Best Python IDEs for Data Science in 2020

Best Python IDEs for Data Science in 2020

If you are a programmer, IDEs are one of the daily tools for you. Is It?  but I will introduce IDEs, even it is too common because most of our readers are new in Data Science and Programming.  So, friends, IDE  is the short form of an Integrated development environment. IDEs facilitates a programmer by providing a complete suite for Source Code Editor and build tool with a debugging feature. Few words for Python, you know very well that Python is one of the emerging languages in every field of software. Whether it is artificial intelligence & machine learning or gaming, Python is one of the trending programming languages. This article will guide you to choose the best Python IDEs for Data science.

Why choose  IDEs?

Most of you must have thought, ” Why to choose Ides“. Actually you can write your code in any text editor. In that scenario, If you are using any text editor, You must use the command prompt to run the code. In the place of using this two different platform for designing, testing, and debugging the code programmer love to use IDEs. Most of the text editors are coming with plugins that transform them completely. Actually plugins give them the functionality of debugging. Have you any time heard about the PDB module? This python module help to debug the code in using the command line and text editor. However, you have to import this module (PDB) in your source code. On the flip side,  If you use any IDE,  you can save your time in such extra efforts. I hope now you are clear with the uses and role of IDEs in programming. Let’s accelerate our journey to find out the Best Python IDEs for Data Science.

Best Python IDEs for Data Science-

Especially if you are a Data Scientist or Data Analyst, You just need a high-performance platform to run your code Right? Here is a complete list of such Best Python IDEs. This is not only for Data science but you can use these IDEs in different python applications whether it is Web Development using Python or any automation python script. One more important thing, Do not think below mention IDEs are only python supportable. Some of them are capable of handling another programming /scripting languages.

1.Spyder

This is one of the best python IDEs for Data science. It is light weighted and capable of running complex python script in the term of computing performance. Mostly machine Learning Engineer or Data Scientist use it as the first priority. You can download Spyder from here. In case you have already installed Anaconda, You need not to explicitly install Spyder IDE. Actually It comes by default with Anaconda distribution. Spyder has so many pre-integrated Data Science libraries like Matplotlib, NumPy, SciPy, etc. You can add more as an extension as per requirement. At last,  Lets us understand why it is  Spyder.

S – Scientific

Py –Python

D- Development

ER- Environment.

Best Python IDEs Spyder
Best Python IDEs Spyder

For more details on Spyder IDE, Please visit Spyder’s official website.

2. Jupyter Notebook –

Documentation and Coding together are easily possible with the Jupyter Notebook. It is also an open-source IDE. Especially for beginners who need more explanation  Jupyter Notebook is the best option. You can download and install Jupyter Notebook from here. Like Spyder, Anaconda distribution has an inbuilt Jupyter Notebook. It has web architecture ( Client-server Architecture ). So You need to turn on the server when you need to run the code.

It is a derived product of IPython. Actually the kernel part for Jupyter is IPython  .you already know, Data Visualization is one of the most important steps in every data science project. Actually it often comes into play in understanding and exploring the data set. Jupyter IDE makes data visualization more iterative. You can add  HTML documents with images and other multimedia components with your source code. This feature of Jupyter IDE enhances the ability of explanation. This is why most of the Data Science bloggers use Jupyter Notebook for educational purposes. Jupyter IDE supports so many programming languages. I do not have any perfect count for this but I assume it is around 40.

Jupyter also supports big data Tool. It means you can use Apache spark and pandas both with Jupyter.

