In 2020 if you are starting with machine learning, deep learning or with data science or if not that and you are already an expert in these fields and looking for a change then you have landed on the right page.
Today we gonna discuss the top 5 free and most amazing Python IDE for machine learning and data science, if you are an expert or a beginner these IDE’s for everyone who loves machine learning & data science.
According to CNBC artificial intelligence (AI) will generate 2.3 million jobs in 2020 and there are around 8,705 startups and companies listed in Crunchbase who work on artificial intelligence & data science.
IDE stands for Integrated Development Environment and it offers a text editor, automated code validation, syntax highlighting, completion, contextual suggestions, method and class specification & debugging tools.
In the ecosystem of Python, there are hundreds of IDE that offers support for machine learning and data science and every other one has their own uniqueness, functionality & customer base.
All the IDE’s listed below and my personal favorites and are selected by a discussion with the machine learning & data science community to whom I belong or I know. They are also not ranked as per the number.
The first IDE on our list for machine learning and data science is Rodeo. It offers a development environment that is lightweight, intuitive and yet customizable, it comes with all the essential features for ML & DS.
The Rodeo was developed by Yhat. Rodeo is exclusively built for doing machine learning and data science in Python. Rodeo has similar feelings & features like Sypder which is another best IDE for ML & DS.
Rodeo makes it very easy to explore, compare and also interact with the data frames and plots. It comes with an auto-completion, syntax highlighting, and built-in IPython support so that you can code faster.
Also, Rodeo comes with Python tutorials integrated within which makes it quite favorable for the users. In addition to all of this that this IDE also comes with cheat sheets that help in referencing helpers material very quickly.
It is very useful for researchers and scientists who are used to working in R and RStudio IDE, as it consists of many features that are similar to Spyder, although it lacks many features such as code analysis, PEP 8, etc.
Sometime Rodeo is also referred to as the RStudio clone as it uses the Ace Editor as its underlying layer, just the same as what powers RStudio but the difference that comes is that Rodeo is browser-based version.
The only thing that I don’t like about Rodeo is that when you install the IDE on your pc or laptop it will auto-update itself every time a new version of the same is released.
PyCharm is one of the most popular IDE’s for machine learning and data science. PyCharm is built by JetBrains & it comes with two editions, first, one is Professional Edition & second one is PyCharm Community Edition.
The Professional Edition comes with a 30-days free trial and after that, it has different plans to offers extra premium features but as I developer I hardly find them using on daily bases so I mostly suggest the PyCharm Community Edition which is a free edition, for beginners or for a professionals it is complete power packed tool.
PyCharm is used by big companies such as HP, Pinterest, Twitter, Symantec, Groupon, etc for the purpose of Machine Learning is due to its ability to provide support for important libraries like Matplotlib, NumPy & Pandas.
PyCharm has an advanced Lint-like checker providing feedback right while you type or see the code. It also contains PEP-8 for python that enables that helps the programmers to write neat code.
It supports both single files and multi-file projects with the same ease. You can step through the Python implemented standard library too if you wish. It supports virtual environments with ease.
It even supports RST inspection for your documentation.
The developers can even customize the PyCharm UI according to their specific needs and preferences. Also, they can extend the IDE by choosing from over 50 plug-ins to meet complex project requirements.
PyCharm makes it easier for developers to implement both local and global changes quickly and efficiently. The developers can even take advantage of the refactoring options provided by the IDE.
3) JuPyter/IPython Notebook
Jupyter Notebook or say IPython Notebook is my personal favorite IDE for machine learning and data science. It’s minimalized design and well-structured toolbar make the workflow very smooth and quick.
The main reason for which I mostly use Jupyter Notebook is for documentation, it has so many features like you can describe the matplotlib code along with the resulted graph, which makes it a very handy tool, it is very easy to share the documents with equations, visualization and most importantly live codes.
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.
Jupyter Notebook provides features like interactive widgets from which the code can produce output such as images, videos. These widgets also allow you to manipulate and visualize the data in real-time.
Along with Python Jupyter Notebook supports around 40 programming languages. It is completely open-sourced and it was launched in 2014 as a combination of an IDE and a server to run your projects.
Jupyter Notebook is completely open-sourced Soon this project was started to support data science and scientific computing across most of the programming languages. Jupyter Notebook constitutes the main three components – notebook web applications, kernels, and notebook documents.
Jupyter Notebook runs/ interpreted via kernels, which seem like virtual machines and will use the memory of the computer running it. The memory will not be released until exiting the execution of the notebook.
Spyder is also a very popular IDE for machine learning and data science. It is an open-source cross-platform IDE & offers plenty of features & support for various libraries like NumPy, SciPy, Matplotlib.
It also supports variable explorer where one can explore and edit the variables that are created during the execution of file from a graphic user interface like Numpy array ones.
It offers advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution& visualization capabilities of a scientific package.
The name stands for Scientific PYthon Development EnviRonment and was released on 18th October 2009.
Spyder is a lightweight IDE with quick and easy installation and comes with detailed documentation. Spyder is distributed with Anaconda. So, when you install Anaconda, you have Spyder as well in your system.
The abilities of the Spyder IDE can be extended using API and plugins. It profiler allows finding and eliminating inefficiencies in the code, the debugger allows tracing each step of the code’s execution interactively.
Spyder is fully written in Python and under the hood, it uses Qt for its Graphical User Interface and is designed to use either Pyside or PyQt python bindings and supports Linux, Windows, and Mac OS.
Geany is also an interesting choice for machine learning and data science. At its core Geany written in C and C++, the IDE is authored by Enrico Troger and was released on 25th October 2019.
Even though Geany is very new to the machine learning and data science world, it is a lightweight IDE capable of doing most of the tasks that other IDE’s can do.
Geany supports line numbering and highlighting of the syntax and also supports code navigation. It is equipped with features like auto-closing of braces, auto XML and HTML tag closing and many more.
I find Geany a good compromise between a basic text editor and a full-blown IDE. Of the IDEs I’ve tried, I generally find them cluttered with irrelevant options, which Geany avoids.
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