted from the data are as follows:                                                     1. Products                                             2. Review                                                          • ID                                                         • ID                                                          • Title                                                     • Customers ID                                                          • Parent                                                 • Headline                                                          • Category                                            • Body                                                                                                                         • DateLet us implement the two elements: import datetime from dataclasses import dataclass @dataclass class Product: id: str parent: str title: str category: str @dataclass class Review: id: str customer_id: str stars: int headline: str body: str  date: datetime.datetime The next step is to create a mapping between columns and the required fields: >>> for value in spreadsheet.iter_rows(min_row=1, ... max_row=1, ... values_only=True): ... print(value) ('marketplace', 'customer_id', 'review_id', 'product_id', ...) >>> # Or an alternative >>> for cell in sheet[1]: ... print(cell.value) marketplace Customer_ID Review_ID Product_ID Product_Parent ...Finally, let us convert the data into new structures which will parse the data in spreadsheet into a list of products and review objects: from datetime import datetime  from openpyxl import load_workbook  from classes import Product,Review  from mapping import PRODUCT_ID,PRODUCT_PARENT,PRODUCT_TITLE, \ PRODUCT_CATEGORY,REVIEW_DATE,REVIEW_ID,REVIEW_CUSTOMER, \ REVIEW_STARS,REVIEW_HEADLINE,REVIEW_BODY # Using the read_only method since you're not gonna be editing the spreadsheet workbook = load_workbook(filename="watch_sample.xlsx",read_only=True)  spreadsheet = workbook.active products = [] reviews = [] # Using the values_only because you just want to return the cell value for row in spreadsheet .iter_rows(min_row=2, values_only=True):  product = Product(id=row[PRODUCT_ID],  parent=row[PRODUCT_PARENT],  title=row[PRODUCT_TITLE],  category=row[PRODUCT_CATEGORY])  products.append(product) # You need to parse the date from the spreadsheet into a datetime format spread_date = row[REVIEW_DATE]  parsed_date = datetime.strptime(spread_date,"%Y-%m-%d") review = Review(id=row[REVIEW_ID], Customer_ID=row[REVIEW_CUSTOMER], stars=row[REVIEW_STARS], headline=row[REVIEW_HEADLINE], body=row[REVIEW_BODY], date=parsed_date) reviews.append(review) print(products[0]) print(reviews[0])After you execute the code, you will get an output that looks like this:Product(id='A90FALZ1ZC',parent=937111370,...) Review(id='D3O9OGZVVQBV76',customer_id=3903882,...)Appending Data To understanding how to append data, let us hover back to the first sample spreadsheet. We will open the document and append some data to it: from openpyxl import load_workbook # Start by opening the spreadsheet and selecting the main sheet workbook = load_workbook(filename="hello_world.xlsx") spreadsheet = workbook.active # Write what you want into a specific cell spreadsheet["C1"]="Manipulating_Data ;)" # Save the spreadsheet workbook.save(filename="hello_world_append.xlsx"If you open your Excel file, you will notice the additional Manipulating_Data being added to an adjacent cell. Writing Excel Spreadsheets With openpyxl A spreadsheet is a file that helps to store data in specific rows and columns. We can calculate and store numerical data and also perform computation using formulas. So, let’s begin with some simple Spreadsheets and understand what each line means. Creating our first simple Spreadsheet 1 from openpyxl import Workbook  2    3 filename = "first_program.xlsx"  4    5 workbook = Workbook()  6 spreadsheet = workbook.active  7    8 sheet["A1"] = "first"  9 sheet["B1"] = "program!" 