Learning Resources for Jupyter Notebook –

If you love to read the book, I will suggest the best book I found on Jupyter is  LEARNING JUPYTER.  Most of our readers demand video resources for learning. Actually learning through videos can save some time but to go for deep understanding books have a monopoly. I will not go deep into this debate. Frankly speaking its a matter of personal choice to choose books or E-learning videos. If you also love  video learning go for this Udemy course –  ” Learning Path: Jupyter: Interactive Computing with Jupyter

Learning Path Jupyter Interactive Computing with Jupyter Udemy

3. PyCharm –

An awesome product by Jet Brains. An Intelligent IDE “PyCharm”  is not only capable of performing High-performance Data Science related tasks but also it is web development friendly. It prompts errors on the fly and also suggest quick-fixes. Code Navigation and refractory is also quite impressive in PyCharm. You can work on different projects with different Python versions. I mean suppose you are working two projects, In which one supports python 2.x and others require 3.x. Pycharm easily manages this situation for you.  If we talk about its UI appearance, Its amazing and Customizable. Please have a look –

Best Python IDEs PyCharm
Best Python IDEs PyCharm

It has enriched Version Control system Integration with so many external plugins support. PyCharm has two IDE sub-products. One is community version and another is Professional. In which Community is free to use. While the Professional version is not free. However, its trial version is free for a limited time.  To know more about its subscription Package and their respective costing visit PyCharm Official Website’s commerce Section.

You can download PyCharm Community Edition for free from here.

Best Python IDEs PyCharm Download
Best Python IDEs PyCharm Download

Learning Resources for PyCharm –

PyCharm is very user friendly. Even in one sight, you can understand almost 80%  functionality. In case you want to be super-specialist with PyCharm , You can read the book Mastering PyCharm . I am recommending to read this book because this will be a solid background of PyCharm in every context ( Web Development, Data Science, etc . )

4. Visual Studio Code –

This is a Microsoft Product. We usually call it the VS code. This is a multi-purpose IDE for multiprogramming languages.

Visual Studio Code data science python ide
Visual Studio Code data science python ide

5. GEANY-

In your programming journey, Some time we come to a point where we need a light weighted IDE. I think GEANY is one of the best solutions for light-weighted IDE. It has a very small size setup. Apart from this,  like other IDEs, It has all common features code highlighter, line numbering, and code folding feature, etc. You can download this light-weighted IDE from here.

6.ATOM –

Atom is an open-source IDE. You can download the ATOM IDE from here. It is interactive with MIT License. You can also contribute to making it better for others. The full code behind this IDE is available in the GitHub repository of ATOM. It supports so many interactive themes. This theme support gives an awesome UI to developers.

Apart from its great UI, Atom’s IDE detail documentation makes developer life easy. Even after reading the documentation, You still need some help to go for Atom Community for discussion.

Best Python IDEs Atom
Best Python IDEs Atom

7. Rodeo –

Like Spyder, It is also data science specific. The best part of this IDE is, it is Integrated with Basic Python Reference. This gives a quick guide for the beginner. Here is the Github repository of Rodeo. You can discuss your doubt related to Rodeo on its highly active Rodeo community.

Best Python IDEs Rodeo
Best Python IDEs Rodeo

8. Other’s Best Python IDEs –

Apart from these, there are so many other Python IDEs that are also very useful. I have not listed them in the top list. It does not mean they are not ordinary. Actually Every IDEs has a different feature set. We have mentioned the above list for the best Python IDEs for  Data Science. Anyways, here I am listing other popular IDEs by python developer –

1.Eric Python IDE.

2. PyDev IDE.

3. Wing Python IDE.

Next Generation Python IDE

In the current time, Software  Development is quite easy because of open source communities and IDEs. So many prepared codes we get on Stack Overflow and so many other code communities. Just because of the frequent emergence of new Programming languages, It is very difficult to remember the syntax for every programmer. So usually most of the time in coding goes in syntax correction. You know it very well that  IDEs make syntax correction also very easy. IDEs are intelligent enough to detect open braces or semicolons etc. These are able to provide Quick Fixes in code. Most of the IDEs are also capable of code optimization. Now you must be thinking what next? If lots are intelligence already there in IDEs. In the next generation, as AI and Machine learning growing, we can see such IDEs   –

1. Which can predict developers’ code intent . and provide the modular solution from the cloud database of source code. You need not surf websites explicitly to find the code.

