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Python is such a popular language that most 'programmers' text editors' have at least rudimentary support, including syntax highlighting. But there are several editors that have especially good support. I've tested the following editors, presented in alphabetical order, as most are fine choices:
Another cross-platform editor, Bluefish is available for Windows, Linux (various distros) and Mac. Its developers pride themselves on its lightweight design; the program is speedy and can handle.
- Python is such a popular language that most 'programmers' text editors' have at least rudimentary support, including syntax highlighting. But there are several editors that have especially good support.
- The set of features comes with an IDE for Python are depended on how good and Rich that Python IDE is, so we have prepared some of the best Python IDE list which you should use if you want to write rich Python code. Best Python IDE. Pycharm (Windows, Mac, Linux) Pycharm is one of the IDE specifically made for Python programming.
- The best python coding solution for the visually impaired as I know is emacs with emacspeak and elpy. I personally use emacs-mac with emacspeak and go-mode to write go code on mac, It does really well.
- 4 thoughts on “ 5 Best Python IDEs for Windows/Mac/Linux ” Dara Sikandar March 16, 2018. I am a new student of programming from a university and i am learning c++ programming so, which editor should i use for effective programming.
- Python is such a popular language that most 'programmers' text editors' have at least rudimentary support, including syntax highlighting. But there are several editors that have especially good support.
Emacs
Emacs is not really a single text editor; it's more a family of text editors that is almost 40 years old, starting with TECO EMACS, which was a set of text-editing macros implemented by Richard Stallman using the TECO editor/programming language, and continuing to be developed today with GNU Emacs, also created by Stallman. There have been other Emacsen, including Gosling Emacs and most prominently XEmacs, but they have all been mostly supplanted by GNU Emacs. It runs on Windows. Mac OS X, Linux, BSD, Haiku, Minix, Android — more or less everywhere.
Emacs' claim to fame is its extensibility, which has allowed its users to create editing modes for almost everything, really. Emacs has modes for every major programming language and most minor ones; it can serve as a newsreader, an email client, a web browser, terminal emulator, image viewer, and blogging client; it has a package manager, Bible-study tools, a web server — you begin to see the point. Emacs is huge for a text editor, but it has been called an operating system for a reason.
Python has not been neglected by Emacs extenders;
python-mode
is included in the base distribution, which allows editing of Python code with syntax highlighting; automatic indentation; descriptions of keywords, modules, classes, and more on the fly; snippet insertion; an interactive Python REPL in a split window with the ability to do partial recompilation; code folding; and more. With the addition ofanything-ipython
, available using the package manager, powerful syntax completion is easily available, including any modules that you import. There are also several packages for integrating unit testing, virtualenv, pylint, on-the-fly error indication, and more. With thepymacs
package, you can even use Python to extend Emacs itself, though I don't recommend it if you think your extensions might ever be useful to anyone else.The downside of Emacs is its insane learning curve. Its model of text is different than anything you're used to, its keyboard shortcuts are nothing like today's de facto standard, and its look and feel is straight out of 1985. It does, however, have a tutorial built in — read the opening screen carefully to see how to open it — and there's much more documentation accessible on the fly, once you've learned to use it. And once you've learned to use it, you can use it to do
anything.
anything.
In short, to be effective, Emacs must be a way of life — but it's a good life if you stick to it. This article was written with Emacs; everything I finish is written using Emacs.
Geany
Geany is a cross-platform programmers' text editor that supplies the very most basic features of an IDE. It has Python syntax highlighting; rough auto-indentation, though no auto-deindentation after
return
and break
statements; reasonable code navigation; code folding; stack trace parsing to locate errors; and, with an extra plugin, some decent snippets — and that's about it.Geany does what it does well, but what it can't do well, it doesn't do at all. Its main advantage is that it satisfies the minimum requirements for a serious Python programmer's editor while having a gentle learning curve. If you don't have an editor to which you're attached, and you don't want to put the time into learning Emacs or Vim, Geany is not a bad choice.
