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Why Do We Use Django For Python?

Django can be defined as a high-level Python Framework that helps the users to come up with faster as well as cleaner development along with some realistic designing of websites. Developers today prefer to use Django over Ruby on Rails due to certain reasons. They are described below:

Python is extremely articulated as a language. You will find definite guidelines along with instructions, particularly about the way the codes are to be written or formatted. Besides, you will also find a clean structure in the codes, irrespective of whatever you do. In fact, the framework follows the good old principle that says, "Codes are more read than they are written."

Presence of 3rd party Libraries
When it comes to 3rd party libraries, using Python will not pose any problem for the developer. The libraries are powerful as well as mature enough to make coding seamless as well as fast as well as free of hiccups.

Helper Tools
It comes up with some helper tools that will make the life of the developers much easier. These tools help you maintain as well as deploy the codes.

In other words, one framework is used over another because of the advantages it is offering. From that point of view, Django scores over Ruby in a number of aspects.
In fact Django is the framework for those who are perfectionists, particularly those, who tend to work with strict deadlines. With a string of value added features like helpers, working ORM, a fantastic admin interface as well as a few more, Django is a developer's dream framework.

Object-Relational Mapper
It comes up with a default implementation mechanism that helps the developers when it comes to writing databases as Python class and query the same databases by using Python. This means that there is no need to write even a single SQL line manually.

Admin Interface
When it comes to taking care of a specific website or a client, it is imperative to manage all the content in a competent and flawless way. However, that does not mean that the codes and other texts need to be written in order to save time. It saves a lot of time as well as work. Django does exactly that.

It is a good tool and a well built one as well and this makes quite a difference at the end of the day.

Guarantee the longevity of the site
This is another cool factor that will speak for Django. The framework helps the sites to enjoy longevity. It means the site will not go down easily. That it ensures better life expectancy of the site is one of the major reasons why sites today are made up more with Django than anything else.

It is fast
Each and every bit of this framework is designed keeping in mind the speed factor. The template language of Django is much faster. The speed is so fast that even the caching compiled templates appears slower than when it comes to re-rendering them upon each and every request.

Django Scales
Whatever you do - right from launching and running personal websites that run on shared hosting to the small band websites and the huge databases of public information to the social networking sites, Django is ultimate when it comes to handling all the data successfully. Hence, the Django development framework features some astounding scalability that makes a difference. On top of everything, the budget involved is truly realistic as well as manageable.

Recently I have shared 7 Django Development Best Practices Each Web Developer Must Know. It most helpful for developers. However, Last but not the least, the admin of Django is amazing and is devoid of that customary writing of content-heavy sites that is associated with admin structuring.

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Why do Many Data Scientists love using Python over Ruby?

At present, big data is one of the hottest trends in enterprise application development. Most organizations nowadays need custom applications to collect, store, analyze and exchange huge volumes of data in a fast, efficient and secure way. The software developers have option to write these applications in a number of high-level programming languages including Ruby and Python. Both Ruby and Python are object-oriented, dynamic, and general-purpose programming languages.

In addition to supporting functional programming, Ruby allows developers to take advantage of features like blocks, mutable strings, and hashable/unhashable types. Likewise, Python also comes with several useful features including internal functions, modules, and rich set of data structures. Also, it handles namespaces in a more efficient way. But a number of surveys indicate that a large percentage of data scientists prefer Python to Ruby.

Why Data Scientists Prefer Python to Ruby?

Simple Syntax Rules
In addition to being easy is python to learn for a first time developer, Python also has simple, precise and efficient syntax. So it becomes easier for users to express concepts without writing longer lines of code. Also, Python, unlike Ruby, requires developers to follow guidelines related to layout, indentation and whitespace usage strictly. So it makes it easier for data scientists to build and manage a variety of custom applications without putting extra time and effort.

Faster than Other Programming Languages
Earlier, programming languages like Matlab, Octave and Stata were used widely by data scientists. These programming languages provide features for text filing, data visualizations and file parsing. But Python is much faster and more scalable than these conventional programming languages. Also, it helps data scientists to keep project overheads under control as an open source programming language.

Option to Include Graphics
Often data scientists are required to present the data analysis in a clear and easy-to-understand way. So these professionals explore ways to boost data visualization by using a variety of graphics. Python enables developers to include graphics in data analysis and reports through various data visualization libraries and application programming interfaces (APIs). At the same time, the data scientists can also use Python for connecting different units of a business, and make the data accessible throughout the organization.

Availability of Many Data Analysis Libraries
The users can further simplify data analysis using Python libraries like SciPy, NumPy, SciKit, Pandas and Matplotlib. SciPy is designed with features to simplify technical and scientific computing, while NumPy makes it easier for data scientists to integrate and use other Python libraries. Likewise, Panda facilitates data munging by providing features like support for automatic data alignment and option to handle missing data. Also, it helps users to work efficiently with data collected from various sources and indexed in a number of ways.

As a machine learning library, SciKit provides a variety of algorithm related to regression, classification and clustering. At the same time, Matplotlib is designed as a 2D plotting library with interactive features. Its features enable users to publish quality figures in different formats and across multiple platforms. The data scientists can further integrate these Python libraries seamlessly, and use them together to collect, manage and analyze huge volumes of data more efficiently and quickly. These data analysis libraries make many data scientists to prefer Python over Ruby.

Large and Active Community
The members of the large community also contribute immensely towards making Python the language of choice for data scientists. The thriving Python community includes many data scientists and data analysts. Such members have been continuously developing new data analysis library for the programming language. At present, the data scientists can take advantage of several data science or data analytics libraries including NumPy, SciPy, Statsmodels, Pandas and SciKit learn.

The data scientists still have option to use Ruby for specific purposes. But the features provided by Ruby enable developers to build a variety of modern websites and web application rapidly. On the other hand, Python provides specific features to effectuate collection, storage, analysis and exchange of large chunks of structured and unstructured data more efficiently and securely.