5 Best Python Machine Learning IDEs

In this article we are going to discuss about best Python Machine Learning IDEs and will find out which one suits you according to your needs. Also we will be deciding what should be the system requirements and hardware configuration of our machine to run these IDEs smoothly without any lag. So without wasting a moment let us get straight to the point.

IDE (Integrated Development Environment)

As it is very much clear from the name itself what an IDE is and for most of the people here who are into programming this is not a new term. So here we are talking about different IDEs that are available for us as a data-engineer/enthusiast and to decide which one will be an ideal choice according to our needs.

Best Python Machine Learning IDEs

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So, here we are going to mention five IDEs that are being helpful for the data scientists and engineers and are productive too. Based on their respective features you will be very easily to choose an IDE of your choice. Come let’s explore.

5 Best Python Machine Learning IDEs

1. Spyder

Coming to our very first focus i.e. Spyder. This IDE got this short name from it’s name itself: “Scientific Python Development Environment”. Pierre Raybaut is the author of Spyder and it got officially released on October 18, 2009 and is written solely in Python.

Features at a glance:

  • Very simple and light-weight IDE with detailed documentation and quite easy to install.
  • This is an open source editor and supports code completion, introspection, goto definition as well as horizontal and vertical splitting.
  • This editor comes with a Documentation Viewer where you can see the documentation related to classes or functions you gotta use.
  • Like most of the IDEs, this also supports Variable Explorer which is a helpful tool to explore and edit the variables that we have created during file execution.
  • It supports runtime debugging i.e. the errors will be seen on the screen as soon as you type them.

This IDE integrates with some of the crucial libraries like NumPy, Matplotlib, SciPy etc.

Spyder is considered best in the cases where it is been used as an interactive console for testing and development of scientific applications and scripts which make use of libraries such as SciPy, NumPy and Matplotlib.

Tip: Want to download? No need to bother we’ve got you covered. Click here to download your version of Spyder.

2. Geany

Geany is primarily a Python machine learning IDE authored by Enrico Troger and got officially released on October 19, 2005. It has been written in C & C++ and is a light-weight IDE. Despite of being a small IDE it is as capable as any other IDE present out there.

Features at a glance

  • Geany’s editor supports highlighting of the Syntax and line numebering.
  • It comes equipped with the features like code completion, auto closing of braces, auto HTML and XML tags closing.
  • It also comes with code folding.
  • This IDE supports code navigation.

Tip: Download your instance of Geany here.

3. Rodeo

This is special we got here. This is a Python IDE that primarily focuses and built for the purpose of machine learning and data science. This particular IDE use IPython kernel (you will know this later) and was authored by Yhat.

Features at a glance

  • It is mainly famous due to its ability to let users explore, compare and interact with the data frames & plots.
  • Like Geany’s editor this also comes with a editor that has capability of auto-completion, syntax highlighting.
  • This also provides a support for IPython making the code writing fast.
  • Also Rodeo comes with Python tutorials integrated within which makes it quite favourable for the users.

This IDE is well known for the fact that for the data scientists and engineers who work on RStudio IDE can very easily adapt to it.

Perfection doesn’t exist and so is the case for Rodeo, it doesn’t consist of code analysis, PEP 8, etc.

Tip: Download your Rodeo workspace here.

4. PyCharm

PyCharm is the IDE which is most famous in the professional world whether it is for data science or for conventional Python programming. This IDE is built by one of the big company out there that we all might have heard about: Jetbrains, company released the official version of PyCharm in October 2010.

PyCharm comes in two different editions: Community Edition which we all can have access to essentially for free and second one is the Professional Edition for which you will need to pay some bucks.

Features at a glance

  • It includes code completion, auto-indentation and code formatting.
  • This also comes with runtime debugger i.e. will display the errors as soon as you type them.
  • It contains PEP-8 that enables writing neat codes.
  • It consist of debugger for Javascript and Python with a GUI.
  • It has one of the most advanced documentation viewer along with video tutorials.

PyCharm being accepted widely among big companies for the purpose of Machine Learning is due to its ability to provide support for important libraries like Matplotlib, NumPy and Pandas.

Also PyCharm is capable of distinguishing between different environments (Python 2.7, Python 3.5) according to different project’s needs.

Tip: Download your version of Pycharm here.

5. JuPyter Notebook or IPython Notebook

Due to its simplicity this one became a sensational IDE among the data enthusiasts as it is the descendant of IPython. Best thing about JuPyter is that there you can very easily switch between the different versions of python (or any other language) according to your preference.

Features at a glance

  • It’s an open source platform
  • It can support up to 40 different languages to work on including languages beneficial for data sciences like R, Python, Julia, etc.
  • It supports sharing live codes, and even documents with equations and visualizations.
  • In JuPyter you can produce outputs in the form of images, videos and even LaTex with the help of several useful widgets.
  • You can even avail the advantage of Big Data tools due to the fact that JuPyter has got Big Data integration within to help the data scientists.

Tip: Download the JuPyter IDE here.

Conclusion

Since we have gone through all the IDEs that are famous in the field of Data Sciences and Machine Learning, now you must be able to make your choices based on the points we’ve discussed above.

Pro Tip: We would recommend our readers to use JuPyter Notebook if you are giving a start to ML. It has got that simplicity and features that most of the IDEs have combined. Apart from the features discussed above it also supports for data cleaning, transformation, etc. to help its users. Talking in terms of ML JuPyter has a good support for the libraries like Pandas, NumPy and Matplotlib. You can get complete guide on how to install and configure JuPyter here.

Comment down below if you know about any other good machine learning ide for python.

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