Komodo Edit
Komodo Edit is an editor/light IDE comprised of the free subset of ActiveState's Komodo IDE. The good points:
- Auto-indentation works well
- Project and code navigation is effective
- Code folding works well
- Auto-completion works well within a project
- Snippets are excellent and well-handled
There are some serious drawbacks, however:
- Auto-completion does not work for non-standard modules
- There is no Python console
- There is no debugging or even built-in support for running your scripts
- Project handling is low-functioning and opaque
The functionality available does not justify the IDE-like interface overhead.
Notepad++
Notepad++ is at base a decent text editor, but it's barely a programmer's editor, at least for Python. It has acceptable syntax highlighting, but that's about all it has to offer. It has word- and function-level auto-completion based on the current file, which is almost no help; nominal auto-indentation which does not function at all for Python; and that's all, frankly. Its only recommendations are that it's easy to learn and better than its namesake.
Unless you're tied to Notepad++ for some reason, Geany or one of the other editors recommended here is probably a better choice.
SciTE
SciTE was originally a demonstration application for the
Scintilla source-code editing component, but it got out of hand and turned into a real text editor scriptable in Lua. Despite its history, it's actually quite nice, and does most of the text-editing work Geany above does; what it lacks is the project- and file-navigation functionality. It is capable of very rough auto-completion using token files, if that's useful to you; it isn't to me.
Scintilla source-code editing component, but it got out of hand and turned into a real text editor scriptable in Lua. Despite its history, it's actually quite nice, and does most of the text-editing work Geany above does; what it lacks is the project- and file-navigation functionality. It is capable of very rough auto-completion using token files, if that's useful to you; it isn't to me.
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SciTE has two advantages: it's light and fast, and it's easy to learn. Like Geany, it is a minimum editor for serious Python programmers, and if that's all you want, it might be a good choice.
SPE (Stani's Python Editor)
SPE, which is about midway between a dedicated Python text editor and a Python IDE, looks very promising but ultimately fails to deliver an acceptable editing experience. The basic editor is effective enough and immediately usable, and it provides a number of good tools, including a well-integrated Python console, an effective search panel, and file browser.
The website for SPE promises syntax highlighting, auto-completion, auto-indentation, syntax checking, wxPython GUI designer, and integrated debugger, but some features are more successful than others. I tested the latest stable release, 0.8.4.h, and while syntax highlighting, auto-indentation, syntax checking, and the GUI designer all functioned acceptably, auto-completion was unusably bad; outside of the most common parts of the standard library, suggestions seemed to be simply pulled from a list of every token in the file. The integrated debugger completely failed to work; the editor crashed hard every time I tried to invoke it.
While SPE looks like a good start at a Python editor or proto-IDE, the last stable release was in 2008, so it looks decreasingly likely that its initial promise will come to fruition.
Sublime Text
Sublime Text is a seriously nice programmers' editor, and it has the advantage of being attractive — like, Mac-level pretty. It has a remarkable selection of powerful features, including multiple cursors (allowing the same edit to be made in multiple places at once), a flexible 'goto anything' interface, a command palette that allows easy keyboard-based access to all of Sublime Text's functionality, split-screen editing, distraction-free mode, and two kinds of customization: a relatively simple one using JSON, and a more advanced plugin API using Python.
All of these capabilities are attractive, and they are almost enough to recommend the editor by themselves. However, there are a number of flaws. Auto-indentation exists, but does not handle much beyond adding a level of indentation after each colon. Auto-completion is available via the SublimeCodeIntel or SublimeRope plugins, but is not very advanced in either case. The great part is that since the editor is so extensible, you can fix these problems if you wish.
I can't fully recommend Sublime Text, based on the poor performance of the plugins in my test, but the editor is so excellent that if you're not a frequent user of auto-completion, you might consider it. I could see using this as my primary editor for Python if I weren't otherwise attached.
Vim
Vi is the anti-Emacs, and also an excellent text editor. Created in 1976, it is old in software terms, almost as old as Emacs. It is light, fast, and is almost always installed by default in Linux and other Unixes. Vim — vi improved — is the most common implementation of vi today, though there are others. Vim or elvis, another clone, is available on Windows, Mac OS X, BSDs, Minix, Haiku, and most other operating systems. Vim has extensions to allow it to edit almost any programming language, no matter how obscure.