10   11 workbook.save(filename=filename)Line 5: In order to make a Spreadsheet, at first,  we have to create an Empty workbook to perform further operations. Lines 8 and 9 : We can add data to a specific cell as per our requirement. In this example, we can see that two values “first” and “program” have been added to specific cells in the sheet. Line 11: The line shows how to save data after all the operations we have done. Basic Spreadsheet Operations Before going to the difficult coding part, at first we have to build our building blocks like how to add and update values, how to manage rows and columns, adding filters, styles or formulas in a Spreadsheet. We have already explained the following code by which we can add values to a Spreadsheet: >>> spreadsheet["A1"] = "the_value_we_want_to_add"There is another way that we can add values to Spreadsheet: >>> cell = sheet["A1"] >>> cell >>> cell.value 'hello' >>> cell.value = "hey" >>> cell.value 'hey'Line 1: In the first line at first we have declared the cell and updated its value. Line 5: We have printed the value of the cell as “first”  because  in the first program we have already assigned sheet["A1"]with “first” Line 8 : We have updated the value of the cell as "second"by simply assigning it to cell.value. Lines 9 : In this line, we have just printed the updated value of cell. Finally, you have to save all the operations you have performed into the spreadsheet once you call workbook.save().If  the cell didn’t exist while adding a value ,then openpyxl creates a cell:>>> # Before, our spreadsheet has only 1 row >>> print_rows() ('first', 'program!') >>> # Try adding a value to row 10 >>> spreadsheet["B10"] = "test" >>> print_rows() ('first', 'program!') (None, None)  (None, None)  (None, None)  (None, None)  (None, None)  (None, None)  (None, None)  (None, None)  (None, 'test') Managing Rows and Columns in Spreadsheet Insertion or deletion of rows (adding or removing elements of rows /columns) is one of the most basic operations in Spreadsheet. In openpyxl.We can perform these operations by simply calling these methods and passing its arguments. .insert_rows().delete_rows().insert_cols().delete_cols()We can pass 2 types of arguments to the methods :  idx amount Idx stands for index position and amount refers to the number of values we can store in the Spreadsheet. Using our basic knowledge based on the first  simple program, let’s see how we can use these methods inside the program: >>> print_rows() ('first', 'program!') >>> # Insert a column at the first position before column 1 ("A") >>> spreadsheet.insert_cols(idx=1) >>> print_rows() (None, 'first', 'program!') >>> # Insert 5 columns in  between column 2 ("B") and 3 ("C") >>> spreadsheet.insert_cols(idx=3,amount=5) >>> print_rows() (None, 'first', None, None, None, None, None, 'program!') >>> # Delete the created columns >>> spreadsheet.delete_cols(idx=3,amount=5) >>> v.delete_cols(idx=1) >>> print_rows() ('first', 'program!') >>> # Insert a new row in the beginning >>> spreadsheet.insert_rows(idx=1) >>> print_rows() (None, None) ('first', 'program!') >>> # Insert 3 new rows in the beginning  >>> spreadsheet.insert_rows(idx=1,amount=3) >>> print_rows() (None, None) (None, None)  (None, None)  (None, None)  ('first', 'program!') >>> # Delete the first 4 rows  >>> spreadsheet.delete_rows(idx=1,amount=4)  >>> print_rows()  ('first', 'program!') Managing SheetsWe have seen the following recurring piece of code in our previous examples .This is one of the ways of selecting the default sheet from the Spreadsheet: spreadsheet = workbook.activeHowever, if you want to open a spreadsheet with multiple sheets, you can write the following command: >>> # Let's say you have two sheets: "Products" and "Company Sales" >>> workbook.sheetnames ['Products', 'Company Sales'] >>> # You can select a sheet using its title >>> Products_Sheet = workbook["Products"] >>> Sales_sheet = workbook["Company Sales"]If we want to change the title of the Sheet, execute the