An IDE (Integrated Development Environment) is used for software development. An IDE may have a compiler, debugger, and all the other requirements needed for software development. IDEs help in consolidating different aspects of a computer program. IDE is also used for development in Data Science (DS) and Machine Learning (ML) due to its vast libraries.
Various aspects of code writing can be implemented through IDEs like compiling, debugging, building executables, editing source code, etc. Python is a widely used language by coders, and python IDEs help in coding & compiling easily. There are IDEs which are used a lot nowadays, let us see some of the best Python IDEs for DS & ML in the market. Read why python is so popular with developers.
List of Best Python IDEs for Machine Learning and Data Science
Scientific Python Development Environment (Spyder) is a free & open-source python IDE. It is lightweight and is an excellent python ide for data science & ML. It is used by a lot of data analysts for real-time code analysis. Spyder has an interactive code execution pattern which gives you the option to compile any single line, a section of the code, or the whole code in one go.
You can find the redundant variables, errors, syntax issues in your code without even compiling it in Spyder via the static code analysis feature. It is also integrated with many DS packages like NumPy, SciPy, Pandas, IPython, etc. to help you in doing data analytics.
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You can control the execution flow of your source code from the Spyder GUI (Graphical User Interface) via the Spyder debugger. The history log page of Spyder records all the commands used in the editor for further references. You can also know about any built-in function, method, class, etc. in Spyder via the Help Pane of Spyder. It is an excellent tool for data science enthusiasts.
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Thonny is an excellent Python IDE that will run on Windows, Linux, and Mac. The debugger of Thonny helps in debugging codes line by line, this process helps a lot for beginners who are learning to code. The excellent GUI of Thonny makes the installation of third-party packages much easier.
Thonny autocompletes code according to its prediction and inspects the code for bracket mismatching and highlights the error which is a great feature for beginners. It is completely free to download. When you call a function in Thonny, it will be done in a separate window which makes the user understand the local variables & call stack of the function better. The package manager of Thonny helps you in downloading them and increasing the functionality of python.
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It is a web-based python IDE for Machine Learning & DS professionals. You can test your code as you write via the interactive output system of JupyterLab. The interface of JupyterLab is quite good as it provides you a simultaneous view of the terminal, text editor, console, and file directory.
Features like auto code completion, auto-formatting, autosave, etc. make it one of the best free Python IDEs for ML and DS professionals. There is a zen mode in JupyterLab which allows users to minimise distractions, unrequired screens, and focus on the project under process. The files created in JupyterLab can be downloaded in various formats like .py, pdf, etc. You can also download them as slides i.e. ‘.png’.
It is an excellent python IDE which has features like auto code completion, auto code indentation, etc. It has a smart debugger that analyses the code and highlights errors. DS & ML professionals who are into web development prefer PyCharm also because of its easy navigation facility. You can search for any particular symbol used in long codes via the navigation feature in PyCharm. Interlinking multiple scripts is also easier in PyCharm.
One can restructure their code easily via PyCharm’s refactoring feature where you can change the method signature, rename the file, extract any method in code. ML professionals use integrated unit testing to test their ML pipelines.
It helps in knowing the performance of any particular ML model. PyCharm comes with inbuilt integrated unit testing and one can see the results in a graphical layout. It also has a version control system that helps in keeping track of the changes made to any particular file/application.
5. Visual Code
It is also a good platform for beginners as you will get hints in the VS Code whenever you create functions or classes. The auto code completion also helps users to save time while coding. VS Code is also integrated with PyLint which checks errors in the source code. You can perform unit testing on your ML or DS models easily via VS Code.
The REPL (read-evaluate-print loop) helps in seeing quick results of any small python code in a separate window. It helps a lot when one is experimenting with any new API or function.
VS Code makes working with SQL, Unity, .NET, Node.js, and many other tools easier. One can rename a file, extract methods, add imports, etc. in your code via the VS Code refactor. VS Code is an excellent IDE for ML & DS to optimise and debug codes easily.
There are many useful packages in Atom like the atom-beautify package which beautifies your code and makes it more accurate. The outline view feature of Atom lets you see a tree-based view of your code and you can cross-check your classes, functions, etc. easily. Atom will provide you many themes and templates from GitHub to choose from.
ML & DS professionals also prefer Atom because of its ability for cross-platform editing. It is one of the best open-source free IDEs to use currently.
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Machine Learning & Data Science are changing the way of working in web development and other automated processes. A good IDE is required by ML & DS professionals to compile, debug, test their code, and make it error-free. These were some of the best IDEs in the market currently.
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