Vim has syntax highlighting, code folding, and automatic indentation built in, and with some modeline comments in each source file or a few additions to its settings file,
Turning Vim into a modern Python IDE).
.vimrc
, it can ensure that you don't mix spaces and tabs. With rope-vim
, python-mode
, or jedi-vim
it can do auto-completion, including non-system modules, and there are a host of modules that let it do almost anything with Python code that is listed for Emacs above. Vim is also extensible using a built-in scripting language, VimScript, though in general Vim users have refrained from the exuberant extension characteristic of Emacs. (I'm no vim expert, but John Anderson is; for advanced tips on setting up vi for Python, see his article onTurning Vim into a modern Python IDE).
Vim, like Emacs, has a learning curve like a smack in the face — maybe even steeper than Emacs, since it is a modal editor, in which different categories of operations, such as navigation and editing, are active at different times. Vim has excellent help built in, accessed by entering
:help
; it includes reference guides and a tutorial.Vim, in fine, is another text-editor-as-lifestyle. Most committed vim users use vim for almost all their editing.
Editors: Summary
There are a lot of decent editors for Python, but a few stand out: there's the lifestyle editors, Emacs and Vim, both of which are powerful but have vicious learning curves. There's SciTE and Geany, which, like any number of similar editors, are a good basic fit for someone who wants to get into Python programming without putting a lot of effort into their editor. And finally, there's Sublime Text, which, while flawed, has a number of unique features and looks poised to become for this generation what Emacs and Vim have been to previous ones: their own editor for everything.
Next, we'll discuss IDEs: Best IDEs for Python Development.
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Tutorials
The best Python IDEs for data science that make data analysis and machine learning easier!
Check out our new Top Python IDEs for 2019 tutorial.
IDE stands for Integrated Development Environment. It’s a coding tool which allows you to write, test, and debug your code in an easier way, as they typically offer code completion or code insight by highlighting, resource management, debugging tools,… And even though the IDE is a strictly defined concept, it’s starting to be redefined as other tools such as notebooks start gaining more and more features that traditionally belong to IDEs. For example, debugging your code is also possible in Jupyter Notebook.
You can probably most clearly see this evolution in the results of the Stack Overflow Developer Survey below, which also includes these new tools, next to the traditional IDEs that you might already know; They all fall under the section “development environment”.
Because of all the features that IDEs have to offer, they are extremely useful for development: they make your coding more comfortable and this is no different for data science. However, given the fact that there aren’t only the traditional IDEs to consider, but also new tools, such as notebooks, you might be wondering which development environment to use when you’re just starting out with data science.
IDEs Versus Text Editors
The truth is that you can code in almost any software, from prompt command to Windows notepad, but you may also want a proper programming environment which combines coding facility with a debugging environment.
So why would or do you choose a traditional IDE instead of, for example, a notepad?
The answer would be practicality. For instance, imagine that you are coding in any text editor like Windows notepad. When your code is ready, you’ll need to run it. You can’t execute your program in a text editor like this, so you must use a prompt command to do it. Rather than use two different programs, wouldn’t better have it all in just one place? That’s what an IDE is ready for.
But, take a closer look at the image above. Notepad++, which is a text editor in its essence, is one of the tools most used by Stack Overflow community.
Isn’t that weird?
Well, many text editors can be used as a development environment if you make use of plugins: when you’re working with Notepad++, for example, you can use these plugins such as the DBGP for Notepad++ to install a debugger in the application, allowing you to run and analyze your code directly in your favorite text editor.
Best Free Editor For Python Mac
And this immediately explains why text editors can be so popular: they not only offer you the blank slate, but they also give you the option to add features that you might need. This stands in clear contrast with IDEs such as Visual Studio and Spyder, where these features are built in and you don’t need to install anything else to start developing, but where the learning curve might be a little bit steeper for users.
The Top 5 Development Environments
Creating a list with just five development environments for data science with Python is a hard task: you might not only want to consider the possible learning curve, price or built-in/downloadable features, but you also might want to take into account the possibility to visualize and report on your results, or how easy a certain the environment is to collaborate with others.
You’ll see that your choice will balance all of these things and that the ‘best’ development environment for you will be the one which makes your life easier and your work more comfortable. This means that you could potentially also switch between IDE, notebook and text editor according to whatever is more useful for you!