following code: >>> workbook.sheetnames ['Products', 'Company Sales'] >>> Products_Sheet = workbook["Products"] >>> Products_Sheet.title = "New Products" >>> workbook.sheetnames ['New Products', 'Company Sales']We can CREATE / DELETE Sheets also with the help of two methods - .create_sheet() and  .remove(): >>> #To print the available sheet names >>> workbook.sheetnames  ['Products', 'Company Sales'] >>> #To create a new Sheet named "Operations" >>> Operations_Sheet = workbook.create_sheet("Operations") >>> #To print the updated available sheet names >>> workbook.sheetnames ['Products', 'Company Sales', 'Operations'] >>> # To define the position Where we want to create the Sheet(here “HR” sheet is created at the first position .Here index 0 represents the first position) >>> HR_Sheet = workbook.create_sheet("HR",0) >>> #To again  print the updated available sheet names >>> workbook.sheetnames ['HR', 'Products', 'Company Sales', 'Operations'] >>> # To remove them,we just have to send the sheet names as an argument which we want to delete to the method  .remove()  >>> workbook.remove(Operations_Sheet) >>> workbook.sheetnames ['HR', 'Products', 'Company Sales'] >>> #To delete hr_sheet >>> workbook.remove(hr_sheet) >>> workbook.sheetnames ['Products', 'Company Sales']Adding Filters to the Spreadsheet We can use openpyxl to add filters in our Spreadsheet but when we open our Spreadsheet, the data won’t be rearranged according to these sorts and filters. When you’re programmatically creating a spreadsheet and it is going to be sent and used by someone else, it is a good practice to add different filters and allow people to use it afterward. In the code below there is a simple example which shows how to add a simple filter to your spreadsheet: >>> # Check the used spreadsheet space using the attribute "dimensions" >>> spreadsheet.dimensions 'A1:O100' >>> spreadsheet.auto_filter.ref="A1:O100" >>> workbook.save(filename="watch_sample_with_filters.xlsx")Adding Formulas to the Spreadsheet Formulas are one of the most commonly used and powerful features of spreadsheets. By using formulas, you can solve various mathematical equations with the additional support of openpyxl which makes those calculations as simple as editing a specific cell’s value.The list of formulas supported by openpyxl are:>>> from openpyxl.utils import FORMULAE >>> FORMULAE frozenset({'ABS',            'AMORLINC',            'ACCRINT',             'ACOS',             'ACCRINTM',             'ACOSH',              ...,                   'AND',            'YEARFRAC',             'YIELDDISC',             'AMORDEGRC',             'YIELDMAT',             'YIELD',             'ZTEST'}) Let’s add some formulas to our spreadsheet. Let’s check the average star rating of  the 99 reviews within the spreadsheet: >>> # Star rating is in column "H"  >>> spreadsheet["P2"] = "=AVERAGE(H2:H100)" >>> workbook.save(filename = "first_example.xlsx")Now, if we open your spreadsheet and go to cell P2, you can see the value to be 4.18181818181818.  Similarly, we can use this methodology to include any formulas for our requirements in our spreadsheet. For example, if we want to count the number of helpful reviews: >>> # The helpful votes  counted in column "I"  >>> spreadsheet["P3"] = '=COUNTIF(I2:I100, ">0")' >>> workbook.save(filename = "first_example.xlsx") Adding Styles to the SpreadsheetIt is not so important and usually, we don’t use this in everyday code but for the sake of completeness, we will also understand this with the following example.Using openpyxl, we get multiple styling options such as including fonts, colors,  borders,and so on.Let’s have a look at an example:>>> # Import necessary style classes >>> from openpyxl.styles import Font,Color,Alignment,Border,Side,colors >>> # Create a few styles >>> Bold_Font = Font(bold=True) >>> Big_Red_Text = Font(color=colors.RED,size=20)  >>> Center_Aligned_Text = Alignment(horizontal="center")  >>> Double_Border_Side = Side(border_style="double")  >>> Square_Border = Border(top=double_border_side,  ... right=double_border_side,  ... bottom=double_border_side,  ... left=double_border_side)  >>> # Style some cells! >>> spreadsheet["A2"].font = Bold_Font >>> spreadsheet["A3"].font = Big_Red_Text >>> spreadsheet["A4"].alignment = Center_Aligned_Text >>> spreadsheet["A5"].border = Square_Border >>> workbook.save(filename="sample_styles.xlsx") If you want to apply multiple styles to one or several cells in our spreadsheets,you can use  NamedStyle class: >>> from openpyxl.styles import NamedStyle >>> # Let's create a style template for the header row >>> header = NamedStyle(name="header") >>> header.font = Font(bold=True) >>> header.border = Border(bottom=Side(border_style="thin")) >>> header.alignment = Alignment(horizontal="center",vertical="center") >>> # Now let's apply this to all first row (header) cells >>> header_row = sheet[1] >>> for cell in header_row: ... cell.style = header >>> workbook.save(filename="sample_styles.xlsx") Adding Charts to our SpreadsheetCharts are a good way to compute and understand large amounts of data quickly and easily. We have a lot of charts such as bar chart, pie chart, line chart, and so on. Let us start by creating a new workbook with some data:  1 from openpyxl import Workbook   2 from openpyxl.chart import BarChart,Reference   3    4 workbook = Workbook()   5 spreadsheet = workbook.active   6    7 # Let's create some sample sales data  8 rows = [   9    ["Product","Online","Store"],  10    [1,30,45],  11    [2,40,30],  12    [3,40,25],  13    [4,50,30],  14    [5,30,25],  15    [6,25,35],  16    [7,20,40],  17 ]  18   19 for row in rows: 20    spreadsheet .append(row)Now let us create a bar chart that will show the total number of sales per product: 22 chart = BarChart() 23 data = Reference(worksheet=sheet, 24                 min_row=1,  25                 max_row=8,  26                 min_col=2,  27                 max_col=3)  28   29 chart.add_data(data,titles_from_data=True) 30 spreadsheet .add_chart(chart, "E2") 31 32 workbook.save("chart.xlsx")You can also create a line chart by simply making some changes to the data:  1 import random   2 from openpyxl import Workbook   3 from openpyxl.chart import LineChart,Reference   4    5 workbook = Workbook()  6 sheet = workbook.active  7    8 # Let's create some sample sales data   9 rows= [ 10    ["", "January", "February", "March", "April",  11    "May", "June", "July", "August", "September",  12     "October", "November", "December"],  13    [1, ], 14    [2, ], 15    [3, ], 16 ]  17   18 for row in rows:  19    sheet.append(row) 20   21 for row in sheet.iter_rows(min_row=2, 22                           max_row=4, 23                           min_col=2, 24                           max_col=13): 25    for cell in row: 26        cell.value = random.randrange(5,100)There are numerous types of charts and various types of customizations you can apply to your spreadsheet to make it more attractive.Convert Python Classes to Excel SpreadsheetLet us now learn how to convert the Excel Spreadsheet data to Python classes.  Assume we have a database and we use some Object Relational mapping to map the database into Python classes and then export the objects into spreadsheets: from dataclasses import dataclass from typing import List @dataclass class Sale: id: str  quantity: int @dataclass  class Product:  id: str  name: str  sales:List[Sale] Now, to generate some random data, let’s assume that the above classes are stored in  db_classes.py file then:  1 import random   2    3 # Ignore these for now. You'll use them in a sec ;)   4 from openpyxl import Workbook   5 from openpyxl.chart import LineChart,Reference   6    7 from db_classes import Product,Sale   8    9 products_range = []  10   11 # Let's create 5 products 12 for idx in range(1,6): 13    sales = []  14   15    # Create 5 months of sales  16    for_in range(5): 17        sale_range = Sale(quantity=random.randrange(5,100)) 18        sales.append(sale) 