If you are a programmer , IDEs are one of the daily tool 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 Integrated development Environment . IDEs facilitates a programmer by providing a complete suit for Source Code Editor and build tool with debugging feature . Few words for Python , you know very well that Python is one of the emerging language in every field of software . Whether it is artificial intelligence & machine learning or gaming , Python is one of the trending programming language . This article will guide you to choose the best Python IDEs for Data science .
Why to 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 command prompt to run the code . In the place of using these two different platform for designing , testing and debugging the code programmer love to use IDEs . Most of the text editors are coming with plugins which transform them completely . Actually plugins give them the functionality of debugging . Have you any time heard about PDB module . This python module help to debug the code in using command line and text editor . However you have to import this module (PDB) in your source code .In 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 .Lets accelerate our journey to find out the Best Python IDEs for Data Science .
Best Python IDEs for Data Science
Specially if you are a Data scientist or Data Analyst , You must need a high performance platform to run your code Right ? Here is complete list of such Best python IDEs . This is not only for Data science but you can use these IDEs in different python application 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 other programming /scripting language .
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 first priority . You can download Spyder from here . In case you have already installed Anaconada ,You need not to explicitly install Spyder IDE . Actually It come 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
ER- Environment .
For more details on Spyder IDE , Please visit Spyder’s official website .
Documentation and Coding together is easily possible with Jupyter Notebook .It is also an open source IDE . Specially for beginner who need more explanation Jupyter Notebook is best option . You can download and install Jupyter Notebook from here . Like Spyder , Anaconda distribution has 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 derive product of IPython . Actually the kernel part for Jupyter is IPython .you already know , Data Visualization is one of the most important step 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 document with images and other multimedia components iwith your source code . This feature of Jupyter IDE enhance the ability of explanation .This is why most of the Data Science bloggers use Jupyter Notebook for educational purposes . Jupyter IDE support so many programming language . I do not have any perfect count for this but I assume it is around 40 .
Jupyter also support 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 book , I will suggest you 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 monopoly . I will not go deep into this debate . Frankly speaking its a matter of personal choice to choose book or E-learning videos . If you also love video learning go for this Udemy course – ” Learning Path: Jupyter: Interactive Computing with Jupyter ”
An awesome product by Jet Brains . An Intelligent IDE “PyCharm” is not only capable of performing High performance Data Science related task 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 version . I mean suppose you are working two project , In which one support python 2.x and other require 3.x . Pycharm easily manage this situation for you . If we talk about its UI appearance , Its amazing and Customizable . Please have a look –
It has enrich 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 commerse Section .
You can download PyCharm Community Edition for free from here .
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 . )
This is a Microsoft Product . We usually call it VS code . This is a multi purpose IDE for multi programming languages .
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 solution for light weighted IDE . It has 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 .
Atom is a open source IDE . You can download ATOM IDE from here . It is interactive with MIT License . You can also contribute to make it better for other . Full code behind this IDE is available in GitHub repository of ATOM .It support so many interactive theme . This theme support gives an awesome UI to developers .
Like Spyder , It is also data science specific .Best part of this IDE is , it is Integrated with Basic Python Reference . This give a quick guide for beginner .Here is the Github repository of Rodeo . You can discuss your doubt related with Rodeo on its highly active Rodeo community .
Other’s Best Python IDEs –
Apart from these ,There are so many other Python IDEs which are also very useful .I have not listed them in top list . It does not mean they are not ordinary . Actually Every IDEs has different feature set . We have mention the above list for 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 for Python IDE
In the current time ,Software Development is quite easy because of open source communities and IDEs . So many prepared code we get on Stack Overflow and so many other code communities .Just because of 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 semicolon etc . These are able to provide Quick Fixes in code . Most of 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 ans Machine learning growing , we can see such IDEs –
1.Which can predict developers code intent . and provide the modular solution from cloud database of source code .You need not to surf websites explicitly to find the code .
2. Cloud base IDEs Like – Eclipse Chi . Frankly speaking cloud base IDEs are not a future Technology. You can say it latest . Most of IDEs has already launched their cloud version . Few IDEs Market players are preparing . You know software Industry is for fast movers .I think its new concept for you all . So lets discuss it in detail –
Cloud base IDEs or 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 need High configuration Rams with multi core CPUs . These are those issues which Data Scientist/ Developer face in day to day life at Hardware end .
Now If we talk about Software side , Usually we need different environments to run and test our applications . In both of the scenario , Cloud make our life easier . Even these days Auto scaling and Load balancing infrastructure are in fashion .Specially the cases where Big Data comes into play . Now we just have to write the code without thinking about infrastructure and environment . Some of them are free to use and some are having different cost and packages .The best part in the context of Costing is ,”Usually you have to pay only when you use them ” . Usually Its not come with fix cost package . Here I am mentioning few of them –
At the end , I will say Using Cloud base IDE is good solution for distributive computing . It is also very popular in different testing types like Load Testing and Stress Testing .You know what make me crazy about Cloud Base IDE is – “You can code from any where just using the browser” . If you are using cloud base IDE ,You need to carry your office laptop every where . Just you need a simple device where you can access the were browser.
Does UI matter for IDE –
Of course Yes ! Like every other software , IDE also need good UI . Specially The IDE which provide Plug and Play Architecture to code, need to be user friendly . Actually if UI is good then Platform become self explanatory .You need not too much tool training . As I have already discuss , Most of the above Python IDE support multiple theme 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 IDE prospective . You know as a coder , You may need cmd , code editor , variable explorer , Package explorer and servers window at once . IDE provide you all these things in 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 provide a full customization in different Prospective . Here I will share my personal experience , I like black back ground and green color text for code . This looks make me crazy for coding .
Best time to switch your IDE ?
Its a choice concern . There are so many different different opinions at this topic . So there is no such straight answer . Usually when you find your current IDE is –
- Not getting updated from longer .
- Having less compatible plugin in Market Place .
- If it is not Intelligent to provide you quick fixes for bugs .
- Less Community support
If you are feeling the same symptoms on your current IDE . Do not hang on with your IDE , It will waste you 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 satisfy your project scope .
Apart from these best Python IDEs , There are so many other which are also popular .For Example PyDev for Eclipse . Every IDEs has 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 –
So much research and development is going on 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 for your question “ Best Python IDEs for Data Science ” . If you think there is some thing 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 contents related to Data Science .
Data Science Learner Team