That’s why it’s best to see this list as a guide of software to test before you pick your favorite.
Spyder
If you have the Anaconda distribution installed on your computer, you probably already know Spyder. It’s an open source cross-platform IDE for data science. If you have never worked with an IDE, Spyder could perfectly be your first approach. It integrates the essentials libraries for data science, such as NumPy, SciPy, Matplotlib and IPython, besides that, it can be extended with plugins.
You should try it out because… Different of most of IDEs around the web, Spyder was built specifically for data science. It may not be as charming as another IDEs such as Visual Studio or Atom, but give it a try! The learning curve is so smooth that you will master it in a blink of an eye. If you are a beginner, you’ll like to use features like the online help, which allows you to search for specific information about libraries.
Best Text Editor For Python Machine Learning
Note also how this interface is quite similar to RStudio; That’s why, if you’re switching between Matlab or R to Python, this is the way to go.
Features Spyder contains features like a text editor with syntax highlighting, code completion and variable exploring, which you can edit its values using a Graphical User Interface (GUI).
Data science enthusiasts say…
“If you are switching from Matlab or Rstudio to Python; Spyder is the way to go, It very intuitive for scientific computing.”
Download Spyder is free and it’s available for Windows, MacOS and major Linux distributions, like Debian, Fedora, and Ubuntu. You can install Spyder by downloading Anaconda on Continuum’s website.
PyCharm
PyCharm is an IDE made by the folks at JetBrain, a team responsible for one of the most famous Java IDE, the IntelliJ IDEA.
You should try it out because… PyCharm is perfect for those who already have experience using another JetBrain’s IDE, due to the fact that the interface and features be similar. Also, if you like IPython or Anaconda distribution, it’s nice for you to know that PyCharm integrates its tools and libraries such as NumPy and Matplotlib, allowing you work with array viewers and interactive plots.
In addition to Python, PyCharm provides support for JavaScript, HTML/CSS, Angular JS, Node.js, and so on, what makes it a good option for web development.
Features Just like other IDEs, PyCharm has interesting features such as a code editor, errors highlighting, a powerful debugger with a graphical interface, besides of Git integration, SVN, and Mercurial. You can also customize your IDE, choosing between different themes, color schemes, and key-binding. Additionally, you can expand PyCharm’s features by adding plugins; You can take a look at the PyCharm Plugins Library here.
Data science enthusiasts say…
“I have tried most of the popular IDE’s for Python and hands down the best one in my opinion is PyCharm. It has a very nice debugger, plays nicely with git, and works easily with the use of multiple Python versions with virtualenv. Reindexing is relatively fast, and I like the interface. The community version is free and does not at all feel like it is lacking.”
Download You can download the Lightweight PyCharm IDE for Python and scientific development for free here. It’s available for free for Windows, macOS, and Linux.
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Thonny
The next IDE is Thonny: an IDE for learning and teaching programming. It’s a software developed at The University of Tartu, which you can download for free on the Bitbucket repository for Windows, Linux, and Mac.
Among its features, Thonny supports code completion and highlight syntax errors, but it also provides a simple debugger, which you can run your program step-by-step. This is very nice for beginners, as they can step through statements and expressions. While editing a function, a new window is opened with local variables and the code being shown separately from your main code. The purpose of Thonny is to give you a good understanding of how Python works under the hood.
Atom
An open source text editor developed by Github. That sounds great, right?
That’s exactly what Atom is.
You should try it out because… Although this text editor is available for many popular programming languages such as Ruby on Rails, PHP, Java and so on, Atom has interesting features that create a good experience for Python developers.
Features One of the best advantages of Atom is its community, chiefly due to the constants enhancements and plugins that they develop in order to customize your IDE and improve your workflow.
For instance, One of these plugins - called “Packages” - is the Data Atom, which allows you to write and execute SQL queries. It supports PostgreSQL, Microsoft SQL Server, and MySQL. Besides that, you can also visualize your results on Atom, without open any other window. Additionally, you also have a plugin called “Markdown Preview Plus”, which provides you with built-in support for editing and visualizing Markdown files and which allows you to open a preview, render LaTeX equations, and much more!