19   20    product = Product(id=str(idx), 21                      name="Product %s" % idx, 22                      sales=sales) 23    products_range.append(product)By running this code, we will get 5 products in 5 months of sale with a random quantity of sales for each month. Now, we have  to convert this into a spreadsheet in which we need to iterate over the data: 25 workbook = Workbook()  26 spreadsheet = workbook.active  27 28 # Append column names first  29 spreadsheet.append(["Product ID","Product Name","Month 1",  30              "Month 2","Month 3","Month 4","Month 5"])  31   32 # Append the data  33 for product in products_range: 34    data = [product.id,product.name] 35    for sale in product.sales: 36        data.append(sale.quantity)  37    spreadsheet.append(data) This will create a spreadsheet with some data coming from your database. How to work with pandas to handle Spreadsheets?We have learned to work with Excel in Python because Excel is one of the most popular tools and finding a way to work with Excel is critical. Pandas is a great tool to work with Excel in Python. It has unique methods to read all kinds of data in an Excel file and we can export items back to Excel using it. To use it, at first we need to install pandas package: $ pip install pandas Then, let’s create a simple DataFrame:  1 import pandas as pd   2    3 data = {   4    "Product Name":["Product 1","Product 2"],   5    "Sales Month 1":[10, 20],   6    "Sales Month 2":[5, 35],   7 }   8 dataframe = pd.DataFrame(data)Now we have some data, and to convert it from a DataFrame into a worksheet we generally use .dataframe_to_rows(): 10 from openpyxl import Workbook 11 from openpyxl.utils.dataframe import  dataframe_to_rows  12   13 workbook = Workbook()  14 spreadsheet = workbook.active  15   16 for row in dataframe_to_rows(df, index=False,header=True):  17    spreadsheet .append(row) 18   19 workbook.save("pandas_spreadsheet.xlsx")We need to use  read_excel method to read data from pandas DataFrame object. excel_file =’movies.xls’  movies=pd.read_excel(excel_file) We can also use Excel file class to use multiple sheets from the same excel file: movies_sheets = [] for sheet in xlsx.sheet_names:     movies_sheets.append(xlsx.parse(sheet))     movies = pd.concat(movies_sheets))Indexes and columns allows you to access data from your DataFrame easily: >>> df.columns  Index(['marketplace', 'customer_id', 'review_id', 'product_id',        'product_parent', 'product_title', 'product_category', 'star_rating',         'helpful_votes', 'total_votes', 'vine', 'verified_purchase',         'review_headline', 'review_body', 'review_date'],        dtype='object') >>> # Get first 10 reviews' star rating  >>> df["star_rating"][:10] R3O9SGZBVQBV76    5 RKH8BNC3L5DLF     5  R2HLE8WKZSU3NL    2  R31U3UH5AZ42LL    5  R2SV659OUJ945Y    4  RA51CP8TR5A2L     5  RB2Q7DLDN6TH6     5  R2RHFJV0UYBK3Y    1  R2Z6JOQ94LFHEP    5  RX27XIIWY5JPB     4  Name: star_rating, dtype: int64 >>> # Grab review with id "R2EQL1V1L6E0C9", using the index >>> df.loc["R2EQL1V1L6E0C9"] marketplace               US customer_id         15305006  review_id     R2EQL1V1L6E0C9  product_id        B004LURNO6  product_parent     892860326  review_headline   Five Stars  review_body          Love it  review_date       2015-08-31  Name: R2EQL1V1L6E0C9, dtype: object Summary In this article we have covered: How to extract information from spreadsheets  How to create Spreadsheets in different ways How to customize a spreadsheet by adding filters, styles, or charts and so on How to use pandas to work with spreadsheets Now you are well aware of the different types of implementations you can perform with spreadsheets using Python. However, if you are willing to gather more information on this topic, you can always rely on the official documentation of openpyxl. To gain more knowledge about Python tips and tricks, check out our Python tutorial. To gain mastery over Python coding,join ourPython certification course.  