2. Cloud base IDEs Like – Eclipse Chi. Frankly speaking, cloud-base IDEs are not future Technology. You can say it latest. Most IDEs have already launched their cloud version. Few IDEs Market players are preparing.  You know the software Industry is for fast movers. I think it’s a new concept for you all.

Cloud base IDEs for Data Science-

Usually, most of the Data Science projects need high-performance resources. You also know it very well that Most of the Deep Learning Libraries like TensorFlow also need GPU. In such cases, Developers have to care about infrastructure. This is not the only case usually every machine learning library needs High configuration Rams with multi-core CPUs.  These are those issues that Data Scientist/ Developer face in day to day life at Hardware end.

Now If we talk about the Software side, Usually we need different environments to run and test our applications. In both of the scenarios, Cloud makes our life easier. Even these days Auto scaling and Load balancing infrastructure are in fashion .Especially the cases where Big Data comes into play. Now we just have to write the code without thinking about infrastructure and the environment. Some of them are free to use and some are having different costs and packages. The best part in the context of Costing is, “Usually you have to pay only when you use them “. Usually, It’s not come with a fixed cost package.  Here I am mentioning few of them –

Codenvy

2. Pythonanywhere

3.  repl.it

4. Koding

In the end, I will say Using Cloud base IDE is a good solution for distributive computing. It is also very popular in different testing types like Load  Testing and Stress Testing. You know what makes me crazy about Cloud Base IDE is – “You can code from anywhere just using the browser“. If you are using a cloud base IDE, You need to carry your office laptop everywhere. Just you need a simple device where you can access the was a browser.

Does UI matter for IDE?

Of course Yes! Like every other software, IDE also needs good UI . Especially The IDE which provides Plug and Plays Architecture to code, needs to be user friendly.  Actually, if UI is good then Platform becomes self-explanatory. You need not too much tool training.  As I have already discussed, Most of the above Python IDE supports multiple themes to give an attractive feel to Programmers. Anyways These all things are quite basic and you certainly know about it. I have explicitly created this section because I want to discuss more on the IDE perspective. You know as a coder, You may need cmd, code editor, variable explorer, Package explorer, and server window at once. IDE provides you all these things in a single place but how efficiently on UI is a question mark.

So from now, Do not say UI is good for an IDE until it provides full customization in different Prospective.   Here I will share my personal experience, I like the black background and green color text for code.  These looks make me crazy about coding.

Best time to Change your IDE?

Its a choice concern. There are so many different opinions on this topic. So there is no such straight answer. Usually when you find your current IDE is –

  1. Slow
  2. Not getting updated from longer.
  3. Having less compatible plugin in Market Place.
  4. If it is not Intelligent to provide you quick fixes for bugs.
  5. Less Community support

If you are feeling the same symptoms on your current  IDE. Do not hang on with your IDE, It will waste your efforts and time. Go for a new one and leverage the technology. Make sure before switching on new IDEs completely check out it that IDE properly satisfies your project scope.

Conclusion-

Apart from these best Python IDEs, There are so many other which are also popular. For Example PyDev for Eclipse. Every IDEs has a common core feature. Extension of functionality achieve by external compatible plugins .changes priority between these IDEs.At last, I will say covering all the information in a single article is not possible. So I am providing you some other useful and trusted external links –

Other Useful External links  Best Python IDEs –

  1.  https://wiki.python.org/moin/IntegratedDevelopmentEnvironments
  2. https://www.python.org/about/gettingstarted/

So much research and development are going on in the Python Data Science world. In this wing the researches, The required tool is IDE. Every other day we hear some promising news about it. Open source communities are contributing their efforts to make development easy and comfortable with the help of IDEs. We have also created this article with the same intent.

I hope You have got the answer to your question “ Best Python IDEs for Data Science “. If you think there is something that we should include in this article. To make this article more informative and complete. Please comment below. You can also contact us via our social channels Data Science Learner Facebook page. We love to interact with our readers. You can also demand new content related to Data Science.

 

Data Science Learner Team

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