As you could have already expected, Atom’s integration with git is awesome. And, as other IDEs, it allows you to use multiples panes, themes, and colors, managing multiples projects.
Do you want to know one of the downsides? Atom might have a weak performance on older CPUs.
Data science enthusiasts say…
“I’m using hydrogen for Atom, it’s very fast and useful, it’s worthwhile try out. […] Very recommended.”
Tip Use Atom in combination with Hydrogen, a package that lets you run your code directly in Atom using any Jupyter kernels you have installed.
Download Based in Electron - also known as Atom Shell, a cross-platform desktop applications framework by using Chromium -, Atom is available for free for Windows, OS X, and Linux. You can download it clicking here!
Jupyter Notebook
Jupyter Notebook was born out of IPython in 2014. It is a web application based on the server-client structure, and it allows you to create and manipulate notebook documents - or just “notebooks”.
You should try it out because… Jupyter Notebook provides you with an easy-to-use, interactive data science environment across many programming languages that doesn’t only work as an IDE, but also as a presentation or education tool. It’s perfect for those who are just starting out with data science!
Features The Jupyter Notebook supports markdowns, allowing you to add HTML components from images to videos. Thanks to Jupyter, you can easily see and edit your code in order to create compelling presentations. For instance, you can use data visualization libraries like Matplotlib and Seaborn and show your graphs in the same document where your code is. Besides all of this, you can export your final work to PDF and HTML files, or you can just export it as a .py file. In addition, you can also create blogs and presentations from your notebooks. If you want to know more about the features that Jupyter has to offer to you, check out this article.
Data science enthusiasts say…
“Jupyter Notebook should be an integral part of any Python data scientist’s toolbox. It’s great for prototyping and sharing notebooks with visualizations.”
Tip If you want to know everything about the Jupyter Notebook, the installation process and how to get started with this tool, check out this tutorial that will guide you through this awesome data science tool.
Download Jupyter Notebook is easy to install and easier to use. You can download it here.
Other IDE Alternatives To Consider
What’s the best IDE for you?
The answer is simple: that one which makes your life easier and your work more comfortable.
The purpose of this list is to give you good references to start off. You can test each one and give your considerations about what it’s good and bad at. In addition to that, you can try some alternatives, and maybe you’ll even find them better than the ones that are put in the top 5!
For instance, nteract could be a good alternative for those who are looking to focus more on writing a code-driven story. nteract is a desktop application that allows you to create notebooks just like Jupyter Notebook. You already see: instead of working in the browser like with Jupyter, you actually download nteract and execute the application to be able to develop beautiful documents with code, words, and images. With this installation procedure, you have a terminal-free experience, which could be even better if you’re new to coding.
Accessible for beginners is key to the features that you’ll find in nteract: you can execute cells, just like in Jupyter Notebook, but you can also move them around by dragging and dropping them. You can also pin cells to the top of the notebook, which stick there as you scroll through the document.
It’s available for Windows, Mac, and Linux, besides of support
ipynb
files - IPython notebooks. You can download nteract.io here.Python Text Editor Mac
Another alternative could be the Visual Studio Code. This is a text editor developed by Microsoft, but which can also be used as an IDE. One good thing about Visual Studio is it Git integration. Like Atom, you can commit, sync and create branches in your repositories easily with this application.
Visual Studio contains a feature called IntelliSense, which provides code completions based on variable types, functions and imported modules. It also provides syntax highlighting and autocomplete function.
You can download Visual Studio Code for Windows, Linux or macOS for free on this page.
As a third alternative option, you could also consider Sublime Text, a powerful text editor. Most people love it for the user interface, the extraordinary features, and performance. Just like with the other text editors that were mentioned above, you can extend Sublime Text with plugins. Check out this page for more information on how to set up Sublime Text to be a light-weight all-in-one data science IDE.
You can download Sublime Text here.
Conclusion
IDEs surely can help you to improve your workflow and make your results profitable. You can write, execute and debug your code easily, aside from customizing windows and colors. Instead of a traditional IDE, you can also try a text editor like Notepad++, but keep in mind that you’ll need to expand it by using plugins (such as PyNPP) if you want to execute your code.
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