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How to Work with Excel Spreadsheets using Python

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What is pip, Getting Started with Python pip

Pip is a package manager for Python that allows you to install additional libraries and packages that are not part of the standard Python library such as the ones found in the Python Package Index. It is a replacement for easy install. If your version of Python is 2.7.9 (or greater) or Python 3.4 (or greater), then PIP comes pre-installed with Python, in other cases you will have to install it separately. PIP is a recursive acronym for “Preferred Installer Program” or “PIP Installs Packages”. It is a command-line utility that installs, reinstalls, or uninstalls PyPI packages with one simple command: pip. You may be familiar with the term package manager if you have used other languages like Ruby uses Gem, JavaScript uses npm for package management, and .NET uses NuGet. Pip has become the standard package manager for Python. The Python installer installs pip automatically, so it is ready for you to use, unless you have installed an older version of Python. You can also verify if pip is available on your Python version by running the command below:On running the command mentioned above, a similar output should be displayed which will show the pip version, along with the location and version of Python. If you are using an older version of Python, the pip version will not be displayed. Then you can install it separately. You can download pip from the following link: https://pypi.org/project/pip/ Installing pip in PythonFor WindowsFollow the instructions to install pip in Python on Windows 7, Windows 8.1, and Windows 10: Download get-pip.py installer script from https://bootstrap.pypa.io/get-pip.py. For Python 3.2, download from https://bootstrap.pypa.io/3.2/get-pip.py. After that, right-click on the link and select Save As and save it to any safe location on your computer. Open Command Prompt and navigate to the get-pip.py file where you saved it previously. Run the command: python get-pip.py For Mac Modern Mac systems have Python and pip pre-installed but the version of Python tends to be outdated and not the best choice for serious programming in Python. So, it’s highly recommended that you install a more updated version of Python and PIP. If you want to use the pre-installed Python application but don’t have PIP available, you can install PIP with the following commands in Terminal:sudo easy_install pipIf you want to install an updated version of Python, then you can use Homebrew. Installing Python with Homebrew requires a single command:brew install pythonInstalling Python with Homebrew will give you the latest version which should come packaged with PIP but if PIP is unavailable, you can re-link Python using the following commands in Terminal:brew unlink python && brew link pythonFor Linux If your Linux distribution came with Python pre-installed, using your system’s package manager you will be able to install PIP. This is preferable since pre-installed versions of Python do not work well with the get-pip.py script used on Windows and Mac. Given below are the commands you should run in order to install pip in your system depending on the version of Python you are using:Advanced Package Tool (Python 2.x):sudo apt-get install python-pip pacman Package Manager (Python 2.x):sudo pacman -S python2-pip Yum Package Manager (Python 2.x):sudo yum upgrade python-setuptools  sudo yum install python-pip python-wheel Dandified Yum (Python 2.x):sudo dnf upgrade python-setuptools  sudo dnf install python-pip python-wheel Zypper Package Manager (Python 2.x):sudo zypper install python-pip python-setuptools python-wheel Advanced Package Tool (Python 3.x): sudo apt-get install python3-pip pacman Package Manager (Python 3.x): sudo pacman -S python-pip Yum Package Manager (Python 3.x): sudo yum install python3 python3-wheel Dandified Yum (Python 3.x): sudo dnf install python3 python3-wheel Zypper Package Manager (Python 3.x): sudo zypper install python3-pip python3-setuptools python3-wheel For Raspberry Pi You are most likely running Raspbian if you are a Raspberry Pi user as it is the official operating system designated and provided by the Raspberry Pi Foundation. PIP comes pre-installed on with Raspbian Jessie. It is one of the biggest reasons to upgrade to Raspbian Jessie instead of using Raspbian Wheezy or Raspbian Jessie Lite. If you are using an older version of Raspbian, you can still manually install PIP. Given below are the commands you should run in order to install pip on your system depending on the version of Python you are using: On Python 2.x:sudo apt-get install python-pipOn Python 3.x:sudo apt-get install python3-pipRaspbian users, working with Python 2.x must use pip while Python 3.x users must use pip3 while running PIP commands.For Ubuntusudo apt-get install python-pipFor Fedorasudo yum install python-pipHow to use PIP and PyPI? PyPI - the Python Package Index After PIP is installed, we need to find a package to install. Packages are usually installed from the repository of software for the Python programming language which is the Python Package Index.Set environment variable for PIP:You won’t have to reference the pip install directory again and again if you set an environment variable.Set: (default = C:\Python27\Scripts) in your Windows/Linux “PATH” environment variable.Getting Started with PIP Now that we know what PIP is and have successfully installed it on our computer, let's get started on how to use it: Commands in PIP Enter pip in the command terminal and it will show the following output on the screen. Usage:pip [options] Commands:InstallInstall packagesDownloadDownload packagesuninstallUninstall packagesunzipUnzip individual packagesbundleCreate pybundleshelpShow help for commandsconfigManage local and global configurationfreezeOutput installed packages in required formatlistList installed packageswheelBuild wheels from your requirementshashCompute hashes of package archivescompletionA helper command used for command completioncheckVerify installed packages have compatible dependenciesshowShow information about installed packagessearchSearch PyPI for packageszipZip individual packagesCommonly used commands in pip are install, upgrade or uninstall. General Options: -h, --help: Shows help. --isolated: To run pip in an isolated mode by ignoring environment variables and user configuration. -v, --verbose: Give more output. Option is additive, and can be used up to 3 times. -V, --version: Show version and exit. -q, --quiet: Give less output. Option is additive, and can be used up to 3 times (corresponding to WARNING, ERROR, and CRITICAL logging levels). --proxy: Specify a proxy in the form [user:passwd@]proxy.server:port. --trusted-host: Mark this host as trusted, even though it does not have valid or any HTTPS. --cert: Path to alternate CA bundle. --client-cert: Path to SSL client certificate, a single file containing the private key and the certificate in PEM format. --retries: Maximum number of retries each connection should attempt(5 times by default). --timeout: Set the socket timeout(15 seconds by default). --exists-action: Default action when a path already exists: (s)witch,(i)gnore, (w)ipe, (b)ackup, (a)bort). --cache-dir: Store the cache data in .--no-cache-dir: Disable the cache. --disable-pip-version-check: Don't periodically check PyPI to determine whether a new version of pip is available for download. Implied with --no-index. Finding required packages:To search any package, i.e. Flask command will be as shown below: pip search Flask The following output will be displayed with all packages and description: Flask-Cache - Adds cache support to your Flask applicationFlask-SeaSurf - An update CSRF extension for FlaskFlask-Admin - Simple and extensible admin interface framework for FlaskFlask-Security - Simple security for Flask appsFlask - A microframework based on Werkzeug, Jinja2 and good intentions Installing a package: To install the required package, in our case it is Flask, enter the following command : pip install Flask Pip – Show information To check information about the newly installed packages enter: pip show Flask  --- Name: Flask  Version: 0.10.1  Location: /usr/local/lib/python2.7/dist-packages  Requires: Werkzeug, Jinja2, itsdangerousUninstalling a package:To uninstall any package installed by PIP, enter the command given below. pip uninstall Flask Uninstalling Flask: ... ..... Proceed (y/n)?  Successfully uninstalled Flask That’s all. The PIP application has been uninstalled. How to Upgrade PIP for Python Although PIP application doesn’t receive updates very often, it’s still important to keep the application up to date with the newer versions because there may be important fixes to bugs, compatibility, and security holes. Fortunately, upgrading to the latest versions of PIP is very fast and simple. On Windows python -m pip install -U pip On Mac, Linux, or Raspberry Pi pip install -U pip Certain versions of Linux and Raspberry Pi, pip3 needs to be entered instead of pip. Using Requirement FilesThe pip install command always installs the latest published version of a package, but you should install the particular version that suits your code. You would want to create a specification of the dependencies and versions that you have used while developing and running your application, so that there are no surprises when you use the application in production. Requirement files allow you to specify exactly the packages and versions that should be installed on your system. Executing pip help shows that there is a freeze command that displays the installed packages in requirements format. This command can be used to redirect the output to a file to generate a requirements file: The freeze command is used to dump all the packages and their versions to a standard output, so as to redirect the output to a file that can be used to install the exact requirements into another system. The general convention is to name this file requirements.txt, but it is completely up to you to name it whatever you want. If you want to replicate the environment in another system, run pip install specifying the requirements file using the -r switch:The versions listed in requirements.txt will match those of the packages: $ pip list Package    Version  ----------        ----------  certifi2018.11.29  chardet3.0.4  idna   2.8  pip    19.0.1  requests     2.21.0  setuptools  40.6.2  urllib31.24.1 You may submit the requirements.txt file to source control and can use it to create the exact environment in other machines. Fine-Tuning Requirements The problem with hardcoding the versions of your packages and their dependencies is that the packages receive frequent updates with bug and security fixes, and you probably want to update to them as soon as they are published. The requirements file format gives you a bit of flexibility to ensure that the packages are up to date by allowing you to enter dependency versions using logical operators, although specifying the base versions of a package. Make the following changes by opening the requirements.txt file in your editor: certifi>=2018.11.29  chardet>=3.0.4  idna>=2.8  requests>=2.21.0  urllib3>=1.24.1 Change the logical operator to >= to install an exact or greater version that has been published. While you set a new environment using the requirments.txt file, pip searches for the latest version that supports the requirement and installs it. The packages in your requirements file can be updated by running the install command with the --upgrade switch: In this case nothing was upgraded because latest versions have already been installed, but if a new version was published for a listed package, then the package would’ve been upgraded. New versions can introduce changes that fix bugs and will make or break your application. In order to fine-tune your requirements, the requirements file syntax supports additional version specifiers. Let us assume that a new version 3.0 of requests is published but it breaks your application as it introduces an incompatible change. In such a case, the requirements file can be modified to prevent 3.0 or higher versions from being installed: certifi>=2018.11.29 chardet>=3.0.4 idna>=2.8 requests>=2.21.0, =1.24.1Changing the version specifier for the requests package ensures that only the versions which are less than 3.0 get installed. Production vs Development DependenciesAll packages which are installed during the development of your applications are not going to be application dependencies. During the development process, there are certain packages published to PyPI that are development tools or libraries that can be useful to you. For example, you would require a unit test framework in order to unit test your application. Pytest is a popular framework for unit testing. You would want to install the unit testing framework in your development environment, but not in your production environment because it is not an application dependency. To set up a development environment, you need to create a second requirements file (requirements_file.txt) to list additional tools: # In requirements_file.txt  pytest>=4.2.0 To do this, you need to install both requirement files using pip: requirements.txt and requirements_file.txt. Pip allows for specifying additional parameters within a single requirements file. The requirements_file.txt can also be modified to install the requirements from the production requirements.txt file: # In requirements_file.txt  -r requirements.txt  pytest>=4.2.0 Notice that the exact same -r switch is being used in order to install the production requirements.txt file. The file format of the requirements file allows you to specify additional arguments right on a requirements file. Alternatives to pipPip is an essential tool for all Pythonistas which is used in developing many applications and projects for package management. This article gives you the basics of Pip for Python but the Python community is very active in providing great tools and libraries for developers using other applications as well. These include alternatives to pip that try to improve and simplify package management. Here are some package management tools other than pip which are available for Python: CondaPoetryPipenvSummaryBy now you know that pip is a package manager for Python that is used in many projects to manage dependencies. It is included with the Python installer, hence it is essential for all Python programmers to know how to use it. Although Python provides a wide range of standard libraries which are suitable for developing all types of applications, the active Python community provides more sets of tools and libraries that speed up the development process of a Python application. In this article, we have covered: The process of installing pip in Python Setting an environment variable for pip Commonly used commands in pip and their functions Finding and installing new packages using pip with requirement files in the command line and getting information about the newly installed package How to uninstall a package in pip? In addition to the above topics we have also covered the importance of keeping dependencies updated and a few alternatives to pip that can help managing those dependencies. 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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What is pip, Getting Started with Python pip

Pip is a package manager for Python that allows